# Events Feed

Start Date: 09 Oct 2019;
Start Time: 11:00AM -
Title: Convex Set Disjointness, Distributed Learning of Halfspace

Bio:
Speaker:

Shay Moran

Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 16 Oct 2019;
Start Time: 11:00AM -
Title: Nullstellensatz Size-Degree Trade-offs from Reversible Pebbling

Bio:
Speaker:

Rob Robere

Abstract:
Location: CoRE 431
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 23 Oct 2019;
Start Time: 11:00AM -
Title: The asymptotic spectrum of tensors and barriers for fast matrix multiplication

Bio:
Speaker:

Jeroen Zuiddam

Abstract:
Location: CoRE 431
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 30 Oct 2019;
Start Time: 11:00AM -
Title: An Improved Lower Bound for Sparse Reconstruction from Subsampled Hadamard Matrices

Bio:
Speaker:

Jarosław Błasiok

Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 06 Nov 2019;
Start Time: 09:45AM -
Title: On Rich 2-to-1 Games

Bio:
Speaker:

Dor Minzer

Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 06 Nov 2019;
Start Time: 11:00AM -
Title: Recent Advances in Stochastic Gradient Methods: From Convex to Non-convex Optimization and Deep Learning

Bio:

Mert Gürbüzbalaban is an assistant professor at Rutgers University. Previously, he was a postdoctoral associate at the Laboratory for Information and Decision Systems (LIDS) at MIT. He is broadly interested in optimization and computational science driven by applications in large-scale information and decision systems. He received his B.Sc. degrees in Electrical Engineering and Mathematics as a valedictorian from Boğaziçi University, Istanbul, Turkey, the “Diplôme d’ingénieur” degree from École Polytechnique, France, and the M.S. and Ph.D. degrees in (Applied) Mathematics from the Courant Institute of Mathematical Sciences, New York University.

Dr. Gürbüzbalaban received the Kurt Friedrichs Prize (given by the Courant Institute of New York University for an outstanding thesis) in 2013, Bronze Medal in the École Polytechnique Scientific Project Competition in 2006, the Nadir Orhan Bengisu Award (given by the electrical-electronics engineering department of Boğaziçi University to the best graduating undergraduate student) in 2005 and the Bülent Kerim Altay Award from the Electrical-Electronics Engineering Department of Middle East Technical University in 2001. He received funding from a variety of sources including multiple programs at the U.S. National Science Foundation. He is also a co-recipient of the Best Paper Runner-Up Award from the International Conference on Machine Learning, 2019.

Speaker:

Mert Gurbuzbalaban

Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 13 Nov 2019;
Start Time: 11:00AM -
Title: Optimal Data Acquisition for Statistical Estimation

Bio:
Speaker:

Juba Zian

Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 20 Nov 2019;
Start Time: 11:00AM -
Title: Online Vector Balancing and Geometric Discrepancy

Bio:
Speaker:

Sahil Singla

Abstract:
Location: CoRE 431
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 22 Nov 2019;
Start Time: 11:00AM -
Title: ACTION GRAMMARS: THE KEY TO AI AND ROBOTICS

Bio:

Yiannis Aloimonos is Professor of Computational Vision and Intelligence at the Department of Computer Science, University of Maryland, College Park, and the Director of the Computer Vision Laboratory at the Institute for Advanced Computer Studies (UMIACS). He is also affiliated with the Institute for Systems Research and the Neural and Cognitive Science Program. He was born in Sparta, Greece and studied Mathematics in Athens and Computer Science at the University of Rochester, NY (PhD 1990). He is interested in Active Perception and the modeling of vision as an active, dynamic process for real time robotic systems. For the past five years he has been working on bridging signals and symbols, specifically on the relationship of vision to reasoning, action and language.

Speaker:

Yiannis Aloimonos

Abstract:
Location: CoRE A 301
Committee:

Dimitris Metaxas

Start Date: 25 Nov 2019;
Start Time: 02:00PM -
Title: Learning Biomarkers from Biomedical Image Data

Bio:

Dr. Sharon Xiaolei Huang is currently an associate professor in the College of Information Sciences and Technology at the Pennsylvania State University, University Park, PA, USA. She is also an affiliated faculty member of Penn State’s Huck Institutes of the Life Sciences. Her research interests lie at the intersection of biomedical image analysis, machine learning, and computer vision. She has over 130 publications (including journal articles, book chapters, and refereed conference papers) and holds 7 patents. She is an associate editor for the Computer Vision and Image Understanding journal. She received her Bachelor’s degree in computer science from Tsinghua University, and her Master’s and doctoral degrees in computer science from Rutgers University. Her research has been funded by the NIH, NSF, the Howard Hughes Medical Institute, and the Pennsylvania state.

Speaker:

Dr. Sharon Xiaolei Huang

Abstract:
Location: CoRE A 301
Committee:

Dimitris Metaxas

Start Date: 02 Dec 2019;
Start Time: 04:00PM -
Title: Fast and Optimal Methods for Object Rearrangement and Multi-Robot Path Planning

Bio:
Speaker:

Shuai Han

Abstract:
Location: 1 Spring Street (CAC), Room 403
Committee:

Prof. Jingjin Yu (Chair), Prof. Kostas Bekris, Prof. Abdeslam Boularias, Prof. Sepehr Assadi

Start Date: 03 Dec 2019;
Start Time: 10:30AM -
Title: Learning Natural Language Semantics with Heterogeneous Supervision

Bio:

Gerard de Melo is an Assistant Professor in the Department of Computer Science at Rutgers University, where he heads the Deep Data Lab. Over the years, he has published around 150 papers on natural language processing, data science, and AI, being awarded Best Paper awards at CIKM 2010, ICGL 2008, and the NAACL 2015 Workshop on Vector Space Modeling, in addition to the WWW 2011 Best Demonstration Award, an ACL
2014 Best Paper Honorable Mention, a nomination for Best Student Paper at ESWC 2015, and a nomination for Best Paper at the IEEE Conference on Semantic Computing 2018. Prior to joining Rutgers, he was a faculty member at Tsinghua University and a Post-Doctoral Research Scholar at ICSI/UC Berkeley. He received his doctoral degree at the Max Planck Institute for Informatics.

Speaker:

Gerard de Melo

Abstract:
Location: CoRE A 301
Committee:

Matthew Stone

Start Date: 10 Dec 2019;
Start Time: 10:30AM -
Title: Scaling Up Computational Physics for Real World Applications

Bio:

Dr. Mridul Aanjaneya is an Assistant Professor in the Department of Computer Science at Rutgers University. Prior to joining Rutgers, he was a postdoctoral researcher in the Department of Computer Sciences at the University of Wisconsin - Madison. He obtained his Ph.D. in Computer Science from Stanford University. While at Stanford, he also worked as a consultant in the Spatial Technologies team at the Nokia Research Center for two years. Mridul's research lies at the intersection of Computer Graphics, Scientific and High-Performance Computing, and Computational Physics. More specifically, he is interested in the design of models, computational techniques, and robust numerical algorithms that can facilitate high-level tasks such as the use of simulations in biomechanics, computational imaging, or robotics. He is a recipient of the Ralph E. Powe Junior Faculty Enhancement Award 2019, sponsored by the Oak Ridge Associated Universities (ORAU).

Speaker: Mridul Aanjaneya
Abstract:
Location: CoRE A 301
Committee:

Matthew Stone

Start Date: 11 Dec 2019;
Start Time: 11:00AM -
Title: Seminar: Double-samplers and local-to-global list decoding

Bio:
Speaker:

Irit Dinur

Abstract: I will describe a recent work that uses so-called “double-samplers” for list decoding. Double samplers are multi-layered graphs that are derived from high dimensional expanders, and whose existence is quite non-trivial. The talk will be flexible depending on the audience preference I can expand on the coding application or on the double samplers themselves. Based on a joint work with Harsha, Livni, Kaufman and Ta-Shma
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 08 Jan 2020;
Start Time: 02:00PM -
Title: TRADING QUALITY FOR RESOURCE CONSUMPTION THROUGH APPROXIMATION MANAGEMENT

Bio:
Speaker:

Liu Liu

Abstract: The goal of traditional optimizations is to map applications onto limited machine resources such that application performance is maximized while application semantics (program correctness) is preserved. Semantics is thought of as a unique mapping from inputs to outcomes. Relaxing application semantics through approximations has the potential of orders of magnitude performance improvements by trading off outcome quality for resource usage. Here, an execution outcome is not only based on its inputs but also resource availability and user quality expectations. Emerging approximation techniques provides various ways to trade-off output quality for lower resource consumption. However, as a developer, the guidance and support on how to utilize the power of approximation in everyday applications are limited and rarely discussed in recent works. The offline training overhead to support approximation is usually huge, but often be treated as "free." Besides, it is surprising that end-users involvement is always overlooked when determining the quality notion, which should be highly subjective. Finally, supporting approximation in a multi-programming environment is crucial to let approximation be widely accepted as a general technique. In this dissertation, I introduce RAPIDS, Reconfiguration, Approximation, Preferences, Implementation, Dependencies, and Structure, a framework for developing and executing applications suitable for dynamic configuration management in approximate computation. The main contribution of RAPIDS is its design to address the above concerns through exploiting the different expertise/strengths of the three actors (developers, users, applications) involved. I conduct comprehensive experiments and show that RAPIDS is adaptive and extendable by providing customizable configuration spaces for developers and the support for customizable quality for end-users. It has low overheads and small cross-platform porting costs. I also introduce an extension of RAPIDS, RAPIDS-M (Rapids for Multi-programming), which is the first system that discusses cross-application approximation management. The target is to understand and overcome the challenges in approximation management fundamentally, then let both developers and end-users benefit from approximation with little extra efforts so that a wider audience can accept the technique.
Location: CoRE 305
Committee:

Defense Committee: Prof. Ulrich Kremer (Chair), Prof. Sibren Isaacman (Loyola University), Prof. Rich Martin, Prof. Desheng Zhang, and Dr. Linbin Yu (Facebook)

Start Date: 23 Jan 2020;
Start Time: 12:00PM -
Title: Pre-Defense: Programming and Managing Data-Driven Applications Between the Edge and the Cloud

Bio:
Speaker:

Eduard Gibert Renart

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: CoRE 305
Committee:

Manish Parashar (Advisor), Ulrich Kremer, Srinivas Narayana

Start Date: 31 Jan 2020;
Start Time: 03:00PM -
Title: PhD Defense: Efficient and Asymptotically Optimal Kinodynamic Motion Planning

Bio:
Speaker:

Zakary Littlefield

Abstract: This dissertation explores properties of motion planners that build tree data structures in a robot’s state space. Sampling-based tree planners are especially useful for planning for systems with significant dynamics, due to the inherent forward search that is performed. This is in contrast to roadmap planners that require a steering local planner in order to make a graph containing multiple possible paths. This dissertation explores a family of motion planners for systems with significant dynamics, where a steering local planner may be computationally expensive or may not exist. These planners focus on providing practical path quality guarantees without prohibitive computational costs. These planners can be considered successors of each other, in that each subsequent algorithm addresses some drawback of its predecessor. The first algorithm, Sparse-RRT, addresses a drawback of the RRT method by considering path quality during the tree construction process. Sparse-RRT is proven to be probabilistically complete under mild conditions for the first time here, albeit with a poor convergence rate. The second algorithm presented, SST, provides probabilistic completeness and asymptotic near-optimality properties that are provable, but at the cost of additional algorithmic overhead. SST is shown to improve the convergence rate compared to Sparse-RRT. The third algorithm, DIRT, incorporates learned lessons from these two algorithms and their shortcomings, incorporates task space heuristics to further improve runtime performance, and simplifies the parameters to more user-friendly ones. DIRT is also shown to be probabilistically complete and asymptotically near-optimal. Application areas explored using this family of algorithms include evaluation of distance functions for planning in belief space, manipulation in cluttered environments, and locomotion planning for an icosahedral tensegrity-based rover prototype that requires a physics engine to simulate its motions.
Location: 1 Spring Street (CAC), Room 403
Committee:

Kostas Bekris (chair), Jingjin Yu, Abdeslam Boularias, Tim Barfoot (University of Toronto)

Start Date: 04 Feb 2020;
Start Time: 01:00PM -
Title: Qualifying Exam: OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization

Bio:
Speaker:

Bingchen Liu

Abstract:
Location: CoRE 301
Committee:

Ahmed Elgammal (Advisor), Gerard De Melo, Yongfeng Zhang, He Zhu (outside member)

Start Date: 05 Feb 2020;
Start Time: 11:00AM - 12:00PM
Title: Scattering and Sparse Partitions, and their Applications

Bio:
Speaker:

Arnold Filtser (Columbia)

Abstract: A partition P of a weighted graph G is (sigma, tau, Delta)-sparse if every cluster has diameter at most Delta, and every ball of radius Delta/sigma intersects at most tau clusters. Similarly, P is (sigma, tau, Delta)-scattering if instead for balls we require that every shortest path of length at most Delta/sigma intersects at most tau clusters. Given a graph G that admits a (sigma, tau, Delta)-sparse partition for all Delta>0, Jia et al. [STOC05] constructed a solution for the Universal Steiner Tree problem (and also Universal TSP) with stretch O(tau sigma^2 log_tau n). Given a graph G that admits a (sigma, tau, Delta)-scattering partition for all Delta>0, we construct a solution for the Steiner Point Removal problem with stretch O( tau^3 sigma^3). We then construct sparse and scattering partitions for various different graph families, receiving new results for the Universal Steiner Tree and Steiner Point Removal problems.
Location: CoRE 301
Committee:
Start Date: 06 Feb 2020;
Start Time: 10:30AM -
Title: Fair representation in group decision-making

Bio:

Edith Elkind is a Professor at University of Oxford, where her work is supported by an ERC Starting Grant. She obtained her PhD from Princeton in 2005, and has held positions in UK, Israel and Singapore since then. Her interests are in algorithmic game theory and computational social choice, and she has published over 100 papers in top AI and algorithmic game theory venues on these topics.  She has served as a program chair of AAMAS'15 and ACM EC'18 and a general chair of AAMAS'19, and she was recently elected to be the program chair of IJCAI'23.

Speaker:

Edith Elkind, University of Oxford

Abstract: Suppose that a group of agents want to select k > 1 alternatives from a given set, and each agent indicates which of the alternatives are acceptable to her: the alternatives could be conference submissions, applicants for a scholarship or locations for a fast food chain. In this setting it is natural to require that the set of winners represents the voters fairly, in the sense that large groups of voters with similar preferences have at least some of their approved alternatives in the winning set. We describe several ways to formalize this idea; our axiomatization enables us to characterize a voting rule that was proposed by a Danish polymath Thiele in the XIXth century. However, this rule has an NP-hard winner determination algorithm; to overcome this issue we design an efficient approximation algorithm for this rule that satisfies the most demanding of our axioms.
Location: CoRE A 301
Committee:
Start Date: 07 Feb 2020;
Start Time: 10:00AM -
Title: Cooperation in Humans and Machines

Bio:
Speaker:

Patrick Shafto

Abstract: Cooperation, specifically cooperative information sharing, is a basic principle of human intelligence. Machine learning, in contrast, focuses on learning from randomly sampled data, which neither leverages others’ cooperation nor prioritizes the ability to communicate what has been learned. I will discuss ways in which our understanding of human learning may be leveraged to develop new machine learning, and form a foundation for improved integration of machine learning into human society.
Location: CoRE 431
Committee:
Start Date: 12 Feb 2020;
Start Time: 10:30AM -
Title: Towards Verifying AI Systems: Testing of Samplers

Bio:

Kuldeep Meel is an Assistant Professor of Computer Science in School of Computing at the National University of Singapore where he holds the Sung Kah Kay Assistant Professorship. He received his Ph.D. (2017) and M.S. (2014) degree from Rice University, and B. Tech. (with Honors) degree (2012) in Computer Science and Engineering from Indian Institute of Technology, Bombay. His research interests lie at the intersection of Artificial Intelligence and Formal Methods. He is a recipient of 2019 NRF Fellowship for AI.  His work received the 2018 Ralph Budd Award for Best PhD Thesis in Engineering, 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms and Best Student Paper Award at CP 2015.

Speaker:

Kuldeep Meel, National University of Singapore

Abstract: The modern AI systems significantly differ from traditional systems in their reliance on probabilistic reasoning. We focus on one core component of probabilistic reasoning: sampling from a discrete distribution specified by a probabilistic model. The widespread usage of heuristics in different sampling-based techniques creates a gap between theory and practice: In theory, heuristics would nullify guarantees while in practice the heuristics seem to work well for problems arising from real-world instances. Often statistical tests are employed to argue for the quality of distributions, but such statistical tests are usually performed on a tiny number of samples for which no theoretical guarantees exist for their accuracy. In contrast to testing for deterministic programs, where one trace is sufficient to prove the existence of a bug; such is not the case for samplers as one sample is typically not sufficient to prove non-conformity of the sampler to the desired distribution. This makes one wonder: whether it is possible to design testing methodology to test whether a sampler under test generates samples close to a given distribution. We will discuss, to the best of our knowledge, the first algorithmic framework, Barbarik, to test whether the distribution generated is close to the uniform distribution. In contrast to the sampling techniques that require an exponential or sub-exponential number of samples for sampler whose support can be represented by n bits, Barbarik requires samples independent of n. We present a prototype implementation of Barbarik and use it to test three state of the art uniform samplers over the support defined by combinatorial constraints. Barbarik is able to provide a certificate of uniformity to one sampler and demonstrate non-uniformity for the other two samplers (joint work with Sourav Chakraborty)
Location: CBIM 22
Committee:
Start Date: 12 Feb 2020;
Start Time: 11:00AM - 12:00PM
Title: Structure and Dynamics of Contagion in Financial Networks

Bio:
Speaker:

Victor Amelkin (Penn)

Abstract: Interdependencies between firms that hold each other's shares (or other obligations) are a channel through which shocks to one firm can be transmitted to others. Thus, a small yet sufficient number of failing firms may bring down the entire network. The key question is what is the impact of firms' cash reserves and structure of the cross holdings network upon the system's exposure to risk. We study a financial network of equity share cross-holdings. In the model, a firm's value depends on its cash endowment and the shares it owns in other firms. If a firm's value falls below a failure threshold, it discontinuously imposes losses on its counter-parties (i.e., default costs). We find that the model's equilibria---understood as consistent combinations of firm market values---are distributed non-uniformly in space: while there is concentration of equilibria around the "worst" and the "best" equilibria, generally, there are many equilibria "in-between", suggesting that the extreme equilibria---focused on in other studies of similar models---are by no means representative. If we let firm market values change according to a natural dynamic, we find that, in many regimes when firms' cash endowments are non-extreme, a large fraction of the model's state space is risk-free in that, if the firms start with their market values being in this safe zone, the initially solvent firms remain solvent or, potentially, firms switch from insolvency to solvency. We also provide a formal characterization of the risk zone, with all the firms whose market values started in the risk zone ending up insolvent. The size of the risk zone is determined by the firms' cash reserves, shock magnitude and occurrence threshold, and the firms cross holdings. Joint work with Santosh Venkatesh and Rakesh Vohra.
Location: CoRE A 301
Committee:
Start Date: 19 Feb 2020;
Start Time: 11:00AM - 12:00PM
Title: Approximation Schemes for a Unit-Demand Buyer with Independent Items via Symmetries

Bio:
Speaker:

Ariel Schvartzman (Princeton)

Location: CoRE A 301
Committee:
Start Date: 25 Feb 2020;
Start Time: 10:30AM -
Title: Emerging Architectures for Humanity's Grand Challenges

Bio:

Yipeng is a postdoctoral research associate at Princeton University, working with Prof. Margaret Martonosi. He received his PhD in computer science in 2018 from Columbia University, working with Prof. Simha Sethumadhavan. His research interest is in building, programming, and in identifying applications for emerging architectures. These include quantum and analog computer architectures that may uniquely address challenges in scientific computing, but would require new programming tools and architectural abstractions. His work has been recognized as an IEEE Micro Top Pick among computer architecture conference papers. He has also received support for his research work through DARPA, in the form of a Small Business Technology Transfer grant to investigate commercial applications of his research.

Speaker:

Yipeng Huang, Princeton University

Abstract: As we enter the post-Moore's law era of computer architecture, researchers are turning to new models of computation to address humanity's Grand Challenges. These new models of computation include analog computing and quantum computing. At a high level, they offer fundamentally different capabilities compared to classical, digital computing machines. These capabilities include simulating natural phenomena using physics as a direct model. The urgent challenges in harnessing these promising models of computation are in connecting them to conventional architectures, and in helping programmers correctly use them. First, I will talk about how analog accelerators can play a role in modern architectures to aid in modeling stochastic and nonlinear phenomena. The key feature of the analog model of computation is that variables evolve continuously, thereby avoiding many problems associated with the step-by-step updating of variables in all digital machines. Using prototype analog accelerators that I helped build at Columbia University, I demonstrate using analog approximate solutions as good initial seeds for GPUs solving scientific computation problems. Second, I will talk about helping programmers debug and simulate quantum computer algorithms. As more capable quantum computer prototypes become available, students and researchers need guidelines and tools for debugging and testing quantum programs. My work discusses what quantum bugs are, how statistical tests may find them, and how programmers can write assertions to more effectively bring up quantum programs. Furthermore, I show how tools originally meant for classical inference may enable new queries in aid of debugging quantum programs.
Location: CoRE A 301
Committee:
Start Date: 26 Feb 2020;
Start Time: 11:00AM -
Title: An Adaptive Step Toward the Multiphase Conjecture

Bio:
Speaker:

Omri Weinstein

Abstract: In 2010, Patrascu proposed the Multiphase problem, as a candidate for proving polynomial lower bounds on the operational time of dynamic data structures. Patrascu conjectured that any data structure for the Multiphase problem must make n^eps cell-probes in either the update or query phase, and showed that this would imply similar unconditional lower bounds on many important dynamic data structure problems. There has been almost no progress on this conjecture in the past decade. We show an ~\Omega(\sqrt{n}) cell-probe lower bound on the Multiphase problem for data structures with general (adaptive) updates, and queries with unbounded but "layered" adaptivity. This result captures all known set-intersection data structures and significantly strengthens previous Multiphase lower bounds which only captured non-adaptive data structures. Our main technical result is a communication lower bound on a 4-party variant of Patrascu's NOF Multiphase game, using information complexity techniques. We show that this communication lower bound implies the first polynomial (n^{1+ 1/k}) lower bound on the number of wires required to compute a *linear* operator using *nonlinear* (degree-k) gates, a longstanding open question in circuit complexity. Joint work with Young Kun Ko.
Location: CoRE A 301
Committee:
Start Date: 27 Feb 2020;
Start Time: 10:30AM -
Title: Postmortem Program Analysis from a Conventional Program Analysis Method to an AI-assisted Approach

Bio:

Dr. Xinyu Xing is an Assistant Professor at Pennsylvania State University. His research interests include exploring, designing and developing new program analysis and AI techniques to automate vulnerability discovery, failure reproduction, vulnerability diagnosis (and triage), exploit and security patch generation. His past research has been featured by many mainstream media and received the best paper awards from ACM CCS and ACSAC. Going beyond academic research, he also actively participates and hosts many world-class cybersecurity competitions (such as HITB and XCTF). As the founder of JD-OMEGA, his team has been selected for DEFCON/GeekPwn AI challenge grand final at Las Vegas. Currently, his research is mainly supported by NSF, ONR, NSA and industry partners.

Speaker:

Dr. Xinyu Xing, Pennsylvania State University

Abstract: Despite the best efforts of developers, software inevitably contains flaws that may be leveraged as security vulnerabilities. Modern operating systems integrate various security mechanisms to prevent software faults from being exploited. To bypass these defenses and hijack program execution, an attacker needs to constantly mutate an exploit and make many attempts. While in their attempts, the exploit triggers a security vulnerability and makes the running process abnormally terminate. After a program has crashed and abnormally terminated, it typically leaves behind a snapshot of its crashing state in the form of a core dump. While a core dump carries a large amount of information, which has long been used for software debugging, it barely serves as informative debugging aids in locating software faults, particularly memory corruption vulnerabilities. As such, previous research mainly seeks fully reproducible execution tracing to identify software vulnerabilities in crashes. However, such techniques are usually impractical for complex programs. Even for simple programs, the overhead of fully reproducible tracing may only be acceptable at the time of in-house testing. In this talk, I will discuss how we tackle this issue by bridging program analysis with artificial intelligence (AI). More specifically, I will first talk about the history of postmortem program analysis, characterizing and disclosing their limitations. Second, I will introduce how we design a new reverse-execution approach for postmortem program analysis. Third, I will discuss how we integrate AI into our reverse-execution method to escalate its analysis efficiency and accuracy. Last but not least, as part of this talk, I will demonstrate the effectiveness of this AI-assisted postmortem program analysis framework by using massive amounts of real-world programs.
Location: CoRE A 301
Committee:
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:

Radha Venkatagiri, University of Illinois at Urbana-Champaign

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:

Zuohui Fu

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:

Fotis Iliopoulos (IAS)

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:

Matei Ciocarlie, Associate Professor, Mechanical Engineering
https://roam.me.columbia.edu/matei-ciocarlie

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:

Meng Xu, Georgia Tech

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:

Rahul Shome

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:

Muthu Venkitasubramaniam, University of Rochester

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:

Chen Wang (Rutgers)

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:

Akanksha Jain, University of Texas at Austin

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:

Rushin Gindra

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:

Eduard Gibert Renart

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:

Xiaoli He

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:

Ji Zhang

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:

Paul Grubbs, Cornell University

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:

Zhihan Fang

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:

Noah Stephens-Davidowitz, Simons Institute in Berkeley

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:

Kun Wang

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:

Honglu Zhou

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:

Abhishek Bhrushundi

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:

Zachary Daniels

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:

Liam Schramm

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:

### Shaohua Duan​

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:

Ligong Han

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:

Xin Dong

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:

Bowen Wen

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:

Vishwas Bhargava

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:

Carlos M. Muniz

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:

Junchi Liang

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:

Yanshi Luo

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:

Qi Chang

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:

Alexander Conway

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:

Mihee Lee

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:

Ruoyuan Gao

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:

Malihe Alikhani

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:

Sepehr Janghorbani

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:

Cong Zhang

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:

Cynthia Rudin, Duke University

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:

Chaitanya Mitash

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:

Guangyan Hu

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:

Yuting Wang

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:

Siwei Feng

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:

Dr. Jingjin Yu

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:

Dr. Konstantinos P. Michmizos

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:

Dr. Abdeslam Boularias

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:

Tugba Kulahcioglu

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:

Rui Wang

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:

Zach Langley (Rutgers University)

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:

Haozhe Su

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: 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:

Guido Tagliavini Ponce

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: