# Events Feed

Start Date: 02 Mar 2020;
Start Time: 10:30AM -
Title: Democratizing Error-Efficient Computing

Bio:

Radha is a doctoral candidate in Computer Science at the University of Illinois at Urbana-Champaign. Her research interests lie in the area of Computer Architecture and Systems. Radha’s dissertation work aims to build efficient computing systems that redefine “correctness” as producing results that are good enough to ensure an acceptable user experience. Radha’s research work has been nominated to the IBM Pat Goldberg Memorial Best Paper Award for 2019. She was among 20 people invited to participate in an exploratory workshop on error-efficient computing systems initiated by the Swiss National Science Foundation and is one of 200 young researchers in Math and Computer Science worldwide to be selected for the prestigious 2018 Heidelberg Laureate Forum. Radha was selected for the Rising Stars in EECS and the Rising Stars in Computer Architecture (RISC-A) workshops for the year 2019. Before joining the University of Illinois, Radha was a CPU/Silicon validation engineer at Intel where her work won a divisional award for key contributions in validating new industry standard CPU features. Prior to that, she worked briefly at Qualcomm on architectural verification of the Snapdragon processor.

Speaker:
Abstract: We live in a world were errors in computation are becoming ubiquitous and come from a wide variety of sources -- from unintentional soft errors in shrinking transistors to deliberate errors introduced by approximation or malicious attacks. Guaranteeing perfect functionality across a wide range of future systems will be prohibitively expensive. Error-Efficient computing offers a promising solution by allowing the system to make controlled errors. Such systems can be considered as being error-efficient: they only prevent as many errors as they need to for an acceptable user experience. Allowing the system to make errors can lead to significant resource (time, energy, bandwidth, etc.) savings. Error-efficient computing can transform the way we design hardware and software to exploit new sources of compute efficiency; however, excessive programmer burden and a lack of principled design methodologies have thwarted its adoption. My research addresses these limitations through foundational contributions that enable the adoption of error-efficiency as a first-class design principle by a variety of users and application domains. In this talk, I will show how my work (1) enables an understanding of how errors affect program execution by providing a suite of automated and scalable error analysis tools, (2) demonstrates how such an understanding can be exploited to build customized error-efficiency solutions targeted to low-cost hardware resiliency and approximate computing and (3) develops methodologies for principled integration of error-efficiency into the software design workflow. Finally, I will discuss future research avenues in error-efficient computing with multidisciplinary implications in core disciplines (programming languages, software engineering, hardware design, systems) and emerging application areas (AI, VR, robotics, edge computing).
Location: CoRE A 301
Committee:
Start Date: 03 Mar 2020;
Start Time: 11:30AM -
Title: ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

Bio:
Speaker:
Abstract: A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied (weighted) averages of word vectors so as to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data. The experiments show that our method outperforms several technically more powerful approaches, especially under challenging low-resource circumstances.
Location: CBIM-22
Committee:

Prof. Gerard de Melo (advisor), Prof. Karl Stratos, Prof. Yongfeng Zhang & Prof. Dong Deng (external committee member)

Start Date: 04 Mar 2020;
Start Time: 11:00AM - 12:00PM
Title: Stochastic local search and the Lovasz Local Lemma

Bio:
Speaker:
Abstract: Numerous problems in computer science and combinatorics can be formulated as searching for objects lacking certain bad properties, or "flaws". For example, constraint satisfaction problems like satisfiability can be seen as searching for objects (truth assignments) that are flawless in the sense that they do not violate any constraint. A large class of practical algorithms for finding flawless objects employ "stochastic local search"; such algorithms start with a flawed object and try to make it flawless via small randomized changes that in each step focus on eradicating a specific flaw (while potentially introducing others). In this talk, I will present a general framework for the design and analysis of stochastic local search algorithms, inspired by and extending the Lovasz Local Lemma. I will also talk about its applications in solving "pseudo-random" instances of constraint satisfaction problems.
Location:
Committee:
Start Date: 04 Mar 2020;
Start Time: 03:30PM -
Title: How to Make, Sense, and Make Sense of Contact in Robotic Manipulation

Bio:

Matei Ciocarlie is an Associate Professor of Mechanical Engineering at Columbia University. His current work focuses on robot motor control, mechanism and sensor design, planning and learning, all aiming to demonstrate complex motor skills such as dexterous manipulation. Matei completed his Ph.D. at Columbia University in New York; before joining the faculty at Columbia, he was a Research Scientist and Group Manager at Willow Garage, Inc., a privately funded Silicon Valley robotics research lab, and then a Senior Research Scientist at Google, Inc. In recognition of his work, Matei has been awarded the Early Career Award by the IEEE Robotics and Automation Society, a Young Investigator Award by the Office of Naval Research, a CAREER Award by the National Science Foundation, and a Sloan Research Fellowship by the Alfred P. Sloan Foundation.

Speaker:
Abstract: Dexterous manipulation is still one of the key open problems for many new robotic applications, owing in great measure to the difficulty of dealing with transient contact. From an analytical standpoint, intermittent frictional contact (the essence of manipulation) is difficult to model, as it gives rise to non-convex problems with no known efficient solvers. Contact is also difficult to sense, particularly with sensors integrated in a mechanical package that must also be compact, highly articulated and appropriately actuated (i.e. a robot hand). Articulation and actuation present their own challenges: a dexterous hand comes with a high-dimensional posture space, difficult to design, actuate, and control. In this talk, I will present our work trying to address these challenges: analytical models of grasp stability (with realistic energy dissipation constraints), design and use of sensors (tactile and proprioceptive) for manipulation, and hand posture subspaces (for design optimization and teleoperation). These are stepping stones towards achieving versatile robotic manipulation, needed by applications as diverse as logistics, manufacturing, disaster response and space robots.
Location: Easton Hub Auditorium (Fiber Optics Building, Busch campus)
Committee:
Start Date: 05 Mar 2020;
Start Time: 10:30AM -
Title: Finding Semantic Bugs in Kernels: the Symbolic Way and the Fuzzy Way

Bio:

Meng Xu is a Ph.D. candidate in the school of computer science at Georgia Tech, advised by Taesoo Kim. His research interests are broadly in the areas of system and software security, with a thesis research on finding semantic bugs via symbolic execution and fuzz testing, and rich experience in achieving security with software diversity and N-version programming. His work has uncovered over 100 bugs in foundational software like OS kernels and browsers, appears in top-tier security and system venues, and receives a distinguished paper award at USENIX Security 2018. He also served on the Program Committee of CCS 2018 as well as the Student PC of Oakland 2018 and EuroSys 2018.

Speaker:
Abstract: The scale and pervasiveness of modern software pose challenges for security researchers: a bug is more devastating than ever, and the growing software complexity keeps exacerbating the situation with more bug species --- expanding the arms race between security practitioners and attackers beyond memory errors. As a consequence, we need a new generation of bug hunting tools that not only scale well with increasingly larger codebases but also catch up with the growing variety of bugs. In this talk, I will present two complementary bug hunting frameworks that meet the scalability and agility requirements: focused symbolic checking and multi-dimensional fuzz testing, and showcase their effectiveness in a challenging arena: OS kernels. While symbolic execution can never scale up to the whole kernel, complete checking may nevertheless be possible in carefully constructed program slices. I will demonstrate how symbolic bug models can help build such slices and enable a jumpstart of symbolic execution from the middle of a program. On the other hand, fuzz testing turns bug finding into a probabilistic search, but current practices restrict themselves to one dimension only (sequential executions). I will illustrate how to explore the concurrency dimension and extend the bug scope beyond memory errors to a broad spectrum of semantic bugs. Finally, I will give a sense of the extensibility of both frameworks with planned bug checker integrations, as well as a vision to have them incorporated into the software development cycle right from day 1.
Location: CoRE A 301
Committee:
Start Date: 05 Mar 2020;
Start Time: 01:00PM - 04:00PM
Title: The Problem of Many: Efficient Multi-arm, Multi-object Task and Motion Planning with Optimality Guarantees

Bio:
Speaker:
Abstract: This thesis deals with task and motion planning challenges, specifically those involving manipulating multiple objects using multiple robot manipulators. The contributions range from a new foundational understanding of the problem and the conditions for achieving asymptotic optimality to devising application-oriented and efficient planning algorithms as well as experimenting on real systems. On the application side, the focus is on overcoming scalability challenges in motion planning for multiple robots, culminating in the dRRT* algorithm. Composing such motions can enable robotic systems to solve complex tasks, motivating the design of task planning algorithms for multiple arms and multiple objects using a rich set of actions, such as picks, placements and handoffs. Extensions into collaborative robotics are also considered.
Location: 1 Spring Street, Room 403
Committee:

Kostas Bekris, Jingjin Yu, Abdeslam Boularias, Danica Kragic (Royal Institute of Technology (KTH), Stockholm, Sweden)

Start Date: 09 Mar 2020;
Start Time: 10:30AM -
Title: Price of Active Security in Multiparty Computation

Bio:

Muthu Venkitasubramaniam is an Associate Professor at the University of Rochester. He received his B.Tech degree in computer science from the Indian Institute of Technology, Madras in 2004 and then his PhD from Cornell in 2011. He spent a year at the Courant Institute as a postdoc researcher under the CIF, and then joined the faculty at Rochester where he is currently an associate professor.  He is a recipient of the GFRA and the ICDE Influential Paper Award.

Speaker:
Abstract: Secure multiparty computation (MPC) allows a group of mutually distrusting parties to collaborate and compute a function jointly over their individual data while guaranteeing maximal privacy. The last decade has seen pivotal developments in cryptography that have enabled dozens of built systems demonstrating novel privacy use cases for this technique. The strongest type of MPC protocol guarantees security against malicious participants who may arbitrarily deviate from the protocol. This is often called “active” security. Designing MPC with active security involves two steps: (1) building a protocol with (weak) passive security, i.e. tolerating only honest-but-curious participants, and then, (2) amplifying to obtain active security. While the price of achieving active security has been well understood in theory, all practical approaches incur a significant communication and computation overhead (20-100x) over the best practical MPC protocols with passive security. In this talk, I will survey my works focused on theoretical and practical advancements towards designing MPC with active security. In particular, I will discuss a recent work where I introduce a new paradigm that allows implementing MPC with active security whose communication is roughly twice the cost of the best protocol with passive security. I will further show how this approach can lead to constructions with essentially no communication overhead by relating the efficiency to certain “leakage-resilience” properties of error-correcting codes. As a culmination in this line of work, I will showcase a recent application for generation of RSA moduli in an MPC that is slated to be used by the Ethereum foundation to bootstrap their next consensus protocol. Using our techniques, we have designed and executed a protocol for n=10,000 participants (the largest known MPC execution) and achieves active security even when all-but-one of the parties are malicious.
Location: CoRE A 301
Committee:
Start Date: 11 Mar 2020;
Start Time: 11:00AM - 12:00PM
Title: Exploration with Limited Memory: Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits

Bio:
Speaker:
Abstract: Consider the following abstract coin tossing problem: Given a set of n coins with unknown biases, find the most biased coin using a minimal number of coin tosses. This is a common abstraction of various exploration problems in theoretical computer science and machine learning and has been studied extensively over the years. In particular, algorithms with optimal sample complexity (number of coin tosses) have been known for this problem for quite some time. Motivated by applications to processing massive datasets, we study the space complexity of solving this problem with optimal number of coin tosses in the streaming model. We will present an algorithm for this problem that simultaneously achieve asymptotically optimal sample complexity and space complexity which turns out to be surprisingly small. Furthermore, we extend our results to the problem of finding the k most biased coins as well as other exploration problems such as finding top-k elements using noisy comparisons or finding an ε-best arm in stochastic multi-armed bandits, and obtain efficient streaming algorithms for these problems. Join work with Sepehr Assadi.
Location: CoRE 431
Committee:
Start Date: 12 Mar 2020;
Start Time: 10:30AM -
Title: (Live-streamed Event) Smarter Hardware Prediction Mechanisms

Bio:

Akanksha Jain is a Research Associate at the University of Texas

at Austin.  She received her PhD in Computer Science from The
University of Texas in December 2016.  In 2009, she received the
B.Tech and M. Tech degrees in Computer Science and Engineering
from the Indian Institute of Technology Madras.  Her research
interests are in computer architecture, with a particular focus
on the memory system and on using machine learning techniques
to improve the design of memory system optimizations.

Speaker:
Abstract: The performance of many applications is limited by large memory access latencies. In this talk, I will present significant advances to two foundational techniques---caching and prefetching---that aim to mitigate this bottleneck. A common theme is the use of idealized design points, which at first glance seem impossible or infeasible, as a key building block of highly effective and practical solutions. I will start by presenting a cache replacement policy that leverages Belady's optimal but clairvoyant algorithm in the design of a practical solution. This policy won the second Cache Replacement Championship, and it inspired us to create new optimal algorithms for complex scenarios. I will then briefly present a series of work in irregular data prefetching that makes a prohibitively expensive prefetching technique practical via new data representations. Finally, I will discuss the role that machine learning can play in improving hardware systems, and I will present a novel approach that uses machine learning as a white-box tool to build practical hardware predictors. I will conclude by discussing my vision for intelligent, high-performant memory systems for future hardware systems.
Location: Via Webex
Committee:
Start Date: 18 Mar 2020;
Start Time: 03:00PM - 04:30PM
Title: Improvements in cross-modality cardiac segmentation for unsupervised domain adaptation frameworks

Bio:
Speaker:
Abstract: In medical image computing, the problem of heterogeneous domain shift is quite common and severe, causing many deep convolutional networks to under-perform on various imaging modalities. Retraining the network is difficult since annotating the new domain data is prohibitively expensive, specifically in medical areas that require expertise. While recent works show approaches to tackle this problem using unsupervised domain adaptation, segmentation modules in such methods can be improved vastly. Our implementation provides two segmentation improvements on the current state-of-the-art framework, Synergistic Image and Feature Adaptation(SIFA). We revisit cascade multi-rate atrous convolutions and also borrow the idea of atrous spatial pyramid pooling while using convolutional features as well as image features for multi-scale object segmentation. We have validated the effectiveness of the improvements on the framework using the challenging application of cross-modality segmentation of cardiac structures. To demonstrate the robustness of the module, extensive experiments have been performed on both Long-Axis(MMWHS) & Short-Axis(MSCMR) cross-modal cardiac segmentation tasks.
Location: CoRE 301
Committee:

Prof. Konstantinos Michmizos

Prof. Karl Stratos

Start Date: 23 Mar 2020;
Start Time: 01:00PM - 03:00PM
Title: Programming and Managing Data-Driven Applications Between the Edge and the Cloud

Bio:
Speaker:
Abstract: Due to the proliferation of the Internet of Things (IoT), the number of devices connected to the Internet is growing. These devices are generating unprecedented amounts of data at the edge of the infrastructure. Although the generated data provides great potential, identifying and processing relevant data points hidden in streams of unimportant data, and doing this in near real time, remains a significant challenge. The prevalent model of moving data from the edge to the cloud of the network is quickly becoming unsustainable. Resulting in an impact on latency, network congestion, storage cost, and privacy, limiting the potential impact of IoT. To address these challenges, this dissertation presents an IoT Edge Framework, called R-Pulsar, that extends cloud capabilities to local devices and provides a programming model for deciding what, when, where and how data get collected and processed. This thesis makes the following contributions: (1) A content- and location-based programming abstraction for specifying what data gets collected and where the data gets analyzed. (2) A rule-based programming abstraction for specifying when to trigger data-processing tasks based on data observations. (3) A programming abstraction for specifying how to split a given dataflow and place operators across edge and cloud resources. (4) An operator placement strategy that aims to minimize an aggregate cost which covers the end-to-end latency (time for an event to traverse the entire dataflow), the data transfer rate (amount of data transferred between the edge and the cloud) and the messaging cost (number of messages transferred between edge and the cloud). (5) Performance optimizations on the data-processing pipeline in order to achieve real-time performance on constrained devices.
Location: Remote
Committee:

Ulrich Kremer

Srinivas Narayana Ganapathy

External member: Otto J. Anshus

Start Date: 23 Mar 2020;
Start Time: 01:00PM - 03:00PM
Title: DATA-DRIVEN DEVELOPMENT OF PERSONALITY PREDICTIVE LEXICA FROM SOCIAL MEDIA

Bio:
Speaker:
Abstract: Automatic personality prediction is getting more popular because it is convenient and reliable. Lexicon-based analysis has been successful in the fields of sentiment analysis and emotion. Many studies have used linear models for personality prediction, which suggests that we can also use lexical-based analysis for personality prediction. In the current study, we developed weighted word lexicons (words and scores) on each dimension of MBTI personality. The lex- icons are built based on eight MBTI datasets, different features (unigram, 1-2 grams, 1-2-3 grams) and weighitings (TF, TF-IDF, TF-logIDF), and different supervised learning models. Then we ran correlation analysis between our MBTI lexicons and other existing lexicons, such as Big-5, emotion, sentiment, age, gender. The correlation analysis shows interesting and rea- sonable correlation between different personality dimensions and other psychological traits, and it also provides evidence for the robustness of our lexicons.
Location: Remote
Committee:

Gerard De Melo (Advisor), Manish Singh, Yongfeng Zhang

Start Date: 25 Mar 2020;
Start Time: 12:30PM - 01:30PM
Title: Combinatorial problems in algorithms and complexity theory

Bio:
Speaker:
Abstract: Theoretical Computer Science has had connections to several areas of mathematics, and one of the more prominent of these connections is to combinatorics. Indeed, many problems in this subject are often very combinatorial in nature. These problems have either used existing techniques from combinatorics or have given rise to new combinatorial techniques. This dissertation is a collection of the study of some such problems. 1. We recover a result by Abbe, Shpilka and Wigderson which states that a Reed-Muller code of rate 1 - Theta((\log^r n)/n) can be recovered from o((log^((r-1)/2))/n) randomly chosen errors in a stronger way. Namely, we show that the set of corrupted locations in the message can be recovered just from the syndrome of the message. Among the techniques are the study of tensor decomposition over finite fields and an algorithm to find the roots of a space of low degree polynomials. 2. We show that it is NP-hard to properly 2-color a k-uniform (k - O(sqrt{k}))-rainbow colorable hypergraph. In particular, we show that it is NP-hard to properly 2-color a 4-uniform $3$-rainbow colorable hypergraph. We further extend this using a notion of almost rainbow colorability. We show that given a k-uniform hypergraph where there is a $(k - \sqrt{ck})$-coloring of the vertices such that every edge gets (k - 3sqrt{ck})-colors, it is NP-hard to properly c-color it. Among the techniques are topological methods to lower bound the chromatic number of certain hypergrpahs, and a theorem of Sarkaria on the chromatic number of the generalized Kneser hypergraph. 3. We show that the discrepancy of a regular hypergraph can be bounded in terms of its spectral information. Let H \subset 2^{[n]} be a t-regular hypergraph where |H| >= n, and M be the |H| x n incidence matrix. Define $lambda : = max_{v \perp 1}||Mv||/||v||$. We show that the discrepancy of H is at most O(sqrt{t} + lambda). In particular, this shows that for every t, the discrepancy of a random t-regular hypergraph on m > = n hyperedges has discrepancy O(sqrt{t}) with high probability as n grows. This bound also comes with an efficient algorithm that takes $\mathcal{H}$ as input and outputs a coloring that has the guaranteed discrepancy. 4. We show that every q-ary error correcting code of distance 1 - q^{-1} - epsilon^2 can be punctured to rate tilde{Omega}((epsilon)/(\log q)) so that it is (O(q),O(1))-zero-error list-decodable. In particular, this shows that there are Reed-Solomon codes that are zero-error list-recoverable beyond the Johnson radius. This immediately improves the degree bound for unbalanced expanders obtained from randomly punctured Reed-Solomon codes.
Location: Remote via Webex
Committee:

Swastik Kopparty (chair), Jeff Kahn, Shubhangi Saraf, Raghu Meka (external)

Start Date: 25 Mar 2020;
Start Time: 02:00PM - 04:00PM
Title: Scene Graph Parsing and Its Application in CV/NLP Cross-Modal Reasoning

Bio:
Speaker:
Abstract: Scene graph parsing aims at understanding an image as a graph where vertices are visual objects (potentially with attributes) and edges are visual relationships among objects. This task is commonly seen as an extension to the object detection task where objects are detected individually, while the former requires recognizing relationships between object pairs. Therefore, scene graphs are usually seen as a better semantic representation of images for visual reasoning. In my presentation I will introduce two salient issues that naturally occur in scene graphs: Ambiguity in the language dimension and ambiguity in the visual dimension. The first happens when the vocabulary of objects and relationships are significantly large, and the second happens when multiple vertices or edges in a scene graph are from the same category and confuse the model to recognize the correct relational pairing. At last, with an accurately parsed scene graph, I will talk about how to use it as an extra channel of features for down-stream visual reasoning tasks such as Video Story Question Answering. Concretely, how do we obtain better interpretability by using scene graphs? How much could scene graph help visual reasoning if it is not perfectly constructed? I will dive deep into these details in the presentation.
Location: Remote via Webex
Committee:

Prof. Yongfeng Zhang

Prof. Gerard de Melo

Dr. Ang Li (External member)

Start Date: 26 Mar 2020;
Start Time: 10:30AM -
Title: Breaking and Building End-to-End Encrypted Systems

Bio:

Paul Grubbs is a PhD candidate in Computer Science at Cornell University. In his research in security, cryptography, and systems, he designs and analyzes systems that use encryption to protect data. To do this, he uses theoretical foundations from cryptography and other fields, empirical methods, and practical knowledge of real systems. He is the recipient of a 2017 NSF Graduate Research Fellowship.

Speaker:
Abstract: Today’s computer systems and their owners fail to protect data. Exacerbating this are new threats stemming from the rise of cloud computing. The consequences are dire: sensitive information like financial statements, medical records, and private messages are disclosed to malicious parties. In my research at the intersection of security, cryptography, and systems, I work to change this by breaking and building efficient end-to-end (E2E) encrypted systems, which protect data by keeping it encrypted throughout processing and storage. In this talk, I’ll explain some of the flaws I’ve found in existing E2E-encrypted systems deployed to billions of users, how the flaws have led me to a new methodology for building these systems that’s rooted in co-design of cryptography and systems, and some of the new E2E-encrypted systems I’m building.
Location: Live-streamed via Webex
Committee:
Start Date: 27 Mar 2020;
Start Time: 10:00AM - 12:00PM
Title: Human Mobility Modeling Based on Heterogeneous Urban Sensing Systems

Bio:
Speaker:
Abstract: Recently, with an increasing number of people living in cities, it introduces new challenges in human mobility such as traffic congestion and energy consumption, which are caused by a dense human population distribution, unbalanced infrastructure deployment, or insufficient understanding of travel demand. Thus, it is essential to improve the mobility of urban residents on a daily basis, which can be achieved by accurately modeling human mobility with ubiquitous urban sensing data from heterogeneous urban sensing systems, e.g., on-board GPS systems including taxis, buses, personal vehicles, and portable device systems such as cellphones. Existing studies modeling human mobility are mostly built upon single systems. However, people in cities take multiple transportation modalities on daily basis, where a single sensing system limits a comprehensive understanding and modeling of human mobility. In the dissertation, we aim to model human mobility at metropolitan scale, by utilizing spatio-temporal data of heterogeneous sensing systems already collected for billing or management purposes. We design, implement and evaluate a urban sensing system named urbanSense with three modules for human mobility modeling (e.g., travel distance, travel time, travel speed): (i) a sensing module to collect and preprocess human mobility sensing data from 8 urban sensing systems crossing 3 domains (i.e., transportation, communication, and payment); (ii) a measurement module where we present a measurement work named SysRep to measure the data bias of urban sensing systems for human mobility modeling. In SysRep, we quantify the data bias of urban sensing systems as the representativeness of sensing systems. We analyze potential reasons for representativeness and found representativeness is highly correlated with contextual factors such as population, mobility, and Point of Interests. We further design a correction model to improve representativeness of sensing systems. The evaluation results show the proposed correction model can improve the representativeness of singe systems by 45%. (iii) a prediction module to model human mobility from heterogeneous urban sensing systems. In particular, we present two works: a work named MultiCell for realtime population modeling and the other work named MAC for travel time prediction. In MultiCell, we design two techniques to model real-time population from multiple cellular networks: a spatial alignment technique to align different spatial partitions into a uniform spatial partition; a co-training technique to learn the relation between active cellphone users of different networks and population distribution simultaneously. MultiCell is implemented with Call Detail Records (CDR) of three major networks in China in the same city covering 100% cellphone users. The evaluation results prove the effectiveness of MultiCell by reducing the modeling error by 27% compared with the start-of-the-art models. In MAC, we decompose travel time of multiple transportation systems (i.e., subway, taxi, bus, and personal vehicle) into fine-grained travel time based on different travel stages (e.g., walking, riding, waiting time). Moreover, we design a time-series model based on Long Short-Term Memory (LSTM) architect to predict the travel delay under the impact of different anomalies. We implement and evaluate MAC with data collected from 37 thousand vehicles and 5 million smart cards. The results show MAC reduces the prediction error by 31% compared with the state-of-the-art methods. Finally, we discuss some lessons learned and potential applications of our framework.
Location: Remote - Webex
Committee:

Desheng Zhang (Rutgers CS, Committee Chair), Jie Gao (Rutgers CS), Yongfeng Zhang (Rutgers CS), Ruilin Liu (Facebook, external member)

Start Date: 30 Mar 2020;
Start Time: 10:30AM -
Title: Foundations of Lattice-Based Cryptography

Bio:

Noah Stephens-Davidowitz is the Microsoft Research Fellow at the Simons Institute in Berkeley. He has also been a postdoctoral researcher at MIT, Princeton, and the Institute for Advanced Study. He received his PhD from NYU,  where his dissertation won the Dean’s Outstanding Dissertation Award in the sciences.

Much of Noah’s research uses the tools of theoretical computer science to answer fundamental questions about the security of widely deployed real-world cryptography, particularly post-quantum lattice-based cryptography. He is also interested more broadly in theoretical computer science, cryptography, and geometry.

Speaker:
Abstract: There has been a recent revolution in cryptography due to the introduction of lattice-based constructions. These are cryptographic schemes whose security relies on the presumed hardness of certain computational problems over ubiquitous (and beautiful) geometric objects called lattices. Their many applications (e.g., fully homomorphic encryption) and security against adversaries with quantum computers has created some urgency to deploy lattice-based schemes widely over the next few years. For example, the National Institute of Standards and Technology is in the process of standardizing lattice-based cryptography, and Google has already implemented such a scheme in its Canary browser. The security of the proposed schemes relies crucially on the assumption that our current best algorithms (both classical and quantum) for the relevant computational lattice problems cannot be improved by even a relatively small amount. I will discuss the state of the art in the study of this assumption. In particular, I will describe the fastest known algorithms for these problems (and potential directions to improve them) as well as a recent series of hardness results that use the tools of fine-grained complexity to provide strong evidence for the security of lattice-based cryptography.
Location: Live-streamed via Webex
Committee:
Start Date: 03 Apr 2020;
Start Time: 03:00PM - 04:00PM
Title: Tensegrity Robots: Control via Model-based RL and Modeling via a Differentiable Engine

Bio:
Speaker:
Abstract: Tensegrity robots are hybrid soft-rigid systems that exhibit adaptability and robustness. For instance, NASA has developed a promising exploration rover in the form of a cable-driven tensegrity named SUPERball, which has 6 rods and 24 cable actuators. At the same time, these platforms introduce significant challenges in terms of locomotion as they involve high-dimensional control, oscillatory behavior and complex, difficult to model dynamics. The first component of this work focuses on reinforcement learning (RL) based on the framework of Guided Policy Search (GPS). A key contribution is symmetry reduction during controller design, which moderates data requirements. Adaptations on the GPS frameworks have resulted in sample-efficient RL that allows any-axis rolling and adaptation to different terrain types. Simulated experiments demonstrate smooth, efficient trajectories. Transferring the results to the real system, however, highlights the reality gap of the available simulation tools. This motivated the second component of the work, which corresponds to a differentiable physics engine for cable-driven systems. The objective is to develop an engine that builds on top of first principles but mitigates the reality gap by learning directly from data. Our current evaluation shows the promise of developing a solution that does not have significant data requirements and paves the way for a differentiable engine that can accurately model a physical platform.
Location: Remote via Webex
Committee:

Prof. Kostas Bekris (Chair), Prof. Mridul Aanjaneya, Prof. Sungjin Ahn, Prof. Sudarsun Kannan

Start Date: 14 Apr 2020;
Start Time: 02:00PM - 03:00PM
Title: Integrating User Representation Learning and Multiscale Modeling: A Hierarchical Framework for Information Diffusion

Bio:
Speaker:
Abstract: Multiscale modeling has yielded immense success on various machine learning tasks. However, it has not been properly explored for the prominent task of information diffusion, which aims to understand how information propagates along users in online social networks. For a specific user, whether and when to adopt a piece of information propagated from another user is affected by complex interactions, and thus, is very challenging to model. Current state-of-the-art techniques invoke deep neural model with vector representations of users. In this study, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling. The proposed framework can be layered on top of all information diffusion techniques that leverage user representations, so as to boost the predictive power and learning efficiency of the original technique. Extensive experiments on three real-world datasets showcase the superiority of our method.
Location: Remote via Webex
Committee:

Prof. Mubbasir Kapadia, Prof. Gerard De Melo, Prof. Yongfeng Zhang, and Prof. Shiqing Ma

Start Date: 17 Apr 2020;
Start Time: 02:30PM - 04:00PM
Title: Towards understanding the approximation of Boolean functions by nonclassical polynomials

Bio:
Speaker:
Abstract: The representation and approximation of Boolean functions by polynomials is an essential tool in theoretical computer science, having numerous applications in various areas including, but not limited to, circuit complexity, communication complexity, pseudorandomness, quantum computation, learning theory, algorithm design, and explicit combinatorial constructions. The refutation of the inverse conjecture of the Gowers norm by Green and Tao (2007), Lovett et al. (2008), and Tao and Ziegler (2011), and subsequent work of Bhowmick and Lovett (2014), all suggest that a barrier to the resolution of some of the outstanding open problems in the area is the existence of the class of nonclassical polynomials and their ability to represent and approximate Boolean functions. Motivated by these works, in this dissertation, we investigate the ability of nonclassical polynomials to approximate Boolean functions under both new and previously studied notions of approximation: • We introduce and study an agreement-based notion of approximation by polynomials over the ring of integers modulo powers of two. This notion serves as a proxy for understanding how well nonclassical polynomials can approximate Boolean functions in the agreement-sense. We prove several new results that shed light on this notion of approximation, answering questions left open in the work of Bhowmick and Lovett (2014). • We propose a new notion of point-wise approximation by nonclassical polynomials. Using a result of Green et al. (1992), which is an extension of the classic work of Beigel and Tarui (1991), we observe that under this notion, Boolean functions computable by ACC0 circuits (constant-depth circuits with AND, OR, NOT, and MOD gates) are amenable to approximation by low-degree nonclassical polynomials. Motivated by this new observation, we then explore the approximability of the majority and the delta functions by nonclassical polynomials in this point-wise sense, in the hope of resolving the long-standing open problem of showing that majority is not computable by ACC0 circuits. We also compare the two notions of approximation introduced by us to other standard notions, especially correlation-based approximation, listing several open problems and future directions of research.
Location: Remote via Webex
Committee:

Swastik Kopparty (Chair), Eric Allender, Shubhangi Saraf, Hamed Hatami (external member - McGill University)

Start Date: 24 Apr 2020;
Start Time: 11:00AM - 12:30PM
Title: Explanation-Driven Learning-Based Models for Visual Recognition Tasks

Bio:
Speaker:
Abstract: Safety-critical applications (e.g., autonomous vehicles, human-machine teaming, and automated medical diagnosis) often require the use of computational agents that are capable of understanding and reasoning about the high-level content of real-world scene images in order to make rational and grounded decisions that can be trusted by humans. Many of these agents rely on machine learning-based models which are increasingly being treated as black-boxes. One way to increase model interpretability is to make explainability a core principle of the model, e.g., by forcing deep neural networks to explicitly learn grounded and interpretable features. This talk will consist of three parts. First, I will provide a high-level overview of the field of explainable/interpretable machine learning and review some existing approaches for interpreting neural networks used for computer vision tasks. Second, I will introduce several novel approaches for making convolutional neural networks (CNNs) more interpretable by utilizing explainability as a guiding principle when designing the model architecture. Third, I will discuss some possible future research directions involving explanation-driven machine learning.
Location: Remote via Webex
Committee:

Prof. Dimitris Metaxas (Chair)

Prof. Konstantinos Michmizos

Prof. George Moustakides

Prof. Fuxin Li (External Member, Oregon State University)

Start Date: 24 Apr 2020;
Start Time: 11:00AM - 12:00PM
Title: Stability of Transferred Learned Models for Planning and Reinforcement Learning

Bio:
Speaker:
Abstract: Dynamics models are useful for task planning, but are often not available when systems become sufficiently complex. In such situations, it is common instead to learn an approximate model, preferably by fine-tuning an existing model with data from the target system. We explore the behavior of dynamics models transferred from one system to another. In particular, we find that 1) models of non-chaotic markov systems may become chaotic when transferred to different systems, 2) the lyapunov exponent (a measure of how chaotic a system is) of a learned model converges to that of the true system in the high-data limit, 3) and the lyapunov exponent of a transferred model that is fine-tuned on a target dataset depends on the size of the target dataset, not the size of the source dataset. Thus, fine-tuning a model with a small amount of data may make it chaotic even if it was not chaotic before. Together, these imply that fine-tuning a model on a target dataset may actually worsen long-horizon predictions (which depend primarily on how chaotic the system is) even as it improves single-step predictions (which depend only on the single-step error). To remedy this problem, we propose two training methods that reduce or eliminate this problem, and evaluate their effectiveness against traditional approaches on a robotic grasping task. We find that both methods significantly outperform their counterparts that only account for single-step errors.
Location: Remote via Webex
Committee:

Prof. Abdeslam Boularias, Prof. Kostas Bekris, Prof. Jingjin Yu, Prof. Jie Gao

Start Date: 24 Apr 2020;
Start Time: 11:00AM - 12:30PM
Title: Addressing Fault Tolerance for Staging Based Scientific Workflows

Bio:
Speaker:
Abstract: Scientific in-situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, running in-situ scientific workflows on extreme-scale computing systems presents fault tolerance challenges which significantly affect the correctness and performance of workflows. This presentation addresses challenges related to data resilience and fault tolerance for in-situ scientific workflows, and makes the following contributions. Firstly, I present CoREC, a scalable resilient in-memory data staging runtime to support the data resilience for large-scale in-situ workflows. Then, I present a staging based error detection approach which leverages idle computation resource in data staging to enable timely detection and recovery from silent data corruption. Finally, I present a loose coupled checkpoint/restart with data logging framework to provide a scalable and flexible fault tolerance scheme for in-situ workflows while still maintaining the data consistency and low resiliency cost.
Location: Remote via Webex
Committee:

### Prof. George Bosilca (External Member, University of Tennessee)

Start Date: 07 May 2020;
Start Time: 12:30PM - 01:30PM
Title: Learning Robust and Unbiased Conditional Generative Adversarial Networks

Bio:
Speaker:
Abstract: Conditional Generative Adversarial Networks (CGANs) are widely used generative models and have shown exceptional generation performance over the past few years. Class conditional information can be incorporated by either (1) conditioning the discriminator directly on labels, or by (2) incorporating an auxiliary classification loss. We notice two limitations in current conditional GAN models: (1) they require large numbers of annotations, and (2) the widely adopted Auxiliary Classifier GANs (ACGANs) learns a biased distribution. To address the first problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. In the light of Bayesian uncertainty estimation and noise-tolerant adversarial training, PC-GAN can estimate attribute rating efficiently and demonstrate robust performance in noise resistance. Through extensive experiments, we show both qualitatively and quantitatively that PC-GAN performs comparably with fully-supervised methods and outperforms unsupervised baselines. Previous attempts have been made to remedy the second problem, such as Twin Auxiliary Classifier GAN (TAC-GAN) which introduces a twin classifier to the min-max game. However, it has been reported that using a twin auxiliary classifier may cause instability in training. To this end, we propose an unbiased Auxiliary GAN (AC-GAN-MINE) that utilizes the Mutual Information Neural Estimator (MINE) to estimate the mutual information between the generated data distribution and labels. Experimental results on three datasets, including Mixture of Gaussian (MoG), MNIST and CIFAR10 datasets, show that our AC-GAN-MINE performs better than AC-GAN and TAC-GAN.
Location: Remote via Webex
Committee:

Prof. Dimitri Metaxas

Prof. Sungjin Ahn

Prof. Yongfeng Zhang

Prof. Zheng Zhang

Start Date: 08 May 2020;
Start Time: 11:00AM - 12:00PM
Title: Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification

Bio:
Speaker:
Abstract: In cross-lingual text classification, one seeks to exploit labeled data from one language to train a text classification model that can then be applied to a completely different language. Recent multilingual representation models have made it much easier to achieve this. Still, there may still be subtle differences between languages that are neglected when doing so. To address this, we present a semi- supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language. Compared with a number of strong baselines, we observe significant gains in effectiveness on document and intent classification for a diverse set of languages.
Location: Remote via Webex
Committee:

Gerard de Melo (Chair), Yongfeng Zhang, Karl Stratos, Srinivas Narayana

Start Date: 08 May 2020;
Start Time: 03:00PM - 04:30PM
Title: Real-time, Robust 6D Pose Estimation and Tracking for Dexterous Manipulation

Bio:
Speaker:
Abstract: Many manipulation tasks, such as picking, placement or within-hand manipulation, require the object’s pose relative to the manipulating agent, robot or human. At the same time, these tasks frequently involve significant occlusions, which complicate the estimation and tracking process. This work presents a framework based on RGB-D data. It aims towards robust pose estimation under severe occlusions, such as those arising due to a hand occluding a manipulated object. It also aims for short response times so as to allow for responsive decision making in highly dynamic setups. The proposed framework leverages the complementary attributes of deep learning and 3D geometric reasoning, so as to achieve high accuracy as well as generalization to different robotic manipulation scenarios. Additionally, only synthetic data are required for training, making the proposed approach applicable to new tasks, circumventing the overhead of collecting labeled real world data. Extensive evaluation on multiple real world benchmarks demonstrates superior performance when compared with state-of-the-art approaches.
Location: Remote via Webex
Committee:

### Committee Members: Abdeslam Boularias, Ahmed Elgammal, Desheng Zhang

Start Date: 14 May 2020;
Start Time: 02:30PM - 04:00PM
Title: Algebraic Methods and their Applications

Bio:
Speaker:
Abstract: Algebraic methods are one of the most important tools for solving various computational problems as well as proving lower bounds for various computational models. In this talk, we will describe various instantiations of the same. The talk will consist of a high-level view of various lower bounds and applications along with a detailed discussion on recent progress in deterministic factorization and learning arithmetic circuits.
Location: Remote via Webex (details below)
Committee:

Prof. Sepehr Assadi, Prof. Swastik Kopparty, Prof. Shubhangi Saraf, Prof. Yongfeng Zhang

Start Date: 18 May 2020;
Start Time: 10:00AM - 12:00PM
Title: Towards a Conversation Analysis Approach for Multicommunicating in Open-Domain, Retrieval-Based Conversational Agents

Bio:
Speaker:
Abstract: Multicommunication is a prevalent behavior found in online human conversation, and yet most human-to-chatbot approaches do not address it. Intrinsic parts of human-to-human conversations like the flexibility of communication tempo, the compartmentalization of conversation, and the topics and intensity of interactions are elevated in multicommunicative human-to-chatbot conversations. Previous Deep Learning approaches may overlook these intrinsic characteristics due to them focusing on the evaluation of human-likeness through sensible and specific responses [Google’s Meena] or on-topic responses [Amazon’s Alexa Prize Challenge]. In order to both capture the human-likeness of sensible, specific, and on-topic responses, we use the fundamental unit of conversation as defined by Conversational Analysis: the adjacency pair; and expand it into an Adjacency Relation to describe a succession of adjacency pairs. To illustrate this Conversational Analysis and Multicommunication Theory, we are developing an Interactive Behavior Tree Conversation Management Framework for Multicommunicating in an Open-Domain, Retrieval-Based Chatbot. We will describe a preliminary deployment of this framework and highlight its advantages (and limitations). We show that this framework provides additional human-likeness not provided by other response retrieval techniques and show this using our own Multi-Engagement Metric. Furthermore, we describe a real problem where a solution of this type will be applied and studied.
Location: Remote via Webex
Committee:

Dr. Mubbasir Kapadia, Dr. Gerard de Melo, Dr. Karl Stratos, and Dr. Sepehr Assadi (External Member)

Start Date: 22 May 2020;
Start Time: 01:00PM - 02:00PM
Title: Inferring Time-delayed Hidden Causal Relations In Reinforcement Learning

Bio:
Speaker:
Abstract: Despite the remarkable progress made by deep RL agents in reaching human-level performance and beyond, they continue to lag behind humans in terms of data efficiency. Model-based RL algorithms arguably require less data than model-free ones. But learning models that are sufficiently accurate for planning is still a challenging problem. The difficulty in learning accurate predictive models can be mostly attributed to the partial observability of the states. In robotics, for example, the Markov condition is seldom verified. Future states and rewards often depend on the entire history of actions and observations. LSTM and GRU architectures are general-purpose tools for solving problems of partial observability by discovering and remembering pertinent information. They tend, however, to require large amounts of data, and they cannot be easily interpreted. To address these two issues, we present here an approach that combines the merits of general function approximators such as neural networks with probabilistic graphical models for representing hidden variables. Given a stream of actions, observations and rewards, a neural network is trained to predict future observations and rewards. Simultaneously, a graphical model of causal relations between observations occurring at different time steps is also gradually constructed. The values of the variables in the graph are also provided to the neural network as additional inputs along with the observations. The learned predictive model is then utilized by the agent to select actions based on their predicted future rewards.
Location: Remote via Webex
Committee:

### Prof. Abdeslam Boularias, Prof. Sungjin Ahn, Prof. Yongfeng Zhang, Prof. Shubhangi Saraf

Start Date: 27 May 2020;
Start Time: 03:00PM - 04:30PM
Title: Design, Model Identification and Control of a Low-Cost Wheeled Mobile Robot Using Differentiable Physics

Bio:
Speaker:
Abstract: With the availability of affordable 3D printers and single-board microcontrollers such as Arduino and Raspberry Pi, there is growing interest in building affordable robots for various tasks. We designed a low-cost wheeled mobile robot and proposed an analytical model for predicting its motion that is derived from first principles. Further, we showed how the robot motion can be accurately predicted under the influence of motor torques and friction forces by learning unknown physical parameters using gradient descent. By combining our analytical model with a neural network, our robot is able to automatically predict the control signal sequences for driving itself along predefined curves. Compared to existing black-box system identification methods and other data-driven techniques, our model is more explanatory and requires much less data. Through experimental evaluation, the robot has great alignment with the predicted motion with a limited amount of data.
Location: Remote via Webex
Committee:

Prof. Mridul Aanjaneya, Prof. Abdeslam Boularias, Prof. He Zhu and Prof. Santosh Nagarakatte

Start Date: 28 May 2020;
Start Time: 02:00PM - 03:30PM
Title: Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data

Bio:
Speaker:
Abstract: We propose a data privacy-preserving and communication efﬁcient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our proposed framework aims to train a central generator learns from distributed discriminator, and use the generated synthetic image solely to train the segmentation model. We validate the proposed framework on the application of health entities learning problem which is known to be privacy sensitive. Our experiments show that our approach: 1) could learn the real image’s distribution from multiple datasets without sharing the patient’s raw data. 2) is more efﬁcient and requires lower bandwidth than other distributed deep learning methods. 3) achieves higher performance compared to the model trained by one real dataset, and almost the same performance compared to the model trained by all real datasets. 4) has provable guarantees that the generator could learn the distributed distribution in an all important fashion thus is unbiased.
Location: Remote via Webex
Committee:

Prof. Dimitri Metaxas; Prof. Konstantinos Michmizos; Prof. Sungjin Ahn; Prof. Aaron Bernstein

Start Date: 28 May 2020;
Start Time: 02:00PM - 03:30PM
Title: Understanding Dictionaries at the Intersection of Theory and Practice

Bio:
Speaker:
Abstract: Dictionaries are fundamental data structures that map a set of keys to values. A dictionary generally must support insertion and lookup, but also optionally delete, update, successor and scan. A system that implements a dictionary is called a key-value store. The work presented in this talk applies the theory of external memory dictionaries to achieve improved performance in systems and also derives theoretically interesting problems and solutions from commonly used systems data structures. This talk will cover file systems aging, optimal external memory hash tables, and high-performance key-value stores targeting NVMe.
Location: Remote via Webex
Committee:

Prof. Martin Farach-Colton (Advisor), Prof. Aaron Bernstein, Prof. Sudarsun Kannen, Dr. Guy Blelloch (External member)

Start Date: 12 Jun 2020;
Start Time: 01:30PM - 02:30PM
Title: Application of meta-learning on Facial Action Unit Detection and learning private-shared latent representation in multimodal VAE

Bio:
Speaker:
Abstract: Detecting facial action units (AU) is one of the fundamental steps in automatic recognition of facial expression of emotions and cognitive states. Though there have been a variety of approaches proposed for this task, most of these models are trained only for the specific target AUs, and as such, they fail to easily adapt to the task of recognition of new AUs. In this paper, we propose a deep learning approach for facial AU detection that can easily and quickly adapt to a new AU or target subject by leveraging only a few labeled samples from the new task (either an AU or subject) based on the notion of the model-agnostic meta-learning. We show on two benchmark datasets, BP4D and DISFA, for facial AU detection that the proposed approach can be easily adapted to new tasks. Using only a few labeled examples from these tasks, the model achieves large improvements over the baselines. Multi-modal generative models represent an important family of deep models, whose goal is to facilitate representation learning on data with multiple views or modalities. However, current deep multi-modal models focus on the inference of shared representations, while neglecting the important private aspects of data within individual modalities. In this paper, we introduce a disentangled multi-modal variational autoencoder (DMVAE) that utilizes disentangled VAE strategy to separate the private and shared latent spaces of multiple modalities. We specifically consider the instance where the latent factor may be of both continuous and discrete nature, leading to the family of general hybrid DMVAE models. We demonstrate the utility of DMVAE on a semi-supervised learning task. Our experiments on several benchmark datasets, including MNIST, SVHN, and CelebA, indicate the importance of the private-shared disentanglement as well as the hybrid latent representation.
Location: Remote via Webex
Committee:

Prof. Sungjin Ahn

Prof. Abdeslam Boularias

Prof. Swastik Kopparty

Start Date: 15 Jun 2020;
Start Time: 01:00PM - 03:00PM
Title: Toward a Fairer Information Retrieval System

Bio:
Speaker:
Abstract: With the increasing popularity and social influence of information retrieval (IR) systems, various studies have raised concerns on the presence of bias in IR and the social responsibilities of IR systems. This dissertation explored ways to investigate, understand and evaluate fairness-aware IR. This dissertation makes the following contributions: (1) An investigation of existing bias presented in search engine results. Several topical diversity fairness ranking strategies were explored to understand the relationship between relevance and fairness in search results. The experimental results showed that different fairness ranking strategies result in distinct utility scores and may perform differently with distinct datasets. (2) A statistical framework to analyze the relationship between data and fairness algorithms. A series of use cases were presented to demonstrate how this framework could be applied to provide insights to fairness optimization problems. (3) A novel evaluation metric situated in fairness-aware ranking and an effective ranking algorithm that jointly optimized fairness and utility. The experiments demonstrated that the proposed metric and algorithm were able to capture multiple aspects of the ranking and improve fairness while closely approximating the ideal utility.
Location: Remote via Webex
Committee:

Prof. Chirag Shah (Chair)

Prof. Yongfeng Zhang

Prof. Geral de Melo

Dr. Fernando Diaz (External Member, Microsoft Research Montréal)

Start Date: 23 Jun 2020;
Start Time: 10:00AM - 12:00PM
Title: Multimodal Communication: Commonsense, Grounding, and Computation Abstract

Bio:
Speaker:
Abstract: From the gestures that accompany speech to images in social media posts, humans effortlessly combine words with visual presentations. Communication succeeds even though visual and spatial representations are not necessarily wired to syntax and conventions, and do not always replicate appearance. Machines, however, are not equipped to understand and generate such presentations due to people’s pervasive reliance on commonsense and world knowledge in relating words and external presentations. I show the potential of discourse modeling for solving the problem of multimodal communication. I start by presenting a computational model for diagram understanding, extending accounts from linguistics to learn the interpretation of schematic elements such as arrows. I then present a novel framework for modeling and learning a deeper combined understanding of text and images by classifying inferential relations to predict temporal, causal, and logical entailments in context. This enables systems to make inferences with high accuracy while revealing author expectations and social-context preferences. I proceed to design methods for generating text based on visual input that use these inferences to provide users with key requested information. The results show a dramatic improvement in the consistency and quality of the generated text by decreasing spurious information by half. Finally, I describe the design of two multimodal communicative systems that can reason on the context of interactions in the areas of human-robot collaboration and conversational artificial intelligence and describe my research vision: to build human-level communicative systems and grounded artificial intelligence by leveraging the cognitive science of language use.
Location: Remote via Webex
Committee:

Matthew Stone (Chair)

Gerard de Melo

Kostas Bekris

Ani Nenkova (external member)

Start Date: 24 Jun 2020;
Start Time: 11:00AM - 12:30PM
Title: Scalable self-supervised representation learning of world models

Bio:
Speaker:
Abstract: Humans perceive their surrounding environment through many trial and error interactions and minimal explicit supervision. This has inspired a new family of deep models that are mainly designed for world representation modelling, and work based on self-supervised learning techniques combined with naturally plausible inductive biases. In every world representation model, object detection and tracking is the first and most fundamental step towards meaningful scene perception. In this talk, I will briefly touch on the main drawbacks of traditional supervised models and give a brief background on newly proposed unsupervised models. Furthermore, I will introduce our proposed object-centered holistic perception model, which is able to do object detection, segmentation, tracking and generation as part of a single model all at once. This model is trained without any supervision at all. Previous unsupervised state-of-the-art models possess important limitations, either not considering temporal dynamics of the environment or not scaling to crowded scenes with many objects. Our proposed probabilistic generative model (1) significantly improves the tracking scalability (two orders of magnitude) compared to the state of the art models, to nearly a hundred objects. (2) Reduces computation time from O(N) to O(1). (3) Is able to model scenes with complex dynamic backgrounds. (4) Is the ﬁrst unsupervised object representation model shown to work for natural scenes containing several tens of moving objects. (4) Is shown to work reasonably well on both a synthetic dataset as well as real CCTV camera footage. Finally I will also explore how this unsupervised approach can be connected to some applications in task-specific language semantic understanding and planning domain generation.
Location: Remote via Webex
Committee:

Committee members: Prof. Karl Stratos, Prof. Yongfeng Zhang, Prof. David Pennock

Start Date: 02 Jul 2020;
Start Time: 02:00PM - 03:30PM
Title: Online Matching with Recourse: Random Edge Arrivals

Bio:
Speaker:
Abstract: The matching problem in the online setting models the following situation: we are given a set of servers in advance, the clients arrive one at a time, and each client has edges to some of the servers. Each client must be matched to some incident server upon arrival (or left unmatched) and the algorithm is not allowed to reverse its decisions. This model is motivated by applications in wireless communication, content delivery, and job scheduling. However due to the no-reversal restriction, we are not able to guarantee an exact maximum matching in this model, only an approximate one. Therefore, it is natural to study a different setting where priority is given to match as many clients as possible, and changes to the matching are allowed but expensive. Formally, the goal is to always maintain a maximum matching while minimizing the number of changes to the matching. This model is called the online matching with recourse. We study the more general model where all the vertices are known in advance, but the edges of the graph are revealed one at a time. In this model, there is a lower bound of \Omega(n^2) known even for the case of simple graphs. So, we study a natural relaxation where the edges arrive in a random order. We show that for some special cases of graphs, random arrival is significantly easier. On the other hand, for general graphs the problem is as hard as in the adversarial setting.
Location: Remote via Webex
Committee:

Prof. Aaron Bernstein, Prof. Eric Allender, Prof. Sepehr Assadi, Prof. Jingjin Yu

Start Date: 13 Jul 2020;
Start Time: 01:30PM - 03:30PM
Title: Order-Revealing Encryption: New Constructions and Barriers

Bio:
Speaker:
Abstract: Order-revealing encryption (ORE) is a symmetric encryption scheme that gives a public procedure by which two ciphertexts can be compared to reveal the order of their underlying plaintexts. ORE is a very popular primitive for outsourcing databases and has seen deployments in products and usage in applied research, as it allows for efficiently performing range queries over encrypted data. However, a series of works, starting with Naveed et al. (CCS 2015), have shown that when the adversary has a good estimate of the distribution of the data, ORE provides little protection. In this dissertation, we present our works on order-revealing encryption, which include novel security notions, new constructions, and barriers. First, we consider the best-possible security notion for ORE (ideal ORE), which means that, given the ciphertexts, only the order is revealed — anything else, such as the distance between plaintexts, is hidden. Despite the fact that this notion provides the best security for ORE, the only known constructions of ideal ORE are based on cryptographic multilinear maps and are currently too impractical for real-world applications. In this work, ii we give evidence that building ideal ORE from weaker tools is hard. Essentially, we show black-box separations between ideal ORE and most symmetric-key primitives, as well as public-key encryption and anything else implied by generic group model in a black-box way. This result tells us that any construction of ORE must either (1) achieve weaker notions of security, (2) be based on more complicated cryptographic tools, or (3) require non-black-box techniques thus it suggests that any ORE achieving ideal security will likely be at least somewhat inefficient. Then we propose a new meaningful security notion— parameter-hiding. In our definition, we consider the case that the database entries are drawn identically and independently from the distribution of known shape, but for which the mean and variance are not (and thus the attacks of Naveed et al. do not apply). We say an ORE is parameter-hiding, if for any probabilistic and polynomial-time adversary, given any sequence (polynomial-size) of ciphertexts, the mean and variance of the message distribution are hidden. Based on this notion, we build the corresponding construction of ORE that satisfying it from bi-linear map. Next, we study a particular case of ORE, which is called order-preserving encryption. OPE schemes are the subset of ORE schemes for which the ciphertexts themselves are numerical values that can be compared naturally. For OPE, we study its ciphertext length under the security notion proposed by Chenette et al. for OPE (FSE 2016); their notion says that the comparison of two ciphertexts should only leak the index of the most significant bit on which they differ (MSDB-secure). In their work, they propose two constructions, both ORE and OPE; the ORE scheme has very short ciphertexts that only expand the plaintext by a factor ≈ 1.58, while the ciphertext-size of the OPE scheme expands the plaintext by a security-parameter factor. We give evidence that this gap between ORE and OPE is inherent, by proving that any OPE meeting the information-theoretic version of their security definition (for instance, in the random oracle model) must have the ciphertext length close to that of their constructions.
Location: Remote via Webex
Committee:

Prof. David Cash

Prof. Eric Allender

Prof. Shiqing Ma

Prof Qiang Tang (External Member)

Start Date: 17 Jul 2020;
Start Time: 10:00AM - 11:30AM
Title: Interpretability vs. Explainability in Machine Learning

Bio:

Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University, and directs the Prediction Analysis Lab, whose main focus is in interpretable machine learning. She is also an associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is a three time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is past chair of both the INFORMS Data Mining Section and the Statistical Learning and Data Science section of the American Statistical Association. She has also served on committees for DARPA, the National Institute of Justice, and AAAI. She has served on three committees for the National Academies of Sciences, Engineering and Medicine, including the Committee on Applied and Theoretical Statistics, the Committee on Law and Justice, and the Committee on Analytic Research Foundations for the Next-Generation Electric Grid. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. She gave a Thomas Langford Lecturer at Duke University during the 2019-2020 academic year, and will be the Terng Lecturer at the Institute for Advanced Study in 2020.

Speaker:
Abstract: With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. Explanations for black box models are not reliable, and can be misleading. If we use interpretable machine learning models, they come with their own explanations, which are faithful to what the model actually computes. In this talk, I will discuss some of the reasons that black boxes with explanations can go wrong, whereas using inherently interpretable models would not have these same problems. I will give an example of where an explanation of a black box model went wrong, namely, I will discuss ProPublica's analysis of the COMPAS model used in the criminal justice system: ProPublica's explanation of the black box model COMPAS was flawed because it relied on wrong assumptions to identify the race variable as being important. Luckily in recidivism prediction applications, black box models are not needed because inherently interpretable models exist that are just as accurate as COMPAS. I will also give examples of interpretable models in healthcare. One of these models, the 2HELPS2B score, is actually used in intensive care units in hospitals; most machine learning models cannot be used when the stakes are so high. Finally, I will discuss two long-term projects my lab is working on, namely optimal sparse decision trees and interpretable neural networks.
Location:
Committee:
Start Date: 17 Jul 2020;
Start Time: 02:00PM - 04:00PM
Title: Scalable, Physics-aware 6D Pose Estimation for Robot Manipulation

Bio:
Speaker:
Abstract: Robot Manipulation often depend on some form of pose estimation to represent the state of the world and allow decision making both at the task-level and for motion or grasp planning. Recent progress in deep learning gives hope for a pose estimation solution that could generalize over textured and texture-less objects, objects with or without distinctive shape properties, and under different lighting conditions and clutter scenarios. Nevertheless, it gives rise to a new set of challenges such as the painful task of acquiring large-scale labeled training datasets and of dealing with their stochastic output over unforeseen scenarios that are not captured by the training. This restricts the scalability of such pose estimation solutions in robot manipulation tasks that often deal with a variety of objects and changing environments. The thesis first describes an automatic data generation and learning framework to address the scalability challenge. Learning is bootstrapped by generating labeled data via physics simulation and rendering. Then it self-improves over time by acquiring and labeling real-world images via a search-based pose estimation process. The thesis proposes algorithms to generate and validate object poses online based on the objects’ geometry and based on the physical consistency of their scene-level interactions. These algorithms provide robustness even when there exists a domain gap between the synthetic training and the real test scenarios. Finally, the thesis proposes a manipulation planning framework that goes beyond model-based pose estimation. By utilizing a dynamic object representation, this integrated perception and manipulation framework can efficiently solve the task of picking unknown objects and placing them in a constrained space. The algorithms are evaluated over real-world robot manipulation experiments and over large-scale public datasets. The results indicate the usefulness of physical constraints in both the training and the online estimation phase. Moreover, the proposed framework, while only utilizing simulated data can obtain robust estimation in challenging scenarios such as densely packed bins and clutter where other approaches suffer as a result of large occlusion and ambiguities due to similar looking texture-less surfaces.
Location: Remote via Webex
Committee:

Ahmed Elgammal

Henrik Christensen (External committee member)

Start Date: 27 Jul 2020;
Start Time: 01:00PM - 03:00PM
Title: Distributed Frameworks for Approximate Data Analytics

Bio:
Speaker:
Abstract: Data-driven discovery has become critical to the mission of many enterprises and scientific research. At the same time, the rate of data production and collection is outpacing technology scaling, suggesting that significant future investment, time, and energy will be needed for data processing. Employing more hardware resources can address the extra processing needs by either adding more CPU cores/memory (scale-up) or more worker nodes~(scale-out). However, it will introduce higher computing cost that may not be feasible when budget is limited. One powerful tool to address the above challenge is approximate computing, which trades off computational time and resources with computational accuracy by reducing the amount of data needed to be processed. Fortunately, many data analytic applications such as data mining, log processing, video/image processing are amenable to approximation. In this thesis, we describe the design and implementation of approximation frameworks to accelerate distributed data analytics. We present the frameworks targeting a variety of tasks and datasets, including log aggregation, text analytics and video querying and aggregation: Our first work targets approximating aggregation jobs with error estimation. Aggregation is central to many decision support queries. Aggregation is an important component in OLAP~(Online Analytical Processing)systems, is frequently used for summarizing data patterns in business intelligence. Aggregation jobs often involve multiple transformation steps in a data processing pipeline. We design and implement a sampling-based approximation framework called ApproxSpark, that can rigorously derive estimators with error bounds for approximate aggregation. Our second work targets approximate text analytic tasks. We propose and evaluate a framework called EmApprox that uses sampling-based approximation to speed up the processing of a wide range of queries over large text datasets. EmApprox builds an index for a dataset by learning a natural language processing model, producing vectors representing words and subcollections of documents. Finally, we target approximate video analytics. Video data embed rich and high-quality information. Yet video analytics is particularly compute intensive as it often involves invoking a deep convolutional neural network~(CNN) for object detection. We design and implement a approximate video analytics framework called VidApprox for accelerating video queries that involve object detection. VidApprox first leverages cheap CNNs to learn vector representations of video segments, and further processes the vectors as persistent index structure. At query processing time, the index lookup will serve as auxiliary information for only retrieving a subset of more similar video segments.
Location: Remote via Webex
Committee:

Prof. Thu Nguyen (chair)

Prof. Uli Kremer

Prof. Yongfeng Zhang

Prof. Zhenhua Liu (Stony Brook University)

Start Date: 12 Aug 2020;
Start Time: 11:00AM - 12:00PM
Title: Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement

Bio:
Speaker:
Abstract: The success of machine learning techniques, including few-shot learning, transfer learning, and image synthesis, relies on how data is represented. Flagship approaches, such as variational autoencoders (VAEs), model the nonlinear generative relationship between data and latent factors in an unsupervised manner. However, such representations entangle and hide more or less the factors of variation inherent in the data. To tackle this limitation, we argue that the desired representation is supposed to satisfy two properties. First, its latent space is able to explain the variation of data in a way that the factors are disentangled. That is, when changing one factor while freezing other factors, the variation is observed in only one aspect of the data. Second, relevant factors that are statistically dependent on the data should be discerned from nuisance factors. Inspired by these properties, we extend VAEs to a hierarchical Bayesian model that introduces hyper-priors on the variances of Gaussian latent priors, thus mimicking an infinite mixture, while maintaining tractable learning and inference of traditional VAEs. Our key insight is that the prior distribution of disentangled latent factors should be learned from data instead of being fixed to normal Gaussian distribution as in VAEs. By doing so, we are also able to better model nuisance factors. Extensive analysis shows the importance of partitioning the latent dimensions corresponding to relevant factors and nuisances, and treating them in different ways. Our proposed model outperforms the state of the art both quantitatively and qualitatively in terms of latent disentanglement on several challenging benchmarks, such as Sprites, 3D-Face, Teapots, and Celeb-A datasets.
Location: Remote via Webex
Committee:

Prof. Yongfeng Zhang

Prof. Konstantinos Michmizos

Prof. Martin Farach-Colton

Start Date: 14 Aug 2020;
Start Time: 03:00PM - 04:30PM
Title: Optimally Covering Critical Sets in R^2 with A Team of Mobile Sensing Robots

Bio:
Speaker:
Abstract: Optimal coverage is an important problem in robotics, e.g. for monitoring changing environments, guarding the perimeter of a building for security, detecting emergencies in an area, and so on. In this domain, we investigated several challenging variations, modelling the robots’ sensing abilities with different models (1D / 2D range sensing) and examining different types of sets to guard (1D perimeter / 2D region). Some of the problems could be optimized quite efficiently, while others are intractable to obtain optimal solutions or even to approximate within some certain ratio. Specifically, in a 1D scenario where the sets to guard are line segments and a single robot can only cover a continuous boundary segment, the maximum coverage length of a robot could be minimized quite efficiently with low polynomial time complexity. However, when the group of robots are heterogeneous, the corresponding problem of minimizing deployment costs or maximum capacity exploitation can be NP-hard to solve. When studying the problem in a 2D scenario, it is even NP-hard to approximate the minimum coverage radius within 15% to cover the boundary of a simple polygon or the simple polygon itself. Yet, it is still possible to obtain good computational results experimentally for these problems by applying some realistic assumptions or using methods as dynamic programming and integer programming.
Location: Remote via Webex
Committee:

Prof. Kostas Bekris

Prof. Jie Gao

Prof. Richard Martin

Start Date: 10 Sep 2020;
Start Time: 10:30AM - 12:00PM
Title: Decision Making for Many Mobile Objects

Bio:

Jingjin Yu is an Assistant Professor in the Department of Computer Science at Rutgers University. He received his B.S. from the University of Science and Technology of China (USTC) in 1998. He obtained his M.S. in Computer Science (2010) and Ph.D. in Electrical and Computer Engineering (2013), both from the University of Illinois, where he briefly stayed as a postdoctoral researcher. He was a postdoctoral researcher at the Massachusetts Institute of Technology from 2013 to 2015. He is broadly interested in the area of algorithmic robotics, control, and optimization, focusing on issues related to optimality, complexity, and the design of efficient decision-making methods for single- and multi-robot systems. He is a recipient of the NSF CAREER award.

Speaker:
Abstract: Systems composed of many mobile bodies (e.g., robots or movable objects) are often highly complex, due to interactions between the bodies. For example, in a multi-robot motion planning scenario, applicable to warehouse automation, port automation, and autonomous driving settings, as the density of robots increases, it becomes increasingly difficult to coordinate robots’ collision-free motion while simultaneously seeking to optimize the overall system throughput. In this talk, I will discuss several practical decision-making problems for systems containing many mobile objects, examine the involved computational challenges, and highlight state-of-the-art algorithmic solutions for dealing with the challenges.
Location: Via Zoom
Committee:
Start Date: 14 Sep 2020;
Start Time: 10:30AM -
Title: Intelligence beyond the neuron: Energy-Efficient and Robust Brain-inspired Computing Algorithms Evaluated in Neuro-Morphic and Neuro-Rehabilitation Robots

Bio:

Konstantinos P. Michmizos is an Assistant Professor of Computer Science, an Executive Council Faculty at Center for Cognitive Science, and a Faculty member of the Brain Health Institute and CBIM at Rutgers University. Previously he held positions at Harvard Medical School and MIT.

Speaker:
Abstract: In our strive for ever more efficient and intelligent systems, the Von Neumann computing paradigm that has been serving us for fifty years is no longer sufficient. The problem lies in the paradox that, despite the pioneering early connections between computation and brain studies, today’s algorithms and systems diverge from what we have lately learned about the brain’s information processing and learning. The neuronal and non-neuronal computing principles lead to dramatic efficiencies in nature but make them extremely ill-suited for executing on today’s energy- and memory-greedy processors. This talk will present our efforts to advance non-Von Neumann computations that draw from the brain’s functional analogies and redefine algorithms as spiking neuronal astrocytic networks, where memory, learning and computing are tightly integrated. Specifically, it will describe how we a) develop and synthesize computational models and learning algorithms grounded in recent breakthroughs in neuroscience, b) translate this spectrum of knowledge into scalable computing primitives and deploy them on large-scale neuromorphic processors, and c) evaluate the emerging behavior as a solution to foundational problems in robotics, leading to 2 orders of magnitude more energy-efficient and significantly more robust solutions compared to the state-of-the-art deep learning. The talk will conclude with the learned lessons and the untapped challenges in harnessing intelligence for robots, by either archetyping it to serve people’s life or targeting it to assist people’s recovery.
Location: Via Webex meeting
Committee:
Start Date: 15 Sep 2020;
Start Time: 10:30AM -
Title: Model Identification for Robotic Manipulation

Bio:

Abdeslam Boularias is an Assistant Professor of computer science at Rutgers, The State University of New Jersey, where he works on robot learning. Previously, he was a Project Scientist in the Robotics Institute of Carnegie Mellon University, and a Research Scientist at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, in the department of Empirical Inference. He receieved a Ph.D. in computer science from Laval University in Canada, an M.S. in computer science from Paris-Sud University in France, and a B.S. in computer science from the École Nationale Supérieure d’Informatique (ESI) in Algeria. He is broadly interested in the area of reinforcement learning, robotic manipulation, and robot vision. He is a recipient of the NSF CAREER award.

Speaker:
Abstract: A popular approach in robot learning is model-free reinforcement learning (RL), where a control policy is learned directly from sensory inputs by trial and error without explicitly modeling the effects of the robot’s actions on the controlled objects or system. While this approach has proved to be very effective in learning motor skills, it suffers from several drawbacks in the context of object manipulation due to the fact that types of objects and their arrangements vary significantly across different tasks and environments. An alternative approach that may address these issues more efficiently is model-based RL. A model in RL generally refers to a transition function that maps a state and an action into a probability distribution over all possible future states. In this talk, I will present my recent works on data-efficient physics-driven techniques for identifying models of manipulated objects. To perform a new task in an environment with unknown objects, a robot first identifies from sequences of images the 3D mesh models of the objects, as well as their physical properties such as their mass distributions, moments of inertia and friction coefficients. The robot then reconstructs in a physics simulation the observed scene, and imagines the future motions of the objects when manipulated. The predicted motions are used to select a sequence of appropriate actions to apply on the real objects. The proposed techniques are evaluated on a wide range of applications in robotics, such as grasping, nonprehensile manipulation, rearrangement, and warehouse automation.
Committee:
Start Date: 17 Sep 2020;
Start Time: 10:30AM -
Title: Intelligent Human-Aware Building Design

Bio:

Mubbasir Kapadia is an Assistant Professor in the Computer Science department at Rutgers University, and the Director of the Intelligent Visual Interfaces Lab. Previously, he held positions at Disney Research Zurich and the Human Modeling and Simulation Lab at University of Pennsylvania. He received his PhD in Computer Science at University of California, Los Angeles. Kapadia’s research lies at the intersection of artificial intelligence, visual computing, and human-computer interaction, with a mission to develop intelligent visual interfaces to empower content creation for human-aware architectural design, digital storytelling, and serious games. He has published more than 100 journal and conference papers at premier venues in Computer Graphics, and Artificial Intelligence. Kapadia’s research is funded by DARPA and NSF, and through generous support from industrial partners including Disney Research, Autodesk Research, Adobe Research, and Unity Labs.

Lab Website: https://ivi.cs.rutgers.edu/

Project Website: https://www.humanbuildinginteraction.com/

Speaker:
Abstract: My research lies at the intersection of artificial intelligence, visual computing, and human-computer interaction, with a focus on human modeling and simulation. My overarching career goal is to address key limitations and simplifying assumptions in virtual crowds, to make it an enabling technology for architectural design, urban simulation, and other forms of content creation. Towards this goal, I have helped lay the foundation for realism in virtual crowds, introduced new experimental paradigms for studying collective human behavior, developed model-free techniques for multi-agent learning from multi-modal data, and enhanced computer-aided design pipelines to seamlessly account for human behavior. My research contributions are actively used by researchers and practitioners all over the world and have helped establish a burgeoning multi-disciplinary research community in Human-Building Interaction''. In this talk, I will describe our ongoing work along three main thrusts: (a) computational modeling of the cognitive processes underlying how people inhabit spaces, (b) predicting the performance of buildings with respect to its inhabitants, and (c) integrating human-centric performance metrics within computer-aided design pipelines. These research objectives culminate towards the goal of democratizing human behavior simulation as a service and developing end-to-end solutions for intelligent human-aware building design.
Location: Via Webex
Committee:
Start Date: 18 Sep 2020;
Start Time: 10:15AM - 12:00PM
Title: Computational Approaches for Semantics-Aware Typographical Choices

Bio:
Speaker:
Abstract: Typographic signals carry strong semantic connotations, e.g., they may convey excitement, anger or even sweetness, which empowers them to affect almost any aspect of life, from perception of an email, to the perceived sweetness of a cup of coffee. This thesis explores some of the possibilities that can be offered by computational approaches to support users in understanding and taking advantage of this impact. More specifically, the focus is on learning font semantics from crowdsourced and Web data, and using this information to facilitate font search and recommendation. Among the novel contributions are the use of CNN-based embeddings to represent fonts in attribute learning, leveraging emotion theories and lexical relations to infer font semantics, a multimodal font search method that allows specifying a reference font together with the target semantic additions, enabled by a cross-modal representation of fonts and words, and the proposal of affect-aware word clouds that let users specify a target emotion, which is used to recommend fonts and color palettes with congruent affective connotations.
Location: Remote via Webex
Committee:

Gerard de Melo

Matthew Stone

Abdeslam Boularias

Start Date: 18 Sep 2020;
Start Time: 02:00PM - 04:00PM
Title: Failure Explanation Planning for Robotic Manipulation: Planning with Discrete Uncertainty and Many Objects

Bio:
Speaker:
Abstract: Traditional task and motion planning problems in robot manipulation focus on finding valid plans for robot arms to fulfill different tasks. Collision-free plans, however, are not often immediately available in highly-challenging manipulation tasks. The first part of the talk will focus on picking an item in the presence of obstacles, while taking into account the uncertainty of the perception process due to occlusions and partial views. The focus is on the case that no obvious collision-free solution can be found. The objective is to minimize the probability of collision with uncertain obstacles and to maximize the probability of picking the target object given a discrete set of pose hypotheses obtained from perception. It follows the concept of conformant probabilistic planning (CPP) and is modelled as a stochastic variant of the Minimum Constraint Removal (MCR) problem so as to achieve the desired objective. This approach is shown experimentally to be effective and is a promising direction for closing the gap between sensing uncertainty and motion planning in manipulation. The second part of the talk will cover the related topic of multi-object rearrangement. Rearrangement can be used either to move blocking obstacles before manipulating the target object when picking in clutter or it can be the task itself in applications, such as logistics, when multiple objects need to be presented to a consumer in a specific manner. Failure explanation planning is also suitable for solving such multi-object task planning problems, especially for non-monotone problem instances, where an object has to be first moved to an intermediate location before being moved to its target pose so that the problem is solvable. The talk covers the existing exploration of this problem in collaboration with colleagues for the case where the rearrangement process has to respect object-to-object interactions but no manipulator-to-object interactions.
Location: Remote via Webex
Committee:

Prof. Jingjin Yu

Prof. Fred Roberts

Start Date: 23 Sep 2020;
Start Time: 11:00AM - 12:00PM
Title: Improved Bounds for Distributed Load Balancing

Bio:
Speaker:
Abstract: We consider the problem of load balancing in the distributed setting. The input is a bipartite graph on clients and servers, where each client comes with a positive weight. The algorithm must assign each client to an adjacent server, minimizing the weighted sum of clients assigned to any one server. This problem has a variety of applications and has been widely studied under several different names, including scheduling with restricted assignment, semi-matching, and distributed backup placement. We show the first distributed algorithms to compute an O(1)-approximation to the load balancing problem in polylog(n) rounds. In the CONGEST model, we give an O(1)-approximation algorithm in polylog(n) rounds for unweighted clients. For weighted clients, the approximation ratio is O(log(n)). In the less constrained LOCAL model, we give an O(1)-approximation algorithm for weighted clients in polylog(n) rounds. Based on joint work with Sepehr Assadi and Aaron Bernstein.
Location: via Zoom
Committee:
Start Date: 06 Oct 2020;
Start Time: 03:00PM - 04:30PM
Title: A Novel Discretization and Numerical Solver for Non-Fourier Diffusion

Bio:
Speaker:
Abstract: We introduce the C-F diffusion model [Andreson and Tamma 2006; Xue et al. 2018] to computer graphics for diffusion-driven problems that has several attractive properties: (a) it fundamentally explains diffusion from the perspective of the non-equilibrium statistical mechanical Boltzmann Transport Equation, (b) it allows for a finite propagation speed for diffusion, in contrast to the widely employed Fick's/Fourier's law, and (c) it can capture some of the most characteristic visual aspects of diffusion-driven physics, such as hydrogel swelling, limited diffusive domain for smoke flow, snowflake and dendrite formation, that span from Fourier-type to non-Fourier-type diffusive phenomena. We propose a unified convection-diffusion formulation using this model that treats both the diffusive quantity and its associated flux as the primary unknowns, and that recovers the traditional Fourier-type diffusion as a limiting case. We design a novel semi-implicit discretization for this formulation on staggered MAC grids and a geometric Multigrid-preconditioned Conjugate Gradients solver for efficient numerical solution. To highlight the efficacy of our method, we demonstrate end-to-end examples of elastic porous media simulated with the Material Point Method (MPM), and diffusion-driven Eulerian incompressible fluids.
Location: Remote via Webex
Committee:

Prof. Mridul Aanjaneya

Prof. Mario Szegedy

Prof. Jingjin Yu

Prof. He Zhu

Start Date: 07 Oct 2020;
Start Time: 11:00AM - 12:00PM
Title: Network Coding Gaps for Completion Times of Multiple Unicasts

Bio:
Speaker:
Abstract: We study network coding gaps for the problem of makespan minimization of multiple unicasts. In this problem, distinct packets at different nodes in a network need to be delivered to a destination specific to each packet, as fast as possible. The *network coding gap* specifies how much coding packets together in a network can help compared to the more natural approach of routing. While makespan minimization using routing has been intensely studied for the multiple unicasts problem, no bounds on network coding gaps for this problem are known. We develop new techniques which allow us to upper bound the network coding gap for the makespan of $k$ unicasts, proving this gap is at most polylogarithmic in $k$. Complementing this result, we show there exist instances of $k$ unicasts for which this coding gap is polylogarithmic in $k$. Our results also hold for average completion time, and more generally any $\ell_p$ norm of completion times. Based on joint work with Bernhard Haeupler and Goran Zuzic, to appear in FOCS’2020.
Location: Via Zoom
Committee:
Start Date: 07 Oct 2020;
Start Time: 12:30PM - 02:00PM
Title: Modelling Goal Oriented Referential Dialogue using Discourse Coherence, Cognitive Modelling, and RL

Bio:
Speaker:
Abstract: Most of the dialogue literature either focuses on evaluation of utterances on their own creating problems in reference resolution or build end-to-end dialogue systems which are hard to customize. We present a dialogue system which represents discourse state as a graph of coherence relations. We use a referential dialogue task in which a director has to describe an ambiguous target to a matcher to show that a coherence-based representation can be successfully used to model dialogue contributions at discourse level and accomplish reference resolution of ambiguous referents. We use data-driven methods to implement individual dialogue modules, considerably reducing human effort. This representation is coupled with a RL method to learn flexible dialogue strategies. Secondly, we show that by combining this method with cognitive modelling we can learn context-sensitive clarification strategies which improve interactive experience with human subjects and better task success rate.
Location: Remote via Webex
Committee:

Dr. Matthew Stone (advisor), Dr. Yongfeng Zhang, Dr. Abdesalam Boularias, Dr. Zheng Zhang

Start Date: 21 Oct 2020;
Start Time: 04:00PM - 06:00PM
Title: Dynamic Heavy-Case Balls-and-Bins, Low-Associativity Paging, and the Address Translation Problem

Bio:
Speaker:
Abstract: Balls-and-bins games are a powerful mathematical abstraction that are used to model a wide variety of scenarios. We consider bounds on the fullest bin in the dynamic case of an unrestricted oblivious adversary: balls can be inserted and deleted in any order and they can be reinserted. There are n bins and at no point does the system has more than m balls. We give a bin-selection rule using d + 1 ≥ 3 hash functions that achieves a maximum load of at most (1 + o(1))m/n + (log log n)/d + O(1), with high probability in n, against any oblivious adversary in the dynamic setting. This result settles a long-standing open question and is tight up to low-order terms when m = ω(n log log n). We apply this result to a paging problem that requires a tight analysis in this range. Specifically, we use this balls-and-bins rule to show that for any fully-associative paging algorithm on a memory of size M, there is a (1 + o(1))-competitive paging algorithm with (1 + o(1))-memory augmentation that has nearly log log M associativity, where the associativity of an algorithm is the maximum number of memory locations in which a page can be placed. This low-associativity paging algorithm is in turn used to reduce the number of bits a translation lookaside buffer (TLB) needs to encode every virtual-to-physical address translation. We show how to pack these small encodings into a TLB and implement what we call hashed huge pages. These do not suffer from the main drawbacks of standard huge pages. We prove that hashed huge pages are (2 + o(1))-competitive with the optimal offline TLB/paging algorithm that uses standard huge pages.
Location: Remote via Webex
Committee:

Prof. Aaron Bernstein

Prof. Yongfeng Zhang

Start Date: 22 Oct 2020;
Start Time: 04:30PM - 06:00PM
Title: Enabling data-driven adaptations for large-scale in-situ scientific workflows

Bio:
Speaker:
Location: Remote via Webex
Committee:

Prof. Uli Kremer

Prof. Sudarsun Kannan

Prof. Mridul Aanjaneya

Start Date: 23 Oct 2020;
Start Time: 10:00AM - 11:30PM
Title: Mechanism Design and Data Science

Bio:

Prof. Hartline’s research introduces design and analysis methodologies from computer science to understand and improve outcomes of economic systems. Optimal behavior and outcomes in complex environments are complex and, therefore, should not be expected; instead, the theory of approximation can show that simple and natural behaviors are approximately optimal in complex environments. This approach is applied to auction theory and mechanism design in his graduate textbook Mechanism Design and Approximation which is under preparation.

Prof. Hartline received his Ph.D. in 2003 from the University of Washington under the supervision of Anna Karlin. He was a postdoctoral fellow at Carnegie Mellon University under the supervision of Avrim Blum; and subsequently a researcher at Microsoft Research in Silicon Valley. He joined Northwestern University in 2008 where he is currently a professor of computer science. He was on sabbatical at Harvard University in the Economics Department during the 2014 calendar year and visiting Microsoft Research, New England for the Spring of 2015. Prof. Hartline codirects the Institute for Data, Econometrics, Algorithms, and Learning (an NSF HDR TRIPODS institute) and is a cofounder of virtual conference organizing platform Virtual Chair.

Presented in association with the DATA-INSPIRE TRIPODS Institute

Speaker:
Abstract: Computer systems have become the primary mediator of social and economic interactions. A defining aspect of such systems is that the participants have preferences over system outcomes and will manipulate their behavior to obtain outcomes they prefer. Such manipulation interferes with data-driven methods for designing and testing system improvements. A standard approach to resolve this interference is to infer preferences from behavioral data and employ the inferred preferences to evaluate novel system designs. In this talk, Prof. Hartline will describe a method for estimating and comparing the performance of novel systems directly from behavioral data from the original system. This approach skips the step of estimating preferences and is more accurate. Estimation accuracy can be further improved by augmenting the original system; its accuracy then compares favorably with ideal controlled experiments, a.k.a., A/B testing, which are often infeasible. A motivating example will be the paradigmatic problem of designing an auction for the sale of advertisements on an Internet search engine.
Location: Join this event via Webex- Details on how to join are listed below
Committee:
Start Date: 27 Oct 2020;
Start Time: 10:00AM -
Title: Using satellite imagery and deep learning to target aid in data-sparse contexts

Bio:
Speaker:
Abstract: Aid policy has the potential to alleviate global poverty by targeting areas of concentrated need. A critical question remains, however, over whether aid is reaching the areas of most need. Often little ground-truth poverty data is available at a granular level (e.g., village) where aid interventions take place. This research explores remote sensing techniques to measure poverty and target aid in data-sparse contexts. Our study of Myanmar examines i) the performance of different methods of poverty estimation and ii) the extent to which poverty and other development characteristics explain community aid distribution. This study draws from the following sources of data: georeferenced community-driven development projects (n=12,504), daytime and nighttime satellite imagery, the Demographic and Health Survey, and conflict data. We first compare the accuracy of four poverty measures in predicting ground-truth survey data. Using the best poverty estimation in the first step, we investigate the association between village characteristics and aid per capita per village. Our results show that daytime features perform the best in predicting poverty as compared to the analysis of RSG color distribution, Kriging, and nighttime-based measures. We use a Convolutional Neural Network, pre-trained on ImageNet, to extract features from the satellite images in our best model. These features are then trained on the DHS wealth data to predict the DHS wealth index/poverty for villages receiving aid. The linear and non-linear estimator indicate that development assistance flows to low-asset villages, but only marginally. Aid is more likely to be disbursed to those villages that are less populous and farther away from fatal conflicts. Our study concludes that the nuances captured in satellite-based models can be used to target aid to impoverished communities.
Location: Via Zoom
Committee:
Start Date: 04 Nov 2020;
Start Time: 03:30PM - 05:00PM
Title: Urban-scale Human Mobility Data Synthesis via Learning-based models with Privacy Awareness

Bio:
Speaker:
Abstract: Real-time human mobility data obtained from urban infrastructure (e.g., cellular networks) have the potential to unleash the full power of social and collaborative computing systems and applications, from crowd sourcing, to ridesharing, mobile networking, trans- portation planning, emergency response, and pandemic mitigation. However, most of the city-wide human mobility data are often pro- prietary and thus cannot be accessed by the research community (e.g., researchers and practitioners) unless released by data owners (e.g., government or industries). Recently, under the context of Data Science for Social Good, some data owners are willing to share their mobility data with the public to unlock their value, but there are significant privacy and security concerns that remain in the way. To overcome these issues, we present a privacy-aware framework to generate synthetic yet realistic mobility data through augmented Generative Adversarial Nets (GANs) based on real world human mobility data from a cellular network. We plug in two auxiliary modules, i.e., privacy classifier and utility classifier, to apply targeted perturbation, which balances the trade-off between sensitive and non-sensitive mobility features. We implement our framework to generate urban-scale cellular connection records based on large scale real world data from Shenzhen cellular networks as a case study and empirically evaluate its performance in both utility and privacy aspects. The quantitative results show that with same level of privacy risks, our model outperforms the baseline models by 33% gain on temporal utility and an up to 49% gain on spatiotemporal utility.
Location: Remote via Webex
Committee:

Prof. Jie Gao

Prof. Yongfeng Zhang

Prof. Eric Allender

Start Date: 06 Nov 2020;
Start Time: 02:00PM -
Title: Developing and Employing Situational Awareness in Autonomous Robotic Systems

Bio:

Philip Dames is an Assistant Professor of Mechanical Engineering at Temple University, where he directs the Temple Robotics and Artificial Intelligence Lab (TRAIL). Prior to joining Temple, he was a Postdoctoral Researcher in Electrical and Systems Engineering at the University of Pennsylvania. He received his PhD Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 2015 and his BS and MS degrees in Mechanical Engineering from Northwestern University in 2010.

Speaker:
Abstract: Robotic systems must possess sufficient situational awareness in order to successfully operate in complex and dynamic real-world environments, meaning they must be able to perceive objects in their surroundings, comprehend their meaning, and predict the future state of the environment. In this talk, I will first describe how multi-target tracking (MTT) algorithms can provide mobile robots with this awareness, including our recent results that extend classical MTT approaches to include semantic object labels. Next, I will discuss two key applications of MTT to mobile robotics. The first problem is autonomous navigation through crowded, dynamic environments. To solve this, we develop a novel neural network-based controller that takes as its input the target tracks from an MTT, unlike previous approaches which only rely on raw sensor data. The second problem is distributed target search and tracking. To solve this, we develop a distributed MTT framework, allowing robots to estimate, in real time, the relative importance of each portion of the environment, and dynamic tessellation schemes, which account for uncertainty in the pose of each robot, provide collision avoidance, and automatically balance task assignment in a heterogeneous team.
Location: Via Zoom
Committee:
Start Date: 10 Nov 2020;
Start Time: 10:30AM -
Title: Bayesian Deep Learning: A Probabilistic Framework to Unify Deep Learning and Graphical Models

Bio:

Hao Wang is currently an Assistant Professor in the Department of Computer Science at Rutgers University. Previously he was a Postdoctoral Associate at the Computer Science & Artificial Intelligence Lab (CSAIL) of MIT, working with Dina Katabi and Tommi Jaakkola. He received his PhD degree from the Hong Kong University of Science and Technology, as the sole recipient of the School of Engineering PhD Research Excellence Award in 2017. He has been a visiting researcher in the Machine Learning Department of Carnegie Mellon University. His research focuses on statistical machine learning, deep learning, and data mining, with broad applications on recommender systems, healthcare, user profiling, social network analysis, text mining, etc. In 2015, he was awarded the Microsoft Fellowship in Asia and the Baidu Research Fellowship for his innovation on Bayesian deep learning and its applications on data mining and social network analysis.

Speaker:
Abstract: While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning, and planning require an even higher level of intelligence.\A0 The past few years have seen major advances in many perception tasks using deep learning models. In terms of higher-level inference, however, probabilistic graphical models, with their ability to expressively describe properties of variables and various probabilistic relations among variables, are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, we have been exploring along a research direction, which we call Bayesian deep learning, to tightly integrate deep learning and Bayesian models within a principled probabilistic framework. In this talk, I will present the proposed unified framework and some of our recent work on Bayesian deep learning with various applications including recommendation, social network analysis, healthcare, and representation learning.
Location: Via Zoom Recording
Committee:
Start Date: 17 Nov 2020;
Start Time: 02:30PM - 04:00PM
Title: Multimodal Content Understanding via Structural Reasoning

Bio:
Speaker:
Abstract: Vision-language tasks require a model to understand input multimodal contents and then conduct a specific goal (such as answer selection, text generation, or span prediction), which poses challenging multimodal representation learning and reasoning scenarios. Many existing works on vision-language tasks simply represent multimodal inputs as a sequence of embeddings (objects in an image or tokens in a sentence). However, such representation ignores the intrinsic structures such as relations or hierarchies of the multimodal contents. We propose to leverage the structural cues to build graph representation for multimodal contents and further reason over it through multimodal graph attention networks. We present experiments on two vision-language tasks to show the effectiveness of incorporating structural representation and reasoning for understanding multimodal contents: 1) The first task is audio-visual scene aware dialog which requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. 2) The second task is document visual question answering which requires the ability of understanding layout in document images to locate the correct answer span for given questions.
Location: Remote via Webex
Committee:

Prof. Yongfeng Zhang (Advisor), Prof. Hao Wang, Prof. Sungjin Ahn, Prof. Dong Deng

Start Date: 24 Nov 2020;
Start Time: 10:00AM - 12:00PM
Title: Cross-Domain User Embedding - Solving Cold-Start Recommendation with Edge Computing

Bio:
Speaker:
Abstract: When a user starts exploring within a recommendation system looking for items from new areas, cross-domain recommendation techniques come into help by transferring abundant knowledge from the user's acquainted'' domains. While this is usually achieved by sharing information between service providers on the clouds, we argue that it can be approached more naturally, efficiently, and effectively through learning on edge devices such as smartphones and laptops. In this work, we formalize the cross-domain recommendation problem under the mobile computing environment as compared to that in a centralized environment and identify two challenges to the community: unavailable direct transfer and heterogeneous domain-specific user representation. We then propose to learn and maintain cross-domain embedding on each user's personal device. The optimization follows a variational inference framework that maximizes the mutual information between cross-domain embeddings and domain-specific user information, and empirical study on real-world datasets exhibits its effectiveness on recommendation tasks and its superiority over domain-pairwise transfer models. We demonstrate that the resulting system has good scalability and allows flexible plugin of domain-specific encoder and decoders.
Location: Remote via Zoom
Committee:

Prof. Dmitris Metaxas

Prof. Yongfeng Zhang

Prof. Yipeng Huang

Start Date: 01 Dec 2020;
Start Time: 10:30AM -
Title: Explainable AI: From Human to Nature

Bio:

Yongfeng Zhang is an Assistant Professor in the Department of Computer Science at Rutgers University. His research interest is in Machine Learning and Data Mining, Information Retrieval and Recommender Systems, Economics of Data Science and Explainable AI. He is a Siebel Scholar of the class 2015, and a 2018 Best Professor Awardee by Rutgers CSGSS for Teaching and Mentoring. Recently, he has been working on various approaches to explainable AI, including but not limited to neural logical reasoning, knowledge graph reasoning, causal machine learning, explainable graph neural networks, as well as their application in human-centered and nature-oriented tasks. His research is generously supported by funds from Rutgers, NSF, Google, Adobe, and NVIDIA. He has been serving as PC or SPC members for top-tier computer science conferences as well as the Associate Editor for the ACM Transactions on Information Systems.

Speaker:
Abstract: AI has been an important part in many research areas or disciplines, spanning from human-centered tasks such as search, recommendation, dialog systems and social networks, to nature-oriented tasks such as drug discovery, bioscience and physics. However, how to understand and interpret the decisions or results produced by AI remains a significant problem, which greatly influences the trust between human and AI. In this talk, we will introduce our recent work on Explainable AI from both technical and application perspectives. On the technical perspective, we will introduce our work on explainable machine learning models for Explainable AI, including neural logic and neural symbolic reasoning, causal and counterfactual reasoning, knowledge graph reasoning, explainable graph neural networks and techniques for generating natural language explanations. On the application perspective, we will introduce our efforts on both human-centered and nature-oriented tasks, including search engine, recommender systems, dialog systems, molecular classification and biodiversity conservation based on our developed Explainable AI models.
Location:
Committee:
Start Date: 08 Dec 2020;
Start Time: 10:30AM -
Title: Unsupervising Vision via Object-Centric World Models

Bio:

Sungjin Ahn is an Assistant Professor of Computer Science at Rutgers University and directs the Rutgers Machine Learning (RUML) lab. He is also affiliated with Rutgers Center for Cognitive Science. He is interested in and have expertise in developing methods for probabilistic learning, deep learning, and their intersection. He is also particularly interested in developing an AI-agent that can discover underlying structure and representations of the world in an unsupervised manner and through interactions with the environment. He received Ph.D. at the University of California, Irvine with Max Welling and did a postdoc with Yoshua Bengio at Mila. Then, he joined Rutgers University in Fall 2018. He has co-organized ICML 2020 Workshop on Object-Oriented Learning and received the ICML best paper award in ICML 2012.

Speaker:
Abstract: Objects and their interactions are the foundational structure of the world that plays the central role in our perception, reasoning, and control of the world. Incorporating such structural knowledge is thus expected to resolve various limitations of current deep learning systems in reasoning, causality, modularity, and systematic generalization. However, current deep learning systems are limited in providing such structures: they either extensively rely on human annotations or uses unsupervised representations with a minimal uninterpretable structure. In this talk, I present recent advances in object-centric latent variable models. I first argue that this class of models provide a probabilistic modeling framework to learn interpretable, structured, and adaptable representations as well as the compositional imagination of multi-object scenes in an unsupervised manner. Also, I present the benefits of combining symbolic and distributed representations in these models, and an approach to learn three-dimensional scene representations in an object-centric manner. I conclude that human-like AI agents should understand the causal structured of the world and the object-centric representations can be the foundation of building such world models.
Location: Via Webex recording
Committee:
Start Date: 10 Dec 2020;
Start Time: 12:00PM - 02:00PM
Title: Computational Aspects of the Triangle Algorithm for Convex Hull Membership - a Universal Geometric Problem

Bio:
Speaker:
Abstract: The Convex Hull Membership (CHM) problem queries whether a point p in m-dimensional Euclidean space lies in the convex hull of a compact subset S in that Euclidean space. For S finite, CHM forms a basic problem in Computational Geometry and Linear Programming. It also serves as a query problem in many applications e.g. Topic Modeling, Non-negative Matrix Factorization. The Triangle Algorithm (TA) invented by Dr. Kalantari is a novel algorithm for CHM, first developed for the case where S is finite and subsequently for the more general case where S is any compact subset. The TA either computes an approximate solution or proves p lies outside of conv(S) by computing a separating hyperplane. The number of iterations of TA to solve CHM is independent of the dimension m and it thus can serve as an efficient oracle in large scale and high dimensional cases of CHM. In this dissertation we mainly investigate the computational aspects of solving three problems via the Triangle Algorithm, as stated below: 1) Irredundancy problem: We investigate the problem of computing all or a good approximation of vertices of a convex hull given a set of n points in dimension m, known as the irredundancy problem. This problem becomes challenging as m grows, especially for classical algorithms such as Gift Wrapping and QuickHull due to their exponential running times in terms of the dimension. The All Vertex Triangle Algorithm (AVTA) is proposed to tackle the efficiency issue for the irredundancy problem and its variations. The AVTA leverages on the TA as a membership oracle and progressively recovers vertices of convex hull of S, potentially after the Johnson-Lindenstrauss projection pre-processing. We apply AVTA to design new practical algorithms for two popular machine learning problems: topic modeling and non-negative matrix factorization. Our experimental results show its robustness and efficiency in solving the irredundancy problem and its variations in real world applications. 2) Spherical-CHM: We investigate Spherical-CHM as a special case of CHM where the n points of S lie on a sphere centered at p. The Spherical-CHM is shown to be equivalent to the general case in the sense that the general CHM is reducible to Spherical-CHM. Based on the structure of Spherical-CHM, we propose a novel version of the TA called the Spherical Triangle Algorithm(Spherical-TA) which converts CHM into Spherical-CHM and then applying the TA. Under mild assumptions, it is provably faster than TA. Empirically, we observe that it has better efficiency than TA. We list several applications of the Spherical-TA and empirically show the efficiency of the Spherical-TA as an oracle for CHM query in solving various problems. 3) Solving Linear System: We consider solving a linear system Ax=b, where A is an m by n real or complex matrix of arbitrary rank. We describe a novel geometric algorithm for computing an approximate solution based on Kalantari’s Triangle Algorithm for general CHM, where the underlying set S is a compact subset of a Euclidean space. The corresponding Triangle Algorithm is applicable to linear systems of arbitrary dimension, requiring no assumption on the matrix A. When A is invertible, the algorithm approximates the unique solution. When A is not invertible but Ax=b is solvable, it is able to approximate the solution with smallest norm, and when Ax=b has no solution, it is able to approximate the solution that minimizes ||Ax – b|| with smallest norm and computes a certificate proving unsolvability. Empirically we compare the algorithm with two popular linear system solvers BiCGSTAB and GMRES with iLU-preconditioner. We consider the case where A is a square matrix and observe superior efficiency of our proposed algorithm in approximating the solution or proving unsolvability.
Location: Remote via Webex
Committee:

Prof. Bahman Kalantari (Chair)

Prof. Abdeslam Boularias

Prof. Min Xu (Outside Member, Statistics Department, Rutgers)

Start Date: 11 Dec 2020;
Start Time: 05:30PM - 07:00PM
Title: AUTOMATED MACHINE LEARNING FOR SUPERVISED AND UNSUPERVISED MODELS WITH ARTIFICIAL NEURAL NETWORKS

Bio:
Speaker:
Abstract: Artificial Neural Networks (ANNs) are powerful machine learning tools to find and apply patterns for intelligent decision making. These tools can be combined with automation to select few results among many trials. Since ANNs are used for both supervised and unsupervised learning, automation can lead to more trusted learning methods across many fields and lead to exploring possibilities that are considered impossible with current technology. In this thesis, at first, I introduce a new form of ANN architecture which is used exclusively for automated robot navigation. By doing so, I provide a high-level overview of both computational neuroscience and the potential of automation. Next, I introduce Greedy AutoAugment to automate the learning of state-of-the-art neural networks for both big and small datasets. I also create an efficient model to evaluate clustering in unsupervised learning. The model is further expanded to introduce unsupervised learning for deep subspace clustering. In the end, I provide discussion and the future research plan for automating ANNs in machine learning applications.
Location: Remote via Webex
Committee:

Prof. Ahmed Elgammal

Prof. Abdeslam Boularias

Prof. Wei Vivian Li (External member)

Start Date: 15 Dec 2020;
Start Time: 10:00AM -
Title: Algorithms for Massive Graphs

Bio:

Aaron Bernstein is an assistant professor at the University of Rutgers working on graph algorithms. He completed his PhD at Columbia University with Cliff Stein as his advisor.

Speaker:
Abstract: Many real-world graphs are too large to be processed by standard algorithms, as examining the entire input would be too slow, and the graph cannot fit in the memory of a single machine. In recent years, much of graph-algorithms research has focused on different models for effectively processing massive graphs. In this talk, I will present my own recent work in this area. I will focus on new approaches to matching and load balancing that lead to new results in many different models. I will also discuss how more efficient data structures for basic graph primitives such as shortest paths can be used to speed up existing algorithms.
Location: Via Webex recording
Committee:
Start Date: 16 Dec 2020;
Start Time: 11:00AM - 01:00PM
Title: Performance Profilers and Debugging Tools for OpenMP Applications

Bio:
Speaker:
Location: Remote via Zoom
Committee:

Prof. Santosh Nagarakatte (Chair)

Prof. Srinivas Narayana

Prof. Martha Kim (Columbia University)

Start Date: 16 Dec 2020;
Start Time: 01:00PM - 03:00PM
Title: Integrating Accelerators

Bio:
Speaker:
Location: Remote via Webex
Committee:

Prof. Thu Nguyen

Prof. Ulrich Kremer

Olivier Giroux (nVidia)

Start Date: 18 Dec 2020;
Start Time: 10:00AM - 12:00PM
Title: Ubiquitous Precise Tracking: From Activity Detection over Indoor Tracking, to Outdoor Vehicle Positioning

Bio:
Speaker:
Abstract: Context awareness and tracking have changed our daily activities and style of living over the last three decades. Many applications have fueled research and industry efforts to establish accurate, energy-efficient, yet scalable and easy to deploy tracking and sensing systems. One of these applications is indoor and outdoor energy saving. For example, precise room occupancy estimation and activity sensing enable better control of indoor amenities such as light, heating, and air conditioning. These indoor applications can be extended to outdoor use for smart cities, e.g., automatic decrease of lighting for empty streets. However, current tracking systems can still not meet the aforementioned stringent requirements, and as a result there are no real world deployments of such applications. For example, conventional wireless tracking relies on WiFi received signal strength, and more recently on channel state information (CSI) offering decimeter level tracking accuracy. However, these tracking systems require either extensive fingerprints collection (wardriving), the knowledge of anchor locations and/or require expensive hardware that prevents wide deployment of such systems. Therefore, these applications with their sensing requirements still demand more accurate and scalable solutions. This thesis focuses on developing wireless tracking solutions targeting submeter accuracy indoors and meter-level accuracy outdoors by leveraging unconventional wireless signals including visible light and WiFi Fine Time Measurements (FTM). These tracking algorithms can adaptively learn and simultaneously map the environment/anchors while tracking users. The goal of this research is to propose tracking and context aware sensing systems that can tweak their parameters and map the environment through crowd-sourcing without the need of offline training. In particular, the proposed solutions include: (i) EyeLight, a device-free sensing system based on visible light to enable accurate tracking indoors and provide occupancy estimation, room activity recognition services; This system integrates photosensors with light bulbs easing its deployment compared to existing systems requiring the deployment of photosensors on the floor, (ii) an open platform for experimenting with WiFi fine time measurements and a general, repeatable, and accurate measurement framework for evaluating time-based ranging systems, (iii) Wi-Go, a system that simultaneously tracks vehicles and maps WiFi access point positions by fusing WiFi FTMs, GPS, and odometry information. We believe these three systems enable energy-efficient, continuous, precise and easy to deploy indoor and outdoor tracking and context awareness sensing solutions.
Location: Remote via Zoom
Committee:

Prof. Dimitri Metaxas

Prof. Richard Martin

Prof. Romit Roy Choudhury (UIUC)

Start Date: 18 Dec 2020;
Start Time: 01:00PM - 03:00PM
Title: Deep Learning based NAS Score and Fibrosis Stage Prediction from CT data

Bio:
Speaker:
Abstract: Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. Without diagnosis at the right time, NAFLD can lead to non-alcoholic steatohepatitis (NASH) and subsequent liver damage. The diagnosis and treatment of NAFLD depend on the NAFLD activity score (NAS) and the liver fibrosis stage, which are usually evaluated from liver biopsies by pathologists. We propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy. We also present a method to combine the information from CT and H\&E stained pathology data to improve the performance of NAS score and fibrosis stage prediction, when both types of data are available. However, the availability of a large annotated dataset cannot be always ensured and there can be domain shifts when using transfer learning. So, we propose a self-supervised learning method to address both problems. As the NAFLD causes changes in the liver texture, we also propose to use texture encoded inputs to improve the performance of the model.
Location: Remote via Webex
Committee:

Prof. Sungjin Ahn

Prof. Aaron Bernstein

Start Date: 22 Dec 2020;
Start Time: 12:00PM - 02:00PM
Title: Graph-Representation Learning for Human-Centered Analysis of Building Layouts

Bio:
Speaker:
Abstract: Floorplans are a standard representation of building layouts. Computer-Aided Design (CAD) applications and existing Building Information Modeling tools rely on simple floorplan representations which are not amenable to automation (e.g., generative design), and do not account for how people inhabit and occupy the space -- two key challenges which must be addressed for intelligent human-aware building design. This thesis addresses these challenges by exploring the use of graph representation learning techniques to implicitly encode the latent state of floorplan configurations, which is more amenable to automation. Specifically, we use graphs as intermediate representation of floorplans. Rooms are nodes and edges indicate a connection between adjacent rooms, either through a door or passageway. The graphs are annotated with a variety of attributes which characterize the semantic, geometric, and dynamic properties of the floorplan with respect to human-centered criteria. To address the variation in dimensionality of graphs, we utilize an intermediate sequential representation (generated by random walks) which allow us to encode the graphical structure in a fixed-dimensional representation. We propose the use of RNN-based variational autoencoder architectures to embed attribute floorplans. We enhance graph-based representations of floorplans with human occupancy and behavior attributes which are extracted by statically analysing the floorplan geometry, and by running human behavior simulations on large datasets of real and procedurally generated synthetic floorplans. We explore the potential of these representations on a variety of tasks including finding semantically similar floorplans, floorplan optimization, and generative design. Our approach and techniques are extensively evaluated through a series of quantitative experiments, and user studies with expert architects to validate our findings. The qualitative, quantitative and user-study evaluations show that our embedding framework produces meaningful and accurate vector representations for floorplans. Our models and associated datasets have been made publicly available to encourage adoption and spark future research in the burgeoning research area of intelligent human-aware building design.
Location: Remote via Webex
Committee:

Prof. Mridul Aanjaneya

Prof. Gerard de Melo

Prof. M. Brandon Haworth

Start Date: 11 Jan 2021;
Start Time: 10:30AM - 11:30AM
Title: Overview of NSF CISE: Vision and Investments in Research, Education, Workforce and Infrastructure

Bio:

Margaret Martonosi is the US National Science Foundation’s (NSF) Assistant
Director for Computer and Information Science and Engineering (CISE). With an
annual budget of more than $1B, the CISE directorate at NSF has the mission to uphold the Nation’s leadership in scientific discovery and engineering innovation through its support of fundamental research and education in computer and information science and engineering as well as transformative advances in research cyberinfrastructure. While at NSF, Dr. Martonosi is on leave from and retains her tenure at Princeton University where she is the Hugh Trumbull Adams '35 Professor of Computer Science. Dr. Martonosi's research interests are in computer architecture and hardware-software interface issues in both classical and quantum computing systems. Dr. Martonosi is an elected member of the American Academy of Arts and Sciences, and a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). Speaker: Abstract: The National Science Foundation (NSF) supports a majority of US academic research in the Computer and Information Science and Engineering (CISE) topic areas. Since February, 2020, Dr. Margaret Martonosi serves as NSF CISE AD, stewarding the CISE directorate’s$1B annual budget on behalf of research, education, workforce and infrastructure funding in CISE topic areas and for science as a whole. Martonosi’s talk will present an overview of CISE investments including both technical visions for the field, as well as thoughts on priorities for advancing that vision. Of particular importance are the people issues: Who will we engage in advancing CISE into the future? Martonosi will discuss CISE and NSF investments in broadening participation in computing, from K-12 to senior career stages. Please join us for this highly-interactive session and please bring your input and questions!
Location: Via Zoom
Committee:
Start Date: 04 Feb 2021;
Start Time: 10:30PM - 11:45PM
Title: Recent Lower Bounds in Algebraic Complexity Theory

Bio:

Ben Lee Volk is a postdoctoral researcher at UT Austin. Previously, he was a postdoctoral scholar at Caltech. He obtained his Ph.D. from Tel Aviv University and his M.Sc. from the Technion, both under the supervision of Amir Shpilka. His research interests include complexity theory, and in particular the study of algebraic models of computation, as well as neighboring areas such as pseudorandomness, coding theory and combinatorics.

Speaker:
Abstract: Algebraic Complexity Theory studies the complexity of solving algebraic computational tasks using algebraic models of computation. One major problem in this area is to prove lower bounds on the number of arithmetic operations required for computing explicit polynomials. This natural mathematical problem is the algebraic analog of the P vs. NP problem. It also has tight connections to other classical mathematical areas and to fundamental questions in complexity theory. In this talk I will provide background and then present some recent progress in this line of study, particularly in the area of proving lower bounds for computing linear transformations.
Location: Via Zoom Recording
Committee:
Start Date: 08 Feb 2021;
Start Time: 10:30AM -
Title: Communication Complexity, Quantum Computing and Optimization: New Connections and Insights

Bio:

Makrand Sinha is currently a postdoctoral researcher at CWI in Amsterdam hosted by Nikhil Bansal, Ronald de Wolf and Monique Laurent. Prior to that, he obtained his Ph.D. in Computer Science from the University of Washington, Seattle in 2018, under the guidance of Anup Rao. His research interests lie in quantum computing, communication complexity and optimization, and in particular in making connections between these areas.

Speaker:
Abstract: How much information flows through a system? This fundamental question is at the heart of communication complexity. Techniques from this field have turned out to be immensely powerful and fairly universal tools to understand the power of different kinds of algorithms. In this talk, I will describe new methods that I have developed to analyze communication which offer fresh insights into quantum computing and optimization. Using these techniques, I will answer a question of Aaronson and Ambainis regarding the maximal advantage that quantum algorithms can have over classical algorithms in the "blackbox" model, and another conjecture due to Rothvoss about optimal linear programs for approximately solving the matching problem. Looking forward, I also propose new directions to explore more connections among these areas with the intention of answering important questions regarding whether quantum speedups need structure, and questions regarding the power of more general optimization approaches, such as semidefinite programming.
Location: Via Zoom
Committee:
Start Date: 09 Feb 2021;
Start Time: 10:30AM -
Title: New Arenas in Hardness of Approximation

Bio:

Karthik is currently a Postdoctoral Fellow at the Courant Institute of Mathematical Sciences, New York University, hosted by Subhash Khot. He received his Ph.D. in 2019 from Weizmann Institute of Science where he was advised by Irit Dinur. Prior to that, he obtained his M.S. from Ecole Normale Superieure in Lyon. His main research interests are in the intersection of complexity theory and combinatorial geometry.

Speaker:
Abstract: In this talk, I will introduce the mathematically-rich area of hardness of approximation through the recent inapproximability results for selected geometric problems whose difficulty ranges from being in P (e.g., closest pair problem) to being NP-hard (e.g., k-means clustering problem). The closest pair problem is a fundamental problem in computational geometry where given a set of n points in high dimensions (say log n dimensions) as input, we are required to find the closest pair of points. This problem can be solved in a straightforward manner in roughly O(n^2) time (by computing all pairwise distances), but can it be solved faster, in say O(n^1.9) time? If not, is there at least a fast algorithm that finds a pair of points that are close to being the nearest? An important task in unsupervised learning is k-means clustering. In this problem, given a set of n points and an integer k, we are asked to find a partition of the point-set into k clusters that minimizes the k-means objective. This problem is NP-hard and thus believed to not admit any polynomial (in n) time algorithm. But is there an algorithm running in polynomial time that can find k clusters whose k-means objective is only a 2 factor away from an optimal clustering solution? In this talk, I will present the recent advances on the above long standing questions and also briefly sketch the landscape of the complexity of geometric problems and the important open questions that need to be addressed therein. I will also highlight the symbiosis in the ideas needed to answer the aforementioned questions on closest pair and clustering problems, which might seem a-priori very different.
Location: Via Zoom Recording
Committee:
Start Date: 11 Feb 2021;
Start Time: 10:30AM -
Title: Algorithms and Barriers for Fast Matrix Multiplication

Bio:

Josh Alman is a Michael O. Rabin postdoctoral fellow in theoretical computer science at Harvard University. He earned his Ph.D. in computer science from MIT in 2019, where he was advised by Virginia Vassilevska Williams and Ryan Williams. He works on both algorithm design and complexity theory, and much of his work combines tools and insights from both areas. He's particularly interested in developing algebraic tools like algorithms for matrix multiplication and Fourier transforms, and applying them to problems throughout computer science. His awards include the European Association of TCS Distinguished Dissertation Award, the George M. Sprowls Award for outstanding Ph.D. theses in computer science at MIT, and best student paper awards at CCC 2019 and FOCS 2019.

Speaker:
Abstract: Matrix multiplication is one of the most basic algebraic operations. Since Strassen's surprising breakthrough algorithm from 1969, which showed that matrices can be multiplied faster than the most straightforward algorithm, algorithmic problems from nearly every area of computer science have been sped up by clever reductions to matrix multiplication. It is popularly conjectured that two n × n matrices can be multiplied in roughly O(n^2) time, but we don't yet have the algorithmic techniques needed to achieve this. In this talk, I'll give an overview of my work on this important problem, in the broad context of my research on combining ideas from algorithm design and complexity theory to help understand problems throughout computer science. First, I'll give an overview of known applications of matrix multiplication, including some new applications to nearest neighbor search and processing data streams. Second, I'll talk about the fastest known algorithm for matrix multiplication, which runs in time O(n^2.37286), which I recently designed with Virginia Vassilevska Williams. Third, I'll describe new barrier results which help to explain why we've been stuck for so long on this problem, and describe what kinds of insights are needed for further improvements.
Location: Via Zoom Recording
Committee:
Start Date: 18 Feb 2021;
Start Time: 10:30AM -
Title: Efficient Algorithms and Hardness for Fundamental Problems in Data Science

Bio:

Peng Zhang is a Postdoctoral Associate in Computer Science at Yale University, under the supervision of Daniel Spielman. She obtained her Ph.D. from Georgia Tech, advised by Richard Peng. Her research lies broadly in designing and understanding the limits of fast algorithms. She has worked on structured linear equations and linear programs, discrepancy theory and its application in the design of randomized controlled trials. Peng’s work received the best student paper award at FOCS 2017, and Georgia Tech College of Computing Dissertation Award in 2019.

Speaker:
Abstract: Two important components of data science are data and computation. Sometimes, data is cheap but computation is expensive. Sometimes, data can be expensive (for example, when data are collected from experiments with human subjects). In this talk, I will discuss efficient algorithms and hardness addressing fundamental problems in computation and data. In the first part of the talk, I will present hardness and fast algorithms for solving structured linear equations and linear programs, which are central tools in data analysis. Solving arbitrary linear equations and linear programs can be time-consuming. However, many linear equations and linear programs arising commonly in practice have additional structures that enable faster solvers. One example is Laplacian linear equations, which can be solved in nearly linear time (Spielman-Teng, 2004). I will show that a slight generalization of Laplacian linear equations is as hard to solve as arbitrary linear equations. But when we know additional geometric structures about this slightly general class of linear equations, we can design asymptotically faster solvers. Then, I will briefly discuss hardness and fast algorithms for positive linear programs, another example showing that structures enable faster solvers. In the second part of the talk, I will present an algorithm that improves the design of randomized controlled trials, which are widely used to test the effectiveness of interventions. In a trial, we randomly partition all subjects to a treatment group and a control group. We want the two groups to be similar in covariates — features that we know about the subjects. By exploiting ideas from discrepancy theory, we obtain random partitions with a nearly optimal tradeoff between the gain we have when covariates are correlated with treatment outcomes and the loss we suffer when they are not.
Location: Via Zoom Recording
Committee:
Start Date: 19 Feb 2021;
Start Time: 10:00AM - 11:00AM
Title: Throwing a Sofa Through the Window

Bio:

Dan Halperin is a professor of Computer Science at Tel Aviv University. His main field of research is Computational Geometry and Its Applications. A major focus of his work has been in research and development of robust geometric algorithms, principally as part of the CGAL project and library. The application areas he is interested in include robotics, automated
manufacturing, algorithmic motion planning and 3D printing. Halperin is an IEEE Fellow and an ACM Fellow. http://acg.cs.tau.ac.il/danhalperin

Speaker:
Abstract: Planning motion for robots and other artifacts toward desired goal positions while avoiding obstacles on the way becomes harder when the environment is tight or densely cluttered. Indeed, prevalent motion-planning techniques often fail in such settings. The talk centers on recently-developed efficient algorithms to cope with motion in tight quarters. We study several variants of the problem of moving a convex polytope in three dimensions through a rectangular (and sometimes more general) window. Specifically, we study variants in which the motion is restricted to translations only, discuss situations in which such a motion can be reduced to sliding (translation in a fixed direction) and present efficient algorithms for those variants. We show cases where sliding is insufficient but purely transnational motion works, or where purely transnational motion is insufficient and rotation must be included. Finally, we explore the general setup, where we want to plan a general motion (with all six degrees of freedom) for the polytope through the window and present an efficient algorithm for this problem, with running time close to O(n^4), where n is the number of edges of the polytope. (Joint work with Micha Sharir and Itay Yehuda.) As time permits I will present additional recent results for motion in tight settings in assembly planning, fixture design, and casting and molding.
Location: Via Webex
Committee:
Start Date: 22 Feb 2021;
Start Time: 10:30AM - 11:45AM
Title: Mechanism Design for Social Good

Bio:

Kira Goldner is a postdoctoral researcher in the Computer Science Department and at the Data Science Institute at Columbia University, hosted by Tim Roughgarden, and supported by NSF Math Sciences and Data Science Institute fellowships.  Kira uses her background in the foundations of mechanism design to address societal problems, e.g., in healthcare, climate change, and privacy.  She has also worked on core problems concerning revenue maximization, simplicity, and robustness.  As part of this agenda, Kira co-founded Mechanism Design for Social Good (MD4SG), an interdisciplinary initiative working to improve access to opportunity for historically disadvantaged communities.  She received her PhD in computer science and engineering from the University of Washington under the advisement of Anna Karlin, and was supported by a Microsoft Research PhD Fellowship and a Google Anita Borg Scholarship.  She has received many awards for her work, including the EC 2019 Best Paper with a Student Lead Author Award and the EC 2020 Best Presentation by a Student or Postdoctoral Researcher Award.

Speaker:
Abstract: Society is run by algorithms, and in many cases, these algorithms interact with autonomous agents who have a stake in the outcome. These participants may behave strategically in an attempt to "game the system," resulting in unexpected or suboptimal outcomes. In order to accurately predict an algorithm's outcome and quality, we must design it to be robust to strategic manipulation. This is the subject of algorithmic mechanism design, which borrows ideas from game theory and economics to design robust algorithms. In this talk, I will show how results from the theoretical foundations of algorithmic mechanism design can be used to solve problems of societal concern. I will focus on applications in carbon license allocations, health insurance markets, and online labor markets.
Location: Via Zoom
Committee:
Start Date: 22 Feb 2021;
Start Time: 02:30PM - 04:00PM
Title: Human-Centered Intelligent Sensing Systems

Bio:

Jorge Ortiz is an Assistant Professor at Rutgers University where he directs the Cyber-Physical Intelligence Lab (CyPhy-Lab) and is where he is also a member of the Wireless Information Network Laboratory (WINLAB). His work focuses on building and studying sensing systems that learn about human behavior, from human feedback, and with humans to improve system objectives and enhance people’s lives. These include a broad range of sensing systems including smart objects, smart built environments, and smart cities, more broadly. Prior to joining Rutgers in 2018, he was a Research Staff Member at IBM Research working on machine learning and the internet of things. In the five years he was at IBM, he attained 12 patents and published in multiple top academic conferences, journals, and books and was awarded ‘Best Poster’ at IEEE/ACM IPSN '08, two ‘Best Paper Runner-ups’ at Buildsys '15, ‘Best Paper’ at ICISSP '18, and ‘Best Paper Runner-up’
at IoTDI '19. At IBM he led teams to commercialize two major research projects and bring them to market. Dr. Ortiz also has extensive industry experience, which includes several years at Oracle Corporation and has worked at and led multiple startups. He is currently serving as co-TPC chair for Buildsys 2020. Dr. Ortiz attained his Ph.D. in Computer Science from UC Berkeley in 2013, M.S.
in Computer Science from Berkeley in 2010, and a BS in Computer Science from MIT in 2003.

Speaker:
Abstract: This talk is about the design of systems and algorithms for sensing systems that interact directly with humans. I will discuss our work in the Cyber Physical Intelligence lab (CyPhy-Lab) at Rutgers University where we study sensing systems that learn about human behavior, from human feedback, and with humans to improve system objectives and enhance people’s lives. I will describe three on-going projects that explore these themes more precisely. First I will describe PillSense, a smart pillbox system for medication adherence. This system helps us learn about how humans take their medication and how physical design is tightly coupled to the system’s ability to identify users effectively. We describe two pill box designs and associated algorithms to address the challenges posed. Then, I will discuss our on-going project Maestro, a system that learns from humans to fill the semantic gap between sensor measurements and their interpretation, in order to facilitate the construction of smart ambient-sensing applications in buildings. Finally, I will discuss project Paz, a system that attempts to learn when to interact with humans as we work to facilitate agent-human collaboration to both attain system objectives (i.e. efficiency) and enhance human productivity, comfort, and entertainment.
Location: Via Zoom
Committee:
Start Date: 23 Feb 2021;
Start Time: 10:30AM -
Title: Proof Complexity and Applications

Bio:

Marc Vinyals is a visiting fellow at the Technion. He works on
computational complexity, and proof complexity in particular, hosted
by Yuval Filmus. His research interests include other areas of
computational complexity such as circuit complexity, communication
complexity, query complexity, and pebble games, as well as the theory
of satisfiability solving. Previously he was a graduate student at the
KTH Royal Institute of Technology, supervised by Jakob Nordström, and
a visiting fellow at the Tata Institute of Fundamental Research,

Speaker:
Abstract: Even though the origins of proof complexity are intimately tied to the P vs NP question, nowadays a significant part of the appeal of research in this area is that new results readily apply to large classes of algorithms. In this talk we will discuss how recent developments in proof complexity are impacting the design of satisfiability solvers, how fine-grained mathematical models can help us smooth over discrepancies between theory and practice, and how questions arising from implementation details end up as meaningful mathematical problems with connections to other topics in computational complexity.
Location: Via Zoom
Committee:
Start Date: 24 Feb 2021;
Start Time: 02:00PM - 04:00PM
Title: GreenDRL: Managing Green Data Centers Using Deep Reinforcement Learning

Bio:
Speaker:
Abstract: Managing data centers to maximize efficiency and sustainability is a complex and challenging problem. In this work, we explore the use of deep reinforcement learning (RL) to manage “green” data centers, bringing a robust approach to optimizing management systems for specific workload and datacenter characteristics. Specifically, we consider data centers that are partially powered by on-site generation of renewable energy and partially cooled by “free-cooling.” We design and evaluate GreenDRL, a hybrid system that combines a deep RL agent and a simple dispatcher to schedule a workload while jointly managing server power consumption and the cooling system to minimize cost. Our design addresses several important challenges, including scalability, robustness ,and effective learning in an environment comprising an enormous state/action space and multiple stochastic processes. Evaluation results show that GreenDRL is able to learn important principles such as delaying deferrable jobs to leverage variable generation of renewable (solar) energy , and avoiding the use of power-intensive cooling settings even at the expense of leaving some renewable energy unused. Simulation of a realistic (small) green data center running a workload containing a fraction of jobs deferrable by up to 12 hours shows that GreenDRL can reduce grid electricity by 26–57% compared to a baseline system, depending on outside temperatures and availability of renewable energy. Overall, our work shows that deep RL is a promising technique for building efficient management systems for green data centers.
Location: Remote via Zoom
Committee:

Ulrich Kremer

He Zhu

Karl Stratos

Start Date: 25 Feb 2021;
Start Time: 02:00PM - 04:00PM
Title: Automatic, Efficient, and Robust Deep Learning

Bio:
Speaker:
Location: Remote via Zoom
Committee:

Prof. Hao Wang

Prof. Sungjin Ahn

Prof. Xiaolei Huang (External member)

Start Date: 09 Mar 2021;
Start Time: 03:00PM - 05:00PM
Title: Deep Learning-based Histopathology Image Analysis for Cancer Diagnosis and Treatment

Bio:
Speaker:
Abstract: Histopathology plays a vital role in cancer diagnosis, prognosis, and treatment decisions. The whole slide imaging technique that captures the entire slide as a digital image (whole slide image, WSI) allows the pathologists to view the slides digitally as opposed to what was traditionally viewed under a microscope. With the development of computational power and image analysis algorithms, computational methods have been developed for the quantitative and objective analyses of histopathology images, which can reduce the intensive labor and improve the efficiency for pathologists compared with manual examinations. In this dissertation, we focus on deep learning-based solutions in histopathology image analysis for cancer diagnosis and treatment, specifically, nuclei segmentation for cancer diagnosis, and gene mutation prediction for cancer treatment. Nuclei segmentation is a critical step in the automatic analysis of histopathology images. We focus on two directions to tackle the problems in the deep learning-based nuclei segmentation task. One is the annotation-efficient algorithms. As the fully supervised learning of deep neural networks requires a large amount of training data, a weakly supervised nuclei segmentation framework based on a portion of nuclear locations is proposed to alleviate the annotation effort of pathologists. This method achieves comparable performance as the fully supervised methods and about 60$\times$ speed-up (10\% points) in the annotation time. The other direction is to improve the instance segmentation performance. The networks and cross entropy loss in current deep learning-based segmentation methods originate from image classification tasks and have drawbacks for segmentation. Therefore, we propose a full resolution convolutional neural network (FullNet) and a variance constrained cross entropy (varCE) loss to improve the fully supervised segmentation performance. Except for the cell-level heterogeneity that is routinely used for cancer diagnosis, it remains unclear for many cancers that how tissue structures in histopathology slides are related to genomic features like gene alterations and expression patterns. We develop a deep learning model to predict the genetic mutations and biological pathway activities directly from histopathology slides in breast cancer. The weight maps of tumor tiles are visualized to understand the decision-making process of deep learning models. Our results provide new insights into the association between pathological image features, molecular outcomes and targeted therapies for breast cancer patients.
Location: Remote via Zoom
Committee: