| Start Date: 04 Oct 2022; Start Time: 11:00AM - Title: Hybrid robotics and implicit learning Bio: Michael Posa is an Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. He leads the Dynamic Autonomy and Intelligent Robotics (DAIR) lab, a group within the Penn GRASP laboratory. His group focuses on developing computationally tractable algorithms to enable robots to operate both dynamically and safely as they quickly maneuver through and interact with their environments, with applications including legged locomotion and manipulation. Michael received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2017, where, among his other research, he spent time on the MIT DARPA Robotics Challenge team. He received his B.S. in Mechanical Engineering from Stanford University in 2007. Before his doctoral studies, he worked as an engineer at Vecna Robotics in Cambridge, Massachusetts, designing control algorithms for the BEAR humanoid robot. He has received the Best Paper award at Hybrid Systems: Computation and Control (HSCC) and been finalist awards at ICRA and IEEE Humanoids. He has also received Google Faculty Research Award in 2019 and the Young Faculty Researcher Award from the Toyota Research Institute in 2021. Speaker: Abstract: Machine learning has shown incredible promise in robotics, with some notable recent demonstrations in manipulation and sim2real transfer. These results, however, require either an accurate a priori model (for simulation) or a large amount of data. In contrast, my lab is focused on enabling robots to enter novel environments and then, with minimal time to gather information, accomplish complex tasks. In this talk, I will argue that the hybrid or contact-driven nature of real-world robotics, where a robot must safely and quickly interact with objects, drives this high data requirement. In particular, the inductive biases inherent in standard learning methods fundamentally clash with the non-differentiable physics of contact-rich robotics. Focusing on model learning, or system identification, I will show both empirical and theoretical results which demonstrate that contact stiffness leads to poor training and generalization, leading to some healthy skepticism of simulation experiments trained on artificially soft environments. Fortunately, implicit learning formulations, which embed convex optimization problems, can dramatically reshape the optimization landscape for these stiff problems. By carefully reasoning about the roles of stiffness and discontinuity, and integrating non-smooth structures, we demonstrate dramatically improved learning performance. Within this family of approaches, ContactNets accurately identifies the geometry and dynamics of a six-sided cube bouncing, sliding, and rolling across a surface from only a handful of sample trajectories. Similarly, a piecewise-affine hybrid system with thousands of modes can be identified purely from state transitions. I'll also discuss how these learned models can be deployed for control via recent results in real-time, multi-contact MPC. Location: 1 Spring street, New Brunswick, NJ – Room 403 + Virtual Committee: |
| Start Date: 19 Oct 2022; Start Time: 05:30PM - Title: SAViR-T: Spatially Attentive Visual Reasoning with Transformers Bio: Speaker: Abstract: Visual Reasoning (VR) operates as a way to measure machine intelligence, by employing previously gained knowledge in new settings. Specifically, in VR, we aim to extract and identify task-relevant information from images. For example, in Raven's Progressive Matrices (RPMs), an instance of VR, we are given an incomplete 3x3 image puzzle. We should find the governing rules that generated the puzzle in order to solve it. In this talk, we will explore the importance of localized spatial information for the solution of RPM puzzles. Our proposed model SAViR-T considers explicit spatial semantics of visual elements within each image in the puzzle, encoded as spatio-visual tokens, and learns the intra-image as well as the inter-image token dependencies. Token-wise relationships, modeled through a transformer-based SAViR-T architecture, followed by a reasoning module are used to extract the underlying rule representations between the rows of the RPM. We use these relation representations to complete the puzzle. Finally, to demonstrate the efficacy of our approach we performed extensive experiments across both synthetic datasets, including RAVEN, I-RAVEN, RAVEN-FAIR, and the natural image-based "V-PROM". Location: Virtual Committee: Professor Vladimir Pavlovic Professor Srinivas Narayana Ganapathy Professor Hao Wang Professor Yongfeng Zhang |
| Start Date: 21 Oct 2022; Start Time: 10:00AM - Title: Learning-Based Robot Control from Vision: Formal Guarantees and Fundamental Limits Bio: Anirudha Majumdar is an Assistant Professor at Princeton University in the Mechanical and Aerospace Engineering (MAE) department, and Associated Faculty in the Computer Science department. He also holds a part-time position as a Visiting Research Scientist at the Google AI Lab in Princeton. He received a Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2016, and a B.S.E. in Mechanical Engineering and Mathematics from the University of Pennsylvania in 2011. Subsequently, he was a postdoctoral scholar at Stanford University from 2016 to 2017 at the Autonomous Systems Lab in the Aeronautics and Astronautics department. He is a recipient of the ONR YIP award, the NSF CAREER award, the Google Faculty Research Award (twice), the Amazon Research Award (twice), the Young Faculty Researcher Award from the Toyota Research Institute, the Best Conference Paper Award at the International Conference on Robotics and Automation (ICRA), the Paper of the Year Award from the International Journal of Robotics Research (IJRR), the Alfred Rheinstein Faculty Award (Princeton), and the Excellence in Teaching Award from Princeton’s School of Engineering and Applied Science. Speaker: Abstract: The ability of machine learning techniques to process rich sensory inputs such as vision makes them highly appealing for use in robotic systems (e.g., micro aerial vehicles and robotic manipulators). However, the increasing adoption of learning-based components in the robotics perception and control pipeline poses an important challenge: how can we guarantee the safety and performance of such systems? As an example, consider a micro aerial vehicle that learns to navigate using a thousand different obstacle environments or a robotic manipulator that learns to grasp using a million objects in a dataset. How likely are these systems to remain safe and perform well on a novel (i.e., previously unseen) environment or object? How can we learn control policies for robotic systems that provably generalize to environments that our robot has not previously encountered? Unfortunately, existing approaches either do not provide such guarantees or do so only under very restrictive assumptions.In this talk, I will present our group’s work on developing a framework for learning control policies for robotic systems with formal guarantees on generalization to novel environments. The key technical insight is to leverage and extend powerful techniques from generalization theory in theoretical machine learning. We apply our techniques on problems including vision-based navigation and manipulation in order to demonstrate the ability to provide strong generalization guarantees on robotic systems with complicated (e.g., nonlinear/hybrid) dynamics, rich sensory inputs (e.g., RGB-D), and neural network-based control policies. I will also present recent work aimed at understanding fundamental limits on safety and performance imposed by a robot’s (imperfect) sensors. Location: Room 403, 1 Spring street, New Brunswick, NJ + Virtual Committee: |
| Start Date: 21 Oct 2022; Start Time: 05:00PM - 07:00PM Title: Complete and Efficient Prehensile Rearrangement in Confined Spaces under Kinematic Constraints Bio: Speaker: Abstract: Rearranging objects in confined spaces has broad applications, such as rearranging products in grocery shelves (e.g., restocking), retrieving food from a packed refrigerator and assembling a product out of individual components. Meanwhile solving such problems in confined spaces is challenging as no top-down grasps are available for approaching the objects, which simplify rearrangement tasks on tabletops. As a result, these problems involve challenging kinematic and geometric constraints, which include both robot-to-object and object-to-object interactions.This thesis is motivated by this domain and proposes task and motion planning algorithms that result in a high-quality sequence of robot motions, which allow to successfully complete rearrangement tasks in confined spaces without undesirable collisions. Specifically, this work introduces efficient monotone solvers for solving monotone problems, i.e., those that can be solved by moving each object at most once, by significantly pruning the search space of possible solutions. In this process, it sidesteps expensive computational operations, while maintaining desirable completeness guarantees. This thesis progresses in incorporating the proposed monotone solvers to develop probabilistically complete non-monotone solvers, which are capable of solving harder instances quickly with fewer buffer locations, i.e., intermediate placements for objects needed during the rearrangement process. This work also provides improved motion planning primitives for rearrangement to speed up online motion planning resolution. The combination of these algorithmic improvements allows for increased feasibility, efficiency and quality of solutions. Finally, this work demonstrates the applicability of the proposed methods via a proof-of-concept real robotic rearrangement system, which integrates visual input and the developed task and motion planning methods. Location: Room 203, 1 Spring Street, New Brunswick, 08901 Committee: Professor Kostas Bekris (Chair) |
| Start Date: 02 Nov 2022; Start Time: 12:00PM - 01:00PM Title: A Manifold View of Adversarial Risk Bio: Speaker: Abstract: The adversarial risk of a machine learning model has been widely studied. Most previous works assume that the data lies in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration. Assuming data lies in a manifold, we investigate two new types of adversarial risk, the normal adversarial risk due to perturbation along normal direction, and the in-manifold adversarial risk due to perturbation within the manifold. We prove that the classic adversarial risk can be bounded from both sides using the normal and in-manifold adversarial risks. We also show with a surprisingly pessimistic case that the standard adversarial risk can be nonzero even when both normal and in-manifold risks are zero. We finalize the paper with empirical studies supporting our theoretical results. Our results suggest the possibility of improving the robustness of a classifier by only focusing on the normal adversarial risk. Location: https://rutgers.zoom.us/j/93084168396?pwd=OFZZeHJOYWlydEEyRDZmdW40aitQdz09 Committee: Prof. Dimitris Metaxas Dr. Hao Wang Prof. Konstantinos Michmizos. Prof. Xiong Fan |
| Start Date: 04 Nov 2022; Start Time: 11:00AM - 12:15PM Title: Foundations of Transaction Fee Mechanism Design Bio: Elaine Shi is an Associate Professor at Carnegie Mellon University. Her research interests include cryptography, algorithms, and foundations of blockchains. Prior to CMU, she taught at the University of Maryland and Cornell University. She is a recipient of the Packard Fellowship, the Sloan Fellowship, the ONR YIP Award, the NSA best scientific cybersecurity paper award, and various other best paper awards. Speaker: Abstract: Space in a blockchain is a scarce resource. Cryptocurrencies today use auctions to decide which transactions get confirmed in the block. Intriguingly, classical auctions fail in such a decentralized environment, since even the auctioneer can be a strategic player. For example, the second-price auction is a golden standard in classical mechanism design. It fails, however, in the blockchain environment since the miner can easily inject a bid that is epsilon smaller than the k-th price where k is the block size. Moreover, the miner and users can also collude through the smart contract mechanisms available in modern cryptocurrencies. I will talk about a new foundation for mechanism design in a decentralized environment. I will prove an impossibility result which rules out the existence of a dream transaction fee mechanism that incentivizes honest behavior for the user, the miner, and a miner-user coalition at the same time. I will then argue why the prior modeling choices are too draconian, and how we can overcome this lower bound by capturing hidden costs pertaining to certain deviations. Location: Core 301 Committee: |
| Start Date: 09 Nov 2022; Start Time: 08:00AM - 05:00PM Title: Towards Real-time Scene-level Tracking and Reconstruction Bio: Speaker: Abstract: Real-time perception is a crucial component of modern robotic manipulation systems. Recent progress in manipulation area has demonstrated that given the geometry model and 6D-pose trajectory of a manipulated object during an expert demonstration, a robot can quickly learn complex and contact-rich manipulation skills. However, a system that can simultaneously provide a geometry model and 6-D pose is notoriously hard to build. In this talk, we present the first real-time system, STAR-no-prior capable of tracking and reconstructing, individually, every visible object in a given scene, without any form of prior on the rigidness of the objects, texture existence, or object category. Despite its strong performance, STAR-no-prior is limited to its multi-camera setting. To compensate for this, we further develop Mono-STAR, the first real-time mono-camera 3D reconstruction system that simultaneously supports semantic fusion, fast motion tracking, non-rigid object deformation, and topological change under a unified framework. Location: https://rutgers.zoom.us/j/92713978624?pwd=UXVyeXBibkdObHJkUGdScmVOMmlPUT09 Meeting ID: 927 1397 8624 Password: 981315 Committee: Abdeslam Boularias Kostas Bekris Mridul Aanjaneya Dong Deng |
| Start Date: 11 Nov 2022; Start Time: 02:30PM - 04:00PM Title: Space Optimal Matching and Vertex Cover in dynamic streams Bio: Speaker: Abstract: We present algorithms for maximum matching and vertex cover in dynamic (insertion-deletions) streams with asymptotically optimal space complexity: for any n-vertex graph, our algorithms with high probability output an α-approximate matching in a single pass using O(n^2/α^3) bits of space and an α-approximate vertex cover using O(n^2/α^2) bits of space.A long line of work on the dynamic streaming matching problem has reduced the gap between space upper and lower bounds first to n^o(1) factors [Assadi-Khanna-Li-Yaroslavtsev; SODA 2016] and subsequently to polylog(n) factors [Dark-Konrad; CCC 2020]. [Dark-Konrad; CCC 2020] also gave upper and lower bounds for streaming vertex cover that were only a polylog(n) factor apart. Our upper bounds now match the DarkKonrad lower bounds up to O(1) factors, thus completing this research direction.Our approach consists of two main steps: we first (provably) identify a family of graphs, similar to the instances used in prior work to establish the lower bounds for this problem, as the only “hard” instances to focus on. These graphs include an induced subgraph which is both sparse and contains a large matching. We then design dynamic streaming algorithms for this family of graphs which is more efficient than prior work. The keys to this efficiency are novel sketching methods, which bypass the typical loss of polylog (n)-factors in space compared to standard L0-sampling primitives, and can be of independent interest in designing optimal algorithms for other streaming problems. Location: Zoom information: https://rutgers.zoom.us/j/91956380264?pwd=c1I0bFk3dkhwalVQMjlmZk1MY3ZrUT09 Meeting ID: 919 5638 0264 P Committee: Prof. Aaron Bernstein Prof. Jie Gao Prof. Sepehr Assadi Prof. Yipeng Huang |
| Start Date: 17 Nov 2022; Start Time: 10:30AM - Title: Unstructured Data Management at Scale for Machine Learning Bio: Dong Deng is an assistant professor in the Computer Science Department at Rutgers University. His research interests include large-scale data management, data science, data curation, and database systems. Before joining Rutgers, he was a postdoc in the Database Group at MIT, where he worked with Mike Stonebraker and Sam Madden on data curation systems. He received his Ph.D. from Tsinghua University with honors. He has published over 30 research papers in top data management venues, mainly SIGMOD, VLDB, and ICDE. Based on Google Scholar, his publications have attracted over 2000 citations. His research is supported by a couple of NSF awards. He regularly serves the PC committee of various data management, data mining, and information retrieval conferences. He also serves on the organization committees of several data management conferences. Speaker: Abstract: A clear trend in machine learning is that models become larger and larger and more and more training data is used. This is especially true for foundation models, such as GPT-3 and DALL-E 2. In this talk, I will discuss a few unstructured data management problems arising in machine learning. First, recent studies show large language models (LLMs) unintendedly memorize part of the training data, which brings significant privacy risks. These studies mostly focus on the exact duplicates. However, how many texts generated by LLMs have near-duplicate sequences in the training data? Do sequences with more near-duplicates in the training data more likely to be memorized by LLMs? To answer these questions, I will introduce a series of works (SIGMOD-21 and SIGMOD-22a) from my group that enables efficient near-duplicate sequence search in terabytes of LLM training texts on a single machine. A major challenge here is the number of sequences in a text is quadratic to the text length. Second, real-world objects such as images and texts can be represented as dense vectors. I will briefly introduce our work on large-scale vector data management, a project funded by NSF IIS. Finally, I will conclude the talk by outlining a few near-future works to be conducted in my group. Location: CoRE 301 Committee: |
| Start Date: 22 Nov 2022; Start Time: 09:30AM - 12:30PM Title: Fine-grained Air Quality Nowcasting Bio: Speaker: Abstract: Prolonged exposure to air pollution is a health hazard. Users could reduce pollution exposure by accessing an accurate air quality information stream. The current air quality information stream is coarse-grained (measurements spatially and temporally few and far between) and does not reflect the localized air quality variations that a fine-grained (spatially and temporally dense measurements) information stream could. This dissertation aims to fill this need by proposing new sensing and modeling recipes to achieve fine-grained information streams with pollution inventory. In the first part of the dissertation, we present two mobile sensing platforms for fine-grained real-time pollution measurements - a portable sensing platform deployable on public transportation infrastructure and a personal sensing device that can create a social pollution sensing network. We assemble mobile sensing platforms and deploy them on Rutgers campus buses for evaluation. We conclude that mobile sensing platforms deployed on public transportation infrastructure can help collect fine-grained pollution measurements. We also propose a new neural network architecture - "InsideOut," to infer Carbon Monoxide measurements outdoors based on in-vehicle Carbon Monoxide measurements collected by users monitoring their personal space, thus contributing to the fine-grained pollution inventory outdoors. In the second part of the dissertation, we propose "X-PoSuRe" - a neural network-based regression model for pollution super-resolution trained to infer fine-grained pollution information from coarse-grained pollution measurements akin to image super-resolution, where a neural network model creates high-resolution images from low-resolution images. The X-PoSuRe model uses Nitrogen Dioxide measurements and other air quality covariates for pollution super-resolution. The proposed X-PoSuRe model provides a promising new and novel method for pollution super-resolution from existing low-resolution data sources without the need for deploying expensive equipment over a large area. We further extend this model to infer fine-grained measurements for other pollutants.In the third and final step, we evaluate the benefits of a fine-grained pollution inventory by demonstrating on a neighborhood scale that a significant reduction in pollution exposure can be achieved by choosing a healthy way instead of the shortest or quickest way.Overall, our work pushes the frontiers of inference models in modeling and inferring a hard-to-measure entity using its easily measurable covariates. Location: CoRE 305 Committee:
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| Start Date: 22 Nov 2022; Start Time: 10:45AM - Title: Bayesian Deep Learning: From Single-Domain Reasoning to Infinite-Domain Adaptation Bio: Hao Wang is currently an Assistant Professor in the Department of Computer Science at Rutgers University. Previously he was a Postdoctoral Associate at the Computer Science & Artificial Intelligence Lab (CSAIL) of MIT, working with Dina Katabi and Tommi Jaakkola. He received his PhD degree from the Hong Kong University of Science and Technology, as the sole recipient of the School of Engineering PhD Research Excellence Award in 2017. He has been a visiting researcher in the Machine Learning Department of Carnegie Mellon University. His research focuses on statistical machine learning, deep learning, and data mining, with broad applications on recommender systems, healthcare, user profiling, social network analysis, text mining, etc. His research was recognized and supported by the Microsoft Fellowship in Asia, the Baidu Research Fellowship, the Amazon Faculty Research Award, and the National Science Foundation. Speaker: Abstract: While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning, and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. In terms of higher-level inference, however, probabilistic graphical models, with their ability to expressively describe properties of variables and various probabilistic relations among variables, are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, we have been exploring along a research direction, which we call Bayesian deep learning, to tightly integrate deep learning and Bayesian models within a principled probabilistic framework. In this talk, I will present the proposed unified framework and some of our recent work on Bayesian deep learning with various applications including recommendation, social network analysis, interpretable healthcare, domain adaptation, and representation learning. Location: CoRE 301 Committee: |
| Start Date: 28 Nov 2022; Start Time: 10:30AM - 12:00PM Title: Self-Supervised Object Understanding for Robot Perception and Manipulation Bio: Speaker: Abstract: : To be deployed in indoor human environments, autonomous robots should be able to reason about unknown objects so as to manipulate them safely and efficiently. It is, however, challenging because household objects can be arbitrary with various textures and geometries. And it is not feasible to manually annotate or train a model for every single object or even at the category level. We aim to let robots understand objects from their own experiences, either through passively observing objects from different viewpoints and locations, or actively manipulating them. In particular, we designed algorithms to automatically find patterns of rigid objects across different scenes even in clutter, and use the pseudo labels to train a segmentation model via contrastive learning. We also built a robotic system to manipulate objects without access to mesh models. The target object is tracked and reconstructed during the manipulation process. Its texture information and reconstructed model are saved in a memory bank to speed up future manipulation of the same object. Through a lifelong process, the robot becomes more capable over time as it observes and manipulates more objects. In the future, we aim to extend the current research to allow robots to robustly handle objects in more complex scenes. Location: 1 Spring Street, Room 204 Committee: Prof. Kostas Bekris (advisor) Prof. Abdeslam Boularias Prof. Kristin Dana Prof. Sepehr Assadi |
| Start Date: 05 Dec 2022; Start Time: 10:30AM - Title: From Deep Learning to Program Learning Bio: He Zhu is an assistant professor in the Department of Computer Science at Rutgers University. He was a research scientist at Galois, Inc. He received his Ph.D. degree from Purdue University. His work spans programming languages, formal methods, and machine learning. He is currently interested in building intelligent program learning systems that tightly integrate deep learning and program synthesis and that can be formally verified. Dr. Zhu received two distinguished paper awards from the prestigious ACM SIGPLAN conference on Programming Language Design and Implementation. His research was supported by the National Science Foundation and the Defense Advanced Research Projects Agency. Speaker: Abstract: Deep learning has led to encouraging successes in many challenging tasks. However, a deep neural net inherently lacks interpretability due to the difficulty of identifying how the model's learned logic relates to its complex network structure. It is difficult to debug and reason about deep neural nets at the same level developers manage conventional software systems. Program-guided models (i.e. neurosymbolic programs) have recently attracted much interest due to their interpretability and compositionality. Yet, synthesizing programs requires optimizing over a combinatorial, non-differentiable, and rapidly exploded space of program structures. In this talk, I will present our recent efforts on enabling human-readable, domain-specific programs as an efficient learning representation. Powered by novel program synthesis algorithms, our method jointly optimizes program structures and program parameters. As a step toward trustworthy learning, it adapts formal methods typically designed for traditional human-written software systems to provide formal correctness guarantees to program-guided models. Experiment results over application domains such as behavior classification and reinforcement learning demonstrate that our algorithms excel in discovering optimal programs that are highly interpretable and verifiable. Location: CoRE 301 + Virtual Committee: |
| Start Date: 06 Dec 2022; Start Time: 10:30AM - Title: Contrastive Self-Supervised Learning and Deep Pre-trained Language Models for Entity Resolution Bio: Speaker: Abstract: Entity Resolution (ER) is a field of study dedicated to finding items that belong to the same entity, and is an essential problem in NLP and data integration and preparation (DI&P). We propose Sudowoodo, a multi-purpose DI&P framework based on contrastive representation learning and deep pre-trained language models. Sudowoodo features a unified, matching-based problem definition capturing a wide range of DI&P tasks including Entity Resolution (ER) in data integration, error correction in data cleaning, semantic type detection in data discovery, and more. Contrastive learning enables Sudowoodo to learn similarity-aware data representations from a large corpus of data items (e.g., entity entries, table columns) without using any labels. The learned representations can later be either directly used or facilitate fine-tuning with only a few labels to support the ER task. Our experiment results show that Sudowoodo achieves multiple state-of-the-art results on different levels of supervision and outperforms previous best specialized blocking or matching solutions for ER. Sudowoodo also achieves promising results in data cleaning and column matching tasks showing its versatility in DI&P applications. For the blocking step of ER, we propose Neural Locality Sensitive Hashing Blocking (NLSHBlock), which is based on pre-trained language models and fine-tuned with a novel LSH-inspired loss function. NLSHBlock out-performs existing methods on a wide range of datasets. Location: Virtual-The zoom link is https://rutgers.zoom.us/j/93769571337?pwd=NC9nR3RVeDJzcHpUZHl1ZWExK0Jndz09 Committee: Dr. Yongfeng Zhang (advisor) Dr. Hao Wang Dr. Dong Deng Dr. Kostas Bekris (external) |
| Start Date: 12 Dec 2022; Start Time: 10:30AM - Title: Transparent Computing in the AI Era Bio: Shiqing Ma is an Assistant Professor in the Department of Computer Science at Rutgers University, the state university of New Jersey. He received his Ph.D. in Computer Science from Purdue University in 2019 and B.E. from Shanghai Jiao Tong University. His research focuses on program analysis, software and system security, adversarial machine learning, and software engineering. His work on operating system transparency received Distinguished Paper Awards from NDSS 2016 and USENIX Security 2017, and his work on adversarial machine learning received Best Student Paper Awards from ECCV ARWO 2022 and ICTAI 2022. Speaker: Abstract: Recent advances in artificial intelligence (AI) have shifted how modern computing systems work, raising new challenges and opportunities for transparent computing. On the one hand, many AI systems are black boxes and have dense connections among their computing units, which makes existing techniques like dependency analysis fail. Such a new computing system calls for new methods to improve its transparency to defend against attacks against AI-powered systems, such as Trojan attacks. On the other hand, it provides a brand-new computation abstraction, which features data-driven computation-heavy applications. It potentially enables new transparent computing applications, typically involving large-scale data processing. In this talk, I will present my work in these two directions. Specifically, I will discuss the challenges in analyzing the deep neural network for security inspection and introduce our novel approach to examining Trojan behaviors. Later, I will talk about AI can help increase the information entropy of large security audit logs to enable efficient lossless compressed storage. Location: CoRE 301 + Virtual Committee: |
| Start Date: 13 Dec 2022; Start Time: 09:00AM - Title: Generative Video Transformer: Can Objects be the Words? Bio: Speaker: Abstract: Transformers have been successful for many natural language processing tasks. However, applying transformers to the video domain for tasks such as long-term video generation and scene understanding has remained elusive due to the high computational complexity and the lack of natural tokenization. In this paper, we propose the Object-Centric Video Transformer (OCVT) which utilizes an object-centric approach for decomposing scenes into tokens suitable for use in a generative video transformer. By factoring the video into objects, our fully unsupervised model is able to learn complex spatio-temporal dynamics of multiple interacting objects in a scene and generate future frames of the video. Our model is also significantly more memory-efficient than pixel-based models and thus able to train on videos of length up to 70 frames with a single 48GB GPU. We compare our model with previous RNN-based approaches as well as other possible video transformer baselines. We demonstrate OCVT performs well when compared to baselines in generating future frames. OCVT also develops useful representations for video reasoning, achieving start-of-the-art performance on the CATER task. Location: Virtual https://rutgers.zoom.us/j/96462372427?pwd=Tm5vUmxzcGdZZ1RaWGVWUEE0VDNwQT09 Committee: Prof. Sungjin Ahn (Chair) Prof. Hao Wang Prof. Abdeslam Boularias Prof.Yongfeng Zhang(external) |
| Start Date: 21 Dec 2022; Start Time: 03:00PM - 04:30PM Title: Methods for Leveraging Auxiliary Signals for Low-Resource NLP Bio: Speaker: Abstract: There is a growing need for NLP systems that support low-resource settings, for which task-specific training data may be lacking, while domain-specific corpora is too scarce to build a reliable system. In the past decade, the co-occurrence-based training objectives of methods such as word2vec are first able to offer word-level semantic information for specific domains. Recently, pretrained language model architectures such as BERT have been shown capable of learning monolingual or multilingual representations with self-supervised objectives under a shared vocabulary, simply by combining the input from single or multiple languages.Such representations greatly facilitate low-resource language applications.Still, the success of such cross-domain transfer hinges on how close the involved domains are, with substantial drops observed for some more distant domain pairs, such as English to Korean, Wikipedia text to social media comments. To address this, domain-specific unlabeled corpora is available to serve as the auxiliary signals to enhance low-resource NLP systems. In this dissertation, we present a series of methods for leveraging auxiliary signals. In particular, cross-lingual sentiment embeddings with transfer learning are proposed to improve sentiment analysis. For cross-lingual text classification, we present a self-learning framework to take advantage of unlabeled data. Furthermore, a framework upon data augmentation with adversarial training for cross-lingual NLI is proposed for the low-resource problem from the target domain. Extensive experimental results demonstrate the effectiveness of the proposed methods in achieving better performance across a variety of NLP tasks. Location: Virtual https://rutgers.zoom.us/j/8203272500?pwd=R0hGYkNBQllsaWdKMWN3OEh5V0dpZz09 Committee: Prof Gerard de Melo (Chair) Prof Yongfeng Zhang Prof Karl Stratos Prof Handong Zhao (external Member) |
| Start Date: 08 Feb 2023; Start Time: 02:00PM - 03:30PM Title: Building Efficient Storage Systems for Modern Near Storage Data Processing Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Sudarsan Kannan (Advisor) Professor Santosh Nagarakatte Professor Srinivas Narayana Ganapathy Professor Hao Wang |
| Start Date: 09 Feb 2023; Start Time: 02:00PM - 03:15PM Title: AI for mathematics Bio: PhD In Mathematics, University of Bonn Worked as a research scientist at Cadence Research Laboratories in Berkeley: design automation of digital circuits, physical design and logic synthesis. Christian is a researcher at Google working on machine learning, AI and computer vision via deep learning. Speaker: Abstract: We give an introduction to recent advances in automating mathematics through automated formalization ("autoformalization") and proof-search using deep learning, specifically transformer-based large language models. Autoformalization is the process of automatically transcribing human-written mathematical texts into computer-verifiable proofs. While most natural language mathematics looks fairly formal to the untrained eye, it can take a great deal of human effort to fully formalize mathematical text using "interactive theorem provers". Recent advances in deep-learning-based language modeling and neural-augmented proof search offer a promising path towards autoformalization and human-level mathematical AI. We present recent advances in this area as well as the challenges ahead. Location: Fiber Optics Material Research Building EHA Committee: |
| Start Date: 09 Feb 2023; Start Time: 04:30PM - 06:30PM Title: Representative Learning Enabled Efficient Provenance Data Storage Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Shiqing Ma (Advisor) Professor Dong Deng Professor Sudarsun Kannan Professor Abdeslam Boularias |
| Start Date: 09 Feb 2023; Start Time: 05:00PM - 07:00PM Title: Deep Learning-based Biomedical Images Classification and Segmentation from Limited Data Bio: Speaker: Abstract: See above Location: CBIM 22 Committee: Professor Dimitris N. Metaxas (Chair) Professor Vladimir Pavlovic Professor Hao Wang Dr. Leon Axel (NYU) |
| Start Date: 10 Feb 2023; Start Time: 02:00PM - 04:00PM Title: Safe Object Rearrangement in Confined Spaces Under Visibility Constraints Bio: Speaker: Abstract: See above Location: 1 Spring Street - Room 319 Committee: Professor Kostas Bekris (Advisor) Professor Jingjin Yu Professor Jie Gao Professor He Zhu |
| Start Date: 23 Feb 2023; Start Time: 10:30AM - 11:30AM Title: A Hybrid Computing Ecosystem For Practical Quantum Advantage Bio: Gokul Subramanian Ravi is a 2020 NSF CI Fellows postdoctoral scholar at the University of Chicago, mentored by Prof. Fred Chong. His research targets quantum computing architecture and systems, primarily on themes at the intersection of quantum and classical computing. He received his PhD in computer architecture from UW-Madison in 2020 and was advised by Prof. Mikko Lipasti. He was awarded the 2020 Best ECE Dissertation Award from UW-Madison and named a 2019 Rising Star in Computer Architecture. His quantum and classical computing research have resulted in publications at top computer architecture, systems, and engineering venues (such as ASPLOS, ISCA, MICRO, HPCA, TACO, ISLPED, QCE, IISWC), as well as three filed and two granted patents. His co-authored work was recognized as the Best Paper at HPCA 2022 and as a 2023 IEEE Micro Top Picks Honorable Mention. Speaker: Abstract: As quantum computing transforms from lab curiosity to technical reality, we must unlock its full potential to enable meaningful benefits on real-world applications with imperfect quantum technology. Achieving this vision requires computer architects to play a key role, leveraging classical computing principles to build and facilitate a hybrid computing ecosystem for practical quantum advantage. First, I will introduce my four research thrusts toward building this hybrid ecosystem: Classical Application Transformation, Adaptive Noise Mitigation, Scalable Error Correction and Efficient Resource Management. Second, from the Classical Application Transformation thrust, I will present "CAFQA: A classical simulation bootstrap for variational quantum algorithms", which enables accurate classical initialization for VQAs by searching efficiently through the classically simulable portion of the quantum space with Bayesian Optimization. CAFQA recovers as much as 99.99% of the accuracy lost in prior state-of-the-art classical initialization, with mean improvements of 56x. Third, from the Scalable Error Correction thrust, I will present "Clique: Better than worst-case decoding for quantum error correction", which proposes the Clique QEC decoder for cryogenic quantum systems. Clique is a lightweight cryo-decoder for decoding and correcting common trivial errors, so that only the rare complex errors are handled outside the cryo-refrigerator. Clique eliminates 90-99+% of the cryo-refrigerator I/O decoding bandwidth, while supporting more than a million physical qubits. Finally, I will conclude with an overview of other prior and ongoing work, along with my future research vision toward practical quantum advantage. Location: Core 301 Committee: |
| Start Date: 27 Feb 2023; Start Time: 09:00AM - 11:00AM Title: ROOTS: Object-Centric Representation and Rendering of 3D Scenes Bio: Speaker: Abstract: See above Location: Hill Center 350 (IDEAS Lounge) Committee: Professor Sungjin Ahn (Advisor) Professor Hao Wang Professor Karl Stratos Professor Xiong Fan |
| Start Date: 27 Feb 2023; Start Time: 10:30AM - 11:30AM Title: Convex Integer Optimization Bio: Haotian Jiang is a Postdoctoral Researcher at Microsoft Research, Redmond. In December 2022, he obtained his PhD from the Paul G. Allen School of Computer Science & Engineering at University of Washington under the supervision of Yin Tat Lee. He is broadly interested in theoretical computer science and applied mathematics. His primary area of expertise is the design and analysis of algorithms for continuous and discrete optimization problems. His work on optimization has been recognized by a Best Student Paper Award in SODA 2021. Speaker: Abstract: Designing and analyzing algorithms for optimization problems is a crucial but challenging task that arises in various fields such as business, science, and engineering. Despite the development of various successful optimization algorithms over the past sixty years, many state-of-the-art algorithms are theoretically far from optimal and ad hoc in nature. In this talk, I will present a unified toolbox for designing optimization algorithms through the general problem of Convex Integer Optimization, which captures many central problems and challenges in optimization today. Our toolbox has resulted in faster algorithms for fundamental tractable problems and better approximation algorithms for central NP-hard problems. Furthermore, it has revealed new connections between NP-hard and tractable problems which have been studied relatively independently for over half a century. Our work has created avenues for further investigations and applications in other fields of computer science and operations research, such as understanding the security of lattice-based cryptography and creating faster solvers for integer programming. Finally, I will conclude the talk with several future research directions and open problems at the frontier of optimization and related areas. Location: Core 301 Committee: |
| Start Date: 02 Mar 2023; Start Time: 08:30AM - 10:30AM Title: Matchings in Evolving Graphs Bio: Speaker: Abstract: See above Location: CoRE 431 Committee: Professor Aaron Bernstein (Chair) Professor Jie Gao Professor Sepehr Assadi Professor Sayan Bhattacharya (University of Warwick) |
| Start Date: 02 Mar 2023; Start Time: 10:30AM - 11:30AM Title: Data Structures for Fast Systems Bio: Alex Conway is a senior researcher at VMware. He received his PhD from Rutgers, where he was advised by Martín Farach-Colton. His work has primarily focused on randomized data structures and their use in storage systems, and covers the full research stack, from theory to systems to product. He is the co-creator and research lead of SplinterDB, an enterprise-grade key-value store deployed in VMware products. Speaker: Abstract: In this talk, I'll show how algorithms can be used to solve decades-old problems in systems design. I'll present an algorithmic approach to co-designing TLB hardware and the paging mechanism to increase TLB reach without the fragmentation issues incurred by huge pages. Along the way, I'll introduce a new hash-table design that overcomes existing tradeoffs, and achieves better performance than state-of-the-art hash tables both in theory and in practice. Key to these results are "tiny pointers," an algorithmic technique for compressing pointers. Location: Core 301 Committee: |
| Start Date: 03 Mar 2023; Start Time: 10:30AM - 11:30AM Title: Designing Exascale Distributed Systems Bio: Saurabh Kadekodi obtained his PhD in the Computer Science Department at Carnegie Mellon University (CMU) in 2020 as part of the Parallel Data Laboratory (PDL) under the guidance of Prof. Gregory Ganger and Prof. Rashmi Vinayak. After graduation Saurabh joined Google as a Visiting Faculty Researcher, and is currently a Research Scientist in the Storage Analytics team. Saurabh is broadly interested in designing distributed systems with special focus on the performance and reliability of storage systems. At Google, Saurabh is working towards implementing his PhD thesis on disk-adaptive redundancy and other exciting research ideas in some of the largest systems in the world. Speaker: Abstract: Fundamental physical limitations have slowed down hardware scaling, thus ending the “free”scaling benefits of processing power and storage capacity. At the same time, data is growing atan unprecedented rate. This data juggernaut is highly disruptive. It morphs benign assumptionsinto critical bottlenecks, and forces radical system (re-)designs. My work replaces designdecisions of distributed systems that are disrupted by scale with new, data-driven solutions thatare efficient, scalable, nimble, and robust. As an example, I will describe disk-adaptiveredundancy (DARE): a novel redesign of data reliability in exascale storage clusters driven byinsights gleaned from studying over 5.3 million disks from production environments of Google,NetApp and Backblaze. I will also describe three new DARE systems that reduce conservativeover-protection of data by up to 20% amounting to millions of dollars of cost savings along witha significant carbon footprint reduction, while always meeting desired data reliability targets.Additionally, I will briefly describe some past and current research efforts to improve theavailability and performance of local and distributed storage systems including new erasurecodes that reduce observed unavailability events at Google by up to 33%, a novel agingframework that can systematically age local file systems to look over 20 years old in less than 6hours, and an efficient packing and indexing layer in public cloud infrastructures that boosts thethroughput of accessing tiny objects by over 60x while simultaneously reducing the cost ofaccessing them by over 25000x. Finally, I will touch upon the open challenges in designingexascale distributed systems and highlight promising future directions. Location: CoRE 301 Committee: |
| Start Date: 03 Mar 2023; Start Time: 03:00PM - 04:00PM Title: Neuro-symbolic learning for bilevel planning Bio: Tom Silver is a fifth year PhD student at MIT EECS advised by Leslie Kaelbling and Josh Tenenbaum. His research is at the intersection of machine learning and planning with applications to robotics, and often uses techniques from task and motion planning, program synthesis, and reinforcement learning. Before graduate school, he was a researcher at Vicarious AI and received his B.A. from Harvard in computer science and mathematics in 2016. His work is supported by an NSF fellowship and an MIT presidential fellowship. Speaker: Abstract: Decision-making in robotics domains is complicated by continuous state and action spaces, long horizons, and sparse feedback. One way to address these challenges is to perform bilevel planning with abstractions, where a high-level search for abstract plans is used to guide planning in the original transition space. In this talk, I will give an overview of our recent efforts [1, 2, 3, 4] to design a bilevel planning system with state and action abstractions that are learned from data. I will also make the case for learning abstractions that are compatible with highly optimized PDDL planners, while arguing that PDDL planning should be only one component of a larger integrated planning system.[1] Learning symbolic operators for task and motion planning. Silver*, Chitnis*, Tenenbaum, Kaelbling, Lozano-Perez. IROS 2021.[2] Learning neuro-symbolic relational transition models for bilevel planning. Chitnis*, Silver*, Tenenbaum, Lozano-Perez, Kaelbling. IROS 2022.[3] Predicate invention for bilevel planning. Silver*, Chitnis*, Kumar, McClinton, Lozano-Perez, Kaelbling, Tenenbaum. AAAI 2023.[4] Learning neuro-symbolic skills for bilevel planning. Silver, Athalye, Tenenbaum, Lozano-Perez, Kaelbling. CoRL 2022. Location: 1 Spring Street, Room 403 Committee: |
| Start Date: 06 Mar 2023; Start Time: 10:30AM - 11:30AM Title: When fast algorithms meet modern society Bio: Omri Ben-Eliezer is an instructor (postdoc) in applied mathematics at MIT. He received his PhD in computer science from Tel Aviv University under the supervision of Prof. Noga Alon, and held postdoctoral positions at Weizmann Institute and Harvard University. His research blends aspects of algorithm design and data modeling, with specific interests including sublinear-time and streaming algorithms, large networks, robustness and privacy, and knowledge representation. For his work, Omri received several awards, including best paper awards at PODS 2020 and at CVPR 2020 Workshop on Text and Documents, the 2021 SIGMOD Research Highlight Award, and the first Blavatnik Prize for outstanding Israeli doctoral students in computer science. Speaker: Abstract: The rapidly growing societal impact of data-driven systems requires modern algorithms to process massive-scale complex data not just efficiently, but also responsibly, e.g., under privacy or robustness guarantees. In this talk I will discuss some of my recent research developing fast (e.g., sublinear-time or sublinear-space) and responsible algorithms for modern data analysis problems. I will focus on three representative lines of work: (i) the first systematic investigation of adversarial robustness in streaming algorithms, (ii) algorithm design in real-world social networks via new notions of core-periphery sparsification, and (iii) differentially private synthetic data generation in high dimensions via beyond-worst-case data modeling. Through these examples, I will demonstrate how the symbiosis between algorithm design and modeling of complex data often leads naturally to new structural insights and multidisciplinary connections Location: Core 301 Committee: |
| Start Date: 06 Mar 2023; Start Time: 02:00PM - 03:30PM Title: Efficient Datacenter Management Using Deep Reinforcement Learning Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Thu Nguyen (Advisor) Professor He Zhu Professor Ulrich Kremer Professor David Pennock
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| Start Date: 07 Mar 2023; Start Time: 10:30AM - 11:30AM Title: When Causal Inference Meets Graph Machine Learning: Unleashing the Potential of Mutual Benefit Bio: Jing Ma is a Ph.D. candidate in the Department of Computer Science at University of Virginia, under the supervision of Dr. Jundong Li and Dr. Aidong Zhang. She received her B.Eng. degree and M.Eng. degree at Shanghai Jiao Tong University with Outstanding Graduate Award. Her research interests broadly cover machine learning and data mining, especially include causal inference, graph mining, fairness, trustworthiness, and AI for social good. Her recent work focuses on bridging the gap between causality and machine learning. Her research papers have been published in top conferences and journals such as KDD, NeurIPS, IJCAI, WWW, AAAI, TKDE, WSDM, SIGIR, ECML-PKDD, AI Magazine, and IPSN. She has rich internship experience in companies and academic organizations such as Microsoft Research. She has won some important awards such as SIGKDD 2022 Best Paper Award and CAPWIC 2022 Best Poster Award. Speaker: Abstract: Recent years have witnessed rapid development in graph-based machine learning (ML) in various high-impact domains (e.g., healthcare, recommendation, and security), especially those powered by effective graph neural networks (GNNs). Currently, the mainstream graph ML methods are based on statistical learning, e.g., utilizing the statistical correlations between node features, graph structure, and labels for node classification. However, statistical learning has been widely criticized for only capturing the superficial relations between variables in the data system, and consequently, rendering the lack of trustworthiness in real-world applications. For example, ML models often make biased predictions toward underrepresented groups. Besides, these ML models often lack explanation for human. Therefore, it is crucial to understand the causality in the data system and the learning process. Causal inference is the discipline that investigates the causality inside a system, for example, to identify and estimate the causal effect of a certain treatment (e.g., wearing a face mask) on an important outcome (e.g., COVID-19 infection). Involving the concepts and philosophy of causal inference into ML methods is often considered as a significant component of human-level intelligence and can serve as the foundation of artificial intelligence (AI). However, most traditional causal inference studies rely on strong assumptions, and focus on independent and identically distributed (i.i.d.) data, while causal inference on graphs is faced with many barriers in effectiveness. Fortunately, the interplay between causal inference and graph ML has the potential to bring mutual benefit to each other. In this talk, we will present the challenges and our contributions for bridging the gap between causal inference and graph ML, mainly including two directions: 1) leveraging graph ML methods to facilitate causal inference in effectiveness; and 2) leveraging causality to facilitate graph ML models in model trustworthiness (e.g., model fairness and explanation). Location: Core 301 Committee: |
| Start Date: 16 Mar 2023; Start Time: 03:00PM - 05:00PM Title: Deep Learning with Limited Data Bio: Speaker: Abstract: See above. Location: CBIM 22 Committee: Professor Dimitris Metaxas (Chair) Professor HaoWang Professor Konstantinos Michmizos Professor Sharon Xiaolei Huang (Pennsylvania State University)
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| Start Date: 20 Mar 2023; Start Time: 10:30AM - 12:00PM Title: Rescuing Data Center Processors Bio: Tanvir Ahmed Khan is a final-year Ph.D. candidate at the University of Michigan. His research brings together computer architecture, compilers, and operating systems to enable efficient data center processing. Consequently, his work has been adopted by Intel and ARM to improve the performance of data center applications. Bridging hardware and software, his research appears in venues like ISCA, MICRO, OSDI, PLDI, FAST, and EuroSys. His work has also been recognized with the MICRO 2022 Best Paper Award, DATE 2023 Best Paper Award Nomination, IEEE Micro Top Picks 2023 distinction, and multiple fellowships. Speaker: Abstract: Billions of people rely on web services powered by data centers, where critical applications run 24/7. Unfortunately, data center applications are extremely inefficient, wasting more than 60% of all processor cycles, and causing millions of dollars in operational expenses and energy costs. In this talk, I will present an overview of my vision to overcome this inefficiency using hardware/software co-design. In particular, I will focus on (1) systems interfaces using which software can reason about hardware inefficiencies; and (2) architectural abstractions using which software can avoid hardware inefficiencies. Finally, I will conclude by describing my ongoing and future research on democratizing hardware/software co-design to enable efficiency across the systems stack. Location: CoRE 301 Committee: |
| Start Date: 21 Mar 2023; Start Time: 10:30AM - 11:30AM Title: AI for Market and Policy Design: Integrating Data, Algorithms, and Economic Modeling Bio: Xintong Wang is a postdoctoral fellow at Harvard University, School of Engineering and Applied Sciences, working with David Parkes. Her research interests lie in understanding agent incentives and behaviors for the efficient design of multi-agent systems, using tools from AI and economics.
Xintong received her Ph.D. in Computer Science from the University of Michigan, advised by Michael Wellman. She has worked as a research intern at Microsoft Research and J.P. Morgan AI Research. She was selected as a Rising Star in EECS by UIUC in 2019 and a Rising Star in Data Science by the University of Chicago in 2022. Previously, Xintong received her B.S. with honors from Washington University in St. Louis in 2015. Speaker: Abstract: Today's markets have become increasingly algorithmic, with participants (i.e., agents) using algorithms to interact with each other at an unprecedented complexity, speed, and scale. Prominent examples of such algorithms include dynamic pricing algorithms, recommender systems, advertising technology, and high-frequency trading. Despite their effectiveness in achieving individual goals, the algorithmic nature poses challenges in designing economic systems that can align individual behavior to social objectives.This talk will highlight our work that tackles these challenges using tools from AI and economics, towards a vision of constructing efficient and healthy market-based, multi-agent systems. I will describe how we combine machine learning with economic modeling to understand strategic behaviors observed in real-world markets, analyze incentives behind such behaviors under game-theoretic considerations, and reason about how agents will behave differently in the face of new designs or environments. I will discuss the use of our method in two settings: (1) understanding and deterring manipulation practices in financial markets and (2) informing regulatory interventions that can incentivize a platform (e.g., Uber Eats) to act, in the forms of fee-setting and matching, to promote the efficiency, user diversity, and resilience of the overall economy. I will conclude by discussing future directions (e.g., model calibration, interpretability, scalability, and behavioral vs. rational assumptions) in using AI for the modeling and design of multi-agent systems. Location: Core 301 Committee: |
| Start Date: 22 Mar 2023; Start Time: 10:30AM - 11:30AM Title: Towards Inclusive and Equitable Language Technologies Bio: Malihe Alikhani is an Assistant Professor of computer science in the School of Computing and Information at the University of Pittsburgh. She earned her Ph.D. in computer science with a graduate certificate in cognitive science from Rutgers University in 2020 under the supervision of Prof. Matthew Stone. Her research interests center on using representations of communicative structure, machine learning, and cognitive science to design equitable and inclusive NLP systems for critical applications such as education, health, and social justice. Her work has received multiple best paper awards at ACL 2021, UAI2022, INLG2021, and UMAP2022 and has been supported by DARPA, NIH, Google, and Amazon. Speaker: Abstract: With the increasing deployment of language technologies to users, the need for a deeper understanding of the impact of natural language processing models on our society and user behaviors has grown. Designing culturally responsible, equitable, and inclusive language technologies that can benefit a diverse population is ever more important. Toward this goal, I present two directions: 1) Discourse-aware models for more inclusive social media moderation methods, and 2) Equitable machine learning frameworks for multimodal communication. Finally, I describe my research vision: building inclusive and collaborative communicative systems by leveraging the cognitive science of language use alongside formal machine learning methods. Location: CBIM 22 Committee: |
| Start Date: 23 Mar 2023; Start Time: 10:30AM - 12:00PM Title: Designing Formally Correct Intermittent Systems Bio: Milijana Surbatovich is a PhD Candidate in the Electrical and Computer Speaker: Abstract: "Extreme edge computing" is an emerging computing paradigm targeting application domains like medical wearables, disaster-monitoring tiny satellites, or smart infrastructure. This paradigm brings sophisticated sensing and data processing into an embedded device's deployment environment, enabling computing in environments that are too harsh, inaccessible, or dense to support frequent communication with a central server. Batteryless, energy harvesting devices (EHDs) are key to enabling extreme edge computing; instead of using batteries, which may be too costly or even impossible to replace, they can operate solely off energy collected from their environment. However, harvested energy is typically too weak to power a device continuously, causing frequent, arbitrary power failures that break traditional software and make correct programming difficult. Given the high assurance requirements of the envisioned application domains, EHDs must execute software without bugs that could render the device inoperable or leak sensitive information. While researchers have developed intermittent systems to support programming EHDs, they rely on informal, undefined correctness notions that preclude proving such necessary correctness and security properties.My research lays the foundation for designing formally correct intermittent systems that provide correctness guarantees. In this talk, I show how existing correctness notions are insufficient, leading to unaddressed bugs. I then present the first formal model of intermittent execution, along with correctness definitions for important memory consistency and timing properties. I use these definitions to design and implement both the language abstractions that programmers can use to specify their desired properties and the enforcement mechanisms that uphold them. Finally, I discuss my future research directions in intermittent system security and leveraging formal methods for full-stack correctness reasoning. Location: CoRE 301 Committee: |
| Start Date: 24 Mar 2023; Start Time: 10:30AM - 11:30AM Title: Towards understanding and defending against algorithmically curated misinformation Bio: Prerna Juneja is a social computing researcher at the University of Washington. She combines data-driven techniques, and human-centered design processes with large-scale real-world deployments to understand and then build defenses against problematic online phenomena. Prerna’s research has won multiple awards at human-computer interaction venues, such as ACM CHI, and ACM CSCW, and has been funded by the prestigious Social Data Research and Dissertation Fellowship. Her work has also received widespread press coverage by notable news channels, such as The Guardian, Al Jazeera, Sky News, and Seattle Times. Speaker: Abstract: Search engines and social media platforms are an indispensable part of our lives. Despite their increasing popularity, their search, ranking, and recommendation algorithms remain a black box to the users. The relevance of results produced by these search engines is driven by market factors and not by the quality of the content of those results. There is no guarantee that the information presented to people on online platforms is credible. To make matters worse, there are increasing concerns that online platforms amplify inaccurate information, making it easily accessible via search results and recommendations. My research takes a step towards understanding and designing defenses against algorithmically curated and amplified misinformation. In this talk, I will first present the results of a series of algorithmic audits I performed on online platforms to determine the extent to which algorithms contribute to the spread of misinformation and under which conditions they do so. Second, I'll present the design of an online system that aims to monitor an online platform for misinformation, developed in collaboration with several fact-checking organizations. Finally, I will discuss the opportunities in the space of algorithm auditing and the various ways in which we can redress the harm caused by the algorithms. Location: Core 301 Committee: |
| Start Date: 27 Mar 2023; Start Time: 10:30AM - 11:30AM Title: Distance-Estimation in Modern Graphs: Algorithms and Impossibility Bio: Nicole Wein is a Simons Postdoctoral Leader at DIMACS at Rutgers University. Previously, she obtained her Ph.D. from MIT advised by Virginia Vassilevska Williams. She is a theoretical computer scientist and her research interests include graph algorithms and lower bounds including in the areas of distance-estimation algorithms, dynamic algorithms, and fine-grained complexity. Speaker: Abstract: The size and complexity of today's graphs present challenges that necessitate the discovery of new algorithms. One central area of research in this endeavor is computing and estimating distances in graphs. In this talk I will discuss two fundamental families of distance problems in the context of modern graphs: Diameter/Radius/Eccentricities and Hopsets/Shortcut Sets. The best-known algorithm for computing the diameter (largest distance) of a graph is the naive algorithm of computing all-pairs shortest paths and returning the largest distance. Unfortunately, this can be prohibitively slow for massive graphs. Thus, it is important to understand how fast and how accurately the diameter of a graph can be approximated. I will present tight bounds for this problem via conditional lower bounds from fine-grained complexity. Secondly, for a number of settings relevant to modern graphs (e.g. parallel algorithms, streaming algorithms, dynamic algorithms), distance computation is more efficient when the input graph has low hop-diameter. Thus, a useful preprocessing step is to add a set of edges (a hopset) to the graph that reduces the hop-diameter of the graph, while preserving important distance information. I will present progress on upper and lower bounds for hopsets. Location: Core 301 Committee: |
| Start Date: 27 Mar 2023; Start Time: 02:00PM - 04:00PM Title: Bioinspired Neuromorphic Motion Control for Robots and Animated Characters Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Mridul Aanjaneya (Co-Adviser) Professor Konstantinos Michmizos (Co-Adviser) Professor Kostas Bekris Professor Abdeslam Boularias Professor Tamar Shinar (UC Riverside)
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| Start Date: 27 Mar 2023; Start Time: 04:00PM - 06:00PM Title: Diffusion Guided Image Generator Domain Adaptation Bio: Speaker: Abstract: See above Location: CBIM 22 Committee: Professor Ahmed Elgammal (Advisor) Professor Dimitris Metaxas Professor Hao Wang Professor Mario Szegedy
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| Start Date: 28 Mar 2023; Start Time: 10:30AM - 11:30AM Title: Navigating the Challenges of Algorithmic Decision Making: Fair and Robust Automated Systems for Low-Resource Communities Bio: Arpita Biswas is currently a Research Associate at the Harvard T.H. Chan School of Public Health. Prior to this, she was a CRCS Postdoctoral Fellow at the John A. Paulson School of Engineering and Applied Sciences, Harvard University. She earned her Ph.D. degree from the Department of Computer Science and Automation (CSA), Indian Institute of Science (IISc). She has been awarded the Best Thesis Prize by the Indian Academy of Engineering (INAE) 2021, Best Thesis Award by the Department of CSA, IISc (2020-2021), a Google Ph.D. Fellowship (2016-2020), and a GHCI scholarship (2018). She has been recognized as a Rising Star in STOC 2021 and in the Trustworthy ML Seminar Series for her contribution to algorithms for fair decision-making. Her broad areas of interest include Algorithmic Game Theory, Optimization, and Machine Learning. She has worked on a wide range of problems, including fair resource allocation, health intervention planning, multi-agent learning, and robust sequential decision making. More details about her can be obtained at https://sites.google.com/view/arpitabiswas/. Speaker: Abstract: Automated decision-making systems play an increasingly important role in many societal decisions, such as health intervention planning, resource allocation, loan approvals, and criminal risk assessments. However, ensuring the responsible use of these systems is a challenging problem, especially for under-represented and low-resource communities. In this talk, I’ll present my research on fair and robust algorithms under resource limitations and other problem-specific constraints. The talk will cover two main themes: (1) Fair decision making in allocation and recommendation: Fairness is an important consideration in scenarios where a limited set of discrete resources is distributed among several agents, each having their own preferences. Two well-studied fairness notions in this context are envy-freeness up to one item (EF1) and maximin share (MMS). I have investigated the existence of these fairness notions under various constrained settings and developed algorithms that satisfy these fairness criteria. Further, I have used these solution concepts to quantify and promote fairness in two-sided markets (such as Netflix and Spotify) comprising customers on one side, and producers of goods/services on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the home-screen results according to the personalized preferences of users. However, our investigation reveals that such customer-centric recommendations may lead to unfair distribution of exposure among the producers, who may depend on such platforms to earn a living. I established that the two-sided fair recommendation problem can be reduced to the problem of constrained fair allocation of indivisible goods. I developed an algorithm, FairRec, that ensures maximin threshold guarantee in the exposure for a majority of the producers, and EF1 fairness for all the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring only a marginal reduction in overall recommendation quality. (2) Robust sequential intervention planning: In many public health settings, it is important to provide interventions to ensure that patients adhere to health programs, such as taking medications and periodic health checks. This is extremely crucial among low-income communities who have limited access to preventive care information and healthcare facilities. In India, a non-profit called ARMMAN provides free automated voice messages to spread preventive care information among pregnant women. One of the key challenges is to ensure that the enrolled women continue listening to the voice messages throughout their pregnancy and after childbirth. Disengagements are detrimental to their health since they often have no other source for receiving timely healthcare information. While systematic interventions, such as scheduling in-person visits by healthcare workers, can help increase their listenership, interventions are often expensive and can be provided to only a small fraction of the enrolled women. I model this as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention provided to them. I developed a robust algorithm to tackle the uncertainty in transition dynamics and can potentially reduce the number of missed voice messages by 50%. I will conclude by delving into the best practices for responsible automated decision-making and discussing future research directions. I aim to showcase my overarching vision of fostering fairness, robustness, and scalability in the realm of automated decision-making through collaboration and continuous innovation. Location: Core 301 Committee: |
| Start Date: 29 Mar 2023; Start Time: 12:15PM - 01:45PM Title: Software Quality Assessment via Specification Synthesis Bio: Juan Zhai is an Assistant Teaching Professor in the Department of Computer Science at Rutgers University. Previously, she was a Postdoctoral Research Associate, working with Prof. Xiangyu Zhang in the Department of Computer Science at Purdue University. She also worked as a tenure-track Assistant Professor at Nanjing University, where she obtained her Ph.D. degree. Her research interests lie in software engineering, natural language processing, and security, focusing on specification synthesis and enforcement. She is the recipient of the Distinguished Paper Award of USENIX Security 2017 and the Outstanding Doctoral Student Award in NASAC 2016. Speaker: Abstract: Program specifications provide clear and precise descriptions of behaviors of a software system, which serves as a blueprint for its design and implementation. They help ensure that the system is built correctly and the functions work as intended, making it easier to troubleshoot, modify, and verify the system if needed. NIST suggests that the lack of high-quality specifications is the most common cause of software project failure. Nowadays, successful projects have an equal or even higher number of specifications than code (counted by lines).In this talk, I will present my research on synthesizing both informal and formal specifications for software systems. I will explain how we use a combination of program and natural language semantics to automatically generate informal specifications, even for native methods without implementation in Java which previous methods could not handle. By leveraging the generated specifications, we successfully detect many code bugs and code-comment inconsistencies. Additionally, I will describe how we derive formal specifications from natural language comments using a search-based technique. The generated formal specifications have been applied to facilitate program analysis for existing tools. They have been shown to greatly improve the capabilities of these tools, by detecting many new information leaking paths and reducing false alarms in testing. Overall, the talk will highlight the importance of program specifications in software engineering and demonstrate the potential of our techniques to improve the development and maintenance of software systems. Location: Core 301 Committee: |
| Start Date: 31 Mar 2023; Start Time: 10:30AM - 11:30AM Title: Responsible AI via Responsible Large Language Models Bio: Sharon is a 5th-year Ph.D. candidate at the University of California, Santa Barbara, where she is advised by Professor William Wang. Her research interests lie in natural language processing, with a focus on Responsible AI. Sharon’s research spans the subareas of fairness, trustworthiness, and safety, with publications in ACL, EMNLP, WWW, and LREC. She has spent summers interning at AWS, Meta, and Pinterest. Sharon is a 2022 EECS Rising Star and a current recipient of the Amazon Alexa AI Fellowship for Responsible AI. Speaker: Abstract: Large language models have advanced the state-of-the-art in natural language processing and achieved success in tasks such as summarization, question answering, and text classification. However, these models are trained on large-scale datasets, which may include harmful information. Studies have shown that as a result, the models exhibit social biases and generate misinformation after training. In this talk, I will discuss my work on analyzing and interpreting the risks of large language models across the areas of fairness, trustworthiness, and safety. I will first describe my research in the detection of dialect bias between African American English (AAE) vs. Standard American English (SAE). The second part will investigate the trustworthiness of models through the memorization and subsequent generation of conspiracy theories. The final part will discuss recent work in AI safety regarding text that may lead to physical harm. I will conclude my talk with discussions of future work in the area of Responsible AI. Location: Core 301 Committee: |
| Start Date: 03 Apr 2023; Start Time: 10:30AM - 12:00PM Title: Building Robust Systems Through System-Memory Co-Design Bio: Minesh Patel is a recent Ph.D. graduate from ETH Zurich. His thesis work focused on overcoming performance, reliability, and security challenges in the memory system. In particular, his dissertation identifies and addresses new challenges for system-level error detection and mitigation targeting memory chips with integrated error correcting codes (ECC). He also worked collaboratively on understanding and solving the RowHammer vulnerability, near-data processing, efficient virtual memory management, and new hardware security primitives. Throughout his graduate career, Minesh's contributions have been recognized with several honors, including DSN'19 and MICRO'20 Best Paper Awards, the William Carter Dissertation Award in Dependability, the ETH Doctoral Medal, and induction into the ISCA Hall of Fame. Minesh has also been actively involved as a teaching assistant and research mentor for undergraduate and graduate students, several of whose research projects resulted in successful publications. Minesh is passionate about addressing robustness and security design challenges for current and emerging computing systems (e.g., autonomous systems, quantum computers) and is committed to teaching and education. Speaker: Abstract: Main memory (DRAM) plays a central role in shaping the performance, reliability, and security of a wide range of modern computing systems. Unfortunately, worsening DRAM scaling challenges (e.g., increasing single-bit error rates, growing RowHammer vulnerability) are a significant threat to building robust systems. In this talk, I will discuss our recent efforts to understand and overcome the system-wide dependability consequences of DRAM on-die error-correcting codes (on-die ECC), a self-contained proprietary error-mitigation mechanism that is prevalent within modern DRAM chips and widely employed throughout computing systems today. Through a combination of real-chip experiments, statistical analyses, and simulation studies, I will: (i) show that on-die ECC obfuscates the statistical properties of main memory errors in a manner specific to the on-die ECC implementation used by a given chip and (ii) build a detailed understanding of how this obfuscation occurs, what its consequences are, and how those consequences can be overcome through system-memory co-design. Finally, I will discuss future research directions that explore practical cross-stack solutions for building robust next-generation computing systems. Location: CoRE 301 Committee: |
| Start Date: 04 Apr 2023; Start Time: 10:30AM - 11:30AM Title: Building Better Data-Intensive Systems Using Machine Learning Bio: Ibrahim Sabek is a postdoc at MIT and an NSF/CRA Computing Innovation Fellow. He is interested in Speaker: Abstract: Database systems have traditionally relied on handcrafted approaches and rules to store large-scale data and process user queries over them. These well-tuned approaches and rules work well for the general-purpose case, but are seldom optimal for any actual application because they are not tailored for the specific application properties (e.g., user workload patterns). One possible solution is to build a specialized system from scratch, tailored to each application's needs. Although such a specialized system is able to get orders-of-magnitude better performance, building it is time-consuming and requires a substantial manual effort. This pushes the need for automated solutions that abstract system-building complexities while getting as close as possible to the performance of specialized systems.In this talk, I will show how we leverage machine learning to instance-optimize the performance of query scheduling and execution operations in database systems. In particular, I will show how deep reinforcement learning can fully replace a traditional query scheduler. I will also show that—in certain situations—even simpler learned models, such as piece-wise linear models approximating the cumulative distribution function (CDF) of data, can help improve the performance of fundamental data structures and execution operations, such as hash tables and in-memory join algorithms. Location: CoRE 301 Committee: |
| Start Date: 06 Apr 2023; Start Time: 10:30AM - 11:30AM Title: Optimization When You Don’t Know the Future Bio: Roie is a Fulbright Postdoctoral Fellow at Tel Aviv University, working with Niv Buchbinder. He received his PhD in Algorithms, Combinatorics and Optimization (ACO) from Carnegie Mellon University where he was advised by Anupam Gupta. Before that, he was a research engineer at the Allen Institute for AI in Seattle, and before that he received bachelor degrees in math and computer science from Brown University. Roie's research spans approximation algorithms, algorithms for uncertain environments, and submodular optimization (the discrete cousin of convex optimization). Speaker: Abstract: Discrete optimization is a powerful toolbox used ubiquitously in computer science and beyond; yet, for many applications, it is unrealistic to expect a complete and accurate description of the problem at hand. How should we approach solving problems when we are uncertain about the input? In this talk I will survey my research on algorithms under uncertainty, which is a framework for answering such questions. I will talk about algorithmic models that try to capture different kinds of uncertainty in optimization problems, the interplay between computational hardness and information, and applications to a variety of common algorithmic tasks.My work has focused on three different kinds of uncertain environments: (a) Online settings where the input is revealed piecemeal, and the algorithm must commit to irrevocable decisions as it maintains feasibility. (b) Dynamic settings where the input changes over time, and the goal is to maintain a feasible solution that moves as little as possible between updates. (c) Streaming settings where the input is too large to hold in memory all at once, and the algorithm must compute a solution with only limited memory after few sequential passes over the data. An important motif throughout my research is the study of submodular functions, which are a natural discrete analog of convex/concave functions Location: Core 301 Committee: |
| Start Date: 06 Apr 2023; Start Time: 03:30PM - 05:00PM Title: Enhancing Language Models with Logical Reasoning and Automatic Error Analysis Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Yongfeng Zhang (Advisor) Professor He Zhu Professor Hao Wang Professor Peng Zhang
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| Start Date: 10 Apr 2023; Start Time: 10:30AM - 11:30AM Title: Gaps in My Research Bio: Bender is Professor and David R. Smith Leading Scholar in Computer Science at Stony Brook University. He was Founder and Chief Scientist at Tokutek, Inc, an enterprise database company, which was acquired byPerconain 2014. Bender's research interests span the areas of data structures and algorithms, I/O-efficient computing, scheduling, and parallel computing. He has coauthored over 180 articles on these and other topics. He has won several awards, including an R&D 100 Award, a Test-of-Time Award, a Distinguished Paper Award, two Best Paper Awards, and five awards for graduate and undergraduate teaching.
Bender received his B.A. in Applied Mathematics from Harvard University in 1992 and obtained a D.E.A. in Computer Science from the EcoleNormaleSuperieure de Lyon, France in 1993. He completed a Ph.D. on Scheduling Algorithms from Harvard University in 1998. He has held Visiting Scientist positions at both MIT and King's College London. He is a Fellow of the European Association for Theoretical Computer Science (EATCS). Speaker: Abstract: In my first computer science course, we learned that insertion sort runs in $O(n^2)$ time---each insertion into the array takes time $O(n)$ and there are $n$ insertions. I distinctly remember asking, "why not do what librarians do? Why not leave gaps in the array in anticipation of future insertions?" Some years later, I would find the answer to this question---adding gaps to insertion sort improves its running time to $O(n \log n)$. This technique of strategically leaving gaps in arrays to support future insertions is surprisingly powerful. I'll explain how leaving gaps leads to a general approach for designing platform-independent data structures. I'll also present two recent theoretical breakthroughs: how we (1) solved a 40-year-old problem on how efficiently one can maintain a dynamic set of sorted items in an array, and (2) overturned 60-year-old conventional wisdom on the performance of linear-probing hash tables. Throughout the talk, I'll emphasize the surprising bi-directional bridge between algorithms and real-world systems building. Location: Core 301 Committee: |
| Start Date: 11 Apr 2023; Start Time: 12:00PM - 02:00PM Title: Towards Generalized Modeling for Physics-based Simulation in Computer Graphics Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Mridul Aanjaneya Professor Dimitris Metaxas Professor Abdeslam Boularias Professor Bo Zhu (Dartmouth College)
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| Start Date: 12 Apr 2023; Start Time: 01:30PM - 03:00PM Title: Programmatic Reinforcement Learning Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Prof. He Zhu (advisor) Prof. Shiqing Ma Prof. Srinivas Narayana Prof. Qiong Zhang
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| Start Date: 14 Apr 2023; Start Time: 10:00AM - 12:00PM Title: Integrate Logical Reasoning and Machine Learning for Decision Making Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Yongfeng Zhang (Advisor) Professor Hao Wang Professor Dong Deng Professor Sudarsan Kannan
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| Start Date: 19 Apr 2023; Start Time: 03:00PM - 04:30PM Title: CrossPrefetch: Accelerating I/O Prefetching for Modern Storage Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Sudarsan Kannan (Advisor) Professor Srinivas Narayana Professor Badri Nath Professor Karthick CS Professor Parashar Manish (University of Utah)
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| Start Date: 20 Apr 2023; Start Time: 10:30AM - 11:30AM Title: Lift-and-Project for Statistical Machine Learning Models Bio: My research interests are in theoretical computer science, cryptography and data privacy, and machine learning theory. I am also interested in understanding the interfaces between these areas. I have been with the faculty of the Department of Management Science as an assistant and associate professor since September 2015. Since then I have cosupervised Hafiz Asif, a Ph.D. student in my department and now an assistant professor at Hofstra University. Currently, I am also the advisor of Nathaniel Hobbs whose expected graduation from the Ph.D. program is in August 2023. Hafiz did his Ph.D. in the theoretical foundations of Data Privacy. Nathaniel is doing his Ph.D. in problems in the intersection of Machine Learning and Cryptography, in particular in obfuscating and interpreting deep networks. Before Rutgers, I was (February 2010 - July 2015) an assistant professor at Andrew Yao's Institute, where four Ph.D. students graduated under my direct supervision (I was habilitated/Ph.D. supervisor of the duration of my appointment at Tsinghua). Bangsheng Tang did his Ph.D. with me in proof complexity and is now with Facebook Research, Hao Song did his PhD with me in communication complexity and is now an engineer at Pony.AI, Guang Yang did his PhD with me in cryptography and is an assistant professor in the Chinese Academy of Sciences (Institute of Computing Technology), and Shiteng Chen did his Ph.D. with me in circuit complexity and is now an associate professor in the Chinese Academy of Sciences (Institute of Software). I also supervised numerous diploma and MSc theses. These students continued their PhDs in Computer Science at Princeton, Harvard, and CMU and are now postdoctoral fellows, research assistant professors, and assistant professors at CMU, UPenn, and elsewhere. Speaker: Abstract: In supervised learning, the prediction accuracy is critically bounded by learning errors. We introduce Lift-and-Project (LnP), a meta algorithm for probabilistic models that boosts multi-class classification accuracy. Unlike previous learning error reduction methods, LnP maps each class into a number of new classes and learns new class distributions "lifted" to a higher dimension. Specifically, instead of estimating the probability of a class c given an instance x, we estimate the probability of (c,c') given x, where (c,c') indicates that c is more likely to be the correct label for x than c', and c' encodes errors of the standard model. By marginalizing the new distributions for c, we "project" the lifted model back to the form of the original problem. We prove that in principle our method reduces the learning error exponentially. Experiments demonstrate significant improvements in prediction accuracy on standard datasets for discriminative and generative models. Location: Core 301 Committee: |
| Start Date: 20 Apr 2023; Start Time: 04:00PM - 06:00PM Title: Unsupervised Learning of Cardiac Wall Motion from Imaging Sequences Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Dimitri Metaxas (Advisor) Professor Yongfeng Zhang Professor Hao Wang Professor Richard Martin
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| Start Date: 21 Apr 2023; Start Time: 11:00AM - 12:30PM Title: Synthesizing Program-guided Machine Learning Models Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor He Zhu (Advisor) Professor Yongfeng Zhang Professor Santosh Naragakatte Professor Konstantinos Michmizos
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| Start Date: 24 Apr 2023; Start Time: 02:00PM - 04:00PM Title: Unlocking Artificial Intelligent Video Understanding through Object-Centric Relational Reasoning Bio: Speaker: Abstract: See above Location: CBIM 22 Committee: Professor Mubbasir Kapadia (Chair) Professor Vladimir Pavlovic Professor Dimitris Metaxas Dr. Iqbal Mohomed (Tornoto AI Research Centre) |
| Start Date: 27 Apr 2023; Start Time: 01:00PM - 03:00PM Title: Learning Explicit Shape Abstractions with Deep Deformable Models Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Dimitris Metaxas (Advisor) Professor Yongfeng Zhang Professor Konstantinos Michmizos Professor Jie Gao
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| Start Date: 27 Apr 2023; Start Time: 04:00PM - 06:00PM Title: Leveraging Powerful Attention Mechanisms for Biological Image Segmentation Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Dimitris Metaxas (Chair) Professor Konstantinos Michmizos Professor Yongfeng Zhang Professor Aaron Bernstein
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| Start Date: 09 May 2023; Start Time: 10:00AM - 12:00PM Title: Efficient Quantum Circuit Compilation with Permutable Operators through a Time-Optimal SWAP Insertion Approach Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Zheng Zhang (Advisor) Professor Yipeng Huang Professor Mario Szegedy Professor Casimir Kulikowski
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| Start Date: 10 May 2023; Start Time: 01:30PM - 03:30PM Title: Multi-pass Semi-streaming Lower Bounds for Approximating Maximum Matching Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Sepehr Assadi (Advisor) Professor Aaron Bernstein Professor Mike Saks Professor Yongfeng Zhang
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| Start Date: 10 May 2023; Start Time: 04:00PM - 06:00PM Title: Visual Learning In-the-Wild with Limited Supervision Bio: Speaker: Abstract: See above Location: CBIM 22 Committee: Professor Vladimir Pavlovic (Chair) Professor Yongfeng Zhang Professor Hao Wang Professor Adriana Kovashka (University of Pittsburgh)
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| Start Date: 12 May 2023; Start Time: 03:00PM - 05:00PM Title: Motion Planning and System Identification for Reliable Robot Actions Bio: Speaker: Abstract: See above Location: SPR-403 (1 Spring Street, New Brunswick, NJ) Committee: Professor Kostas Bekris (Advisor) Professor Abdeslam Boularias Professor Mridul Aanjaneya Professor Yipeng Huang
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| Start Date: 12 May 2023; Start Time: 04:00PM - 05:30PM Title: Skeleton-Based Isolated Sign Recognition using Graph Convolutional Networks Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Dimitris N. Metaxas (Advisor) Professor Konstantinos Michmizos Professor Vladimir Pavlovic Professor Zheng Zhang
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| Start Date: 15 May 2023; Start Time: 11:00AM - 01:00PM Title: Cyber-Physical Systems for Logistics Delivery Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Desheng Zhang (Advisor) Professor Hao Wang Professor Dong Deng Professor Xiong Fan
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| Start Date: 16 May 2023; Start Time: 09:00AM - 11:00AM Title: Cyber-Physical Systems for Location-based Services Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Desheng Zhang (Advisor) Professor Yongfeng Zhang Professor Jie Gao Professor Jingjin Yu
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| Start Date: 16 May 2023; Start Time: 10:30AM - 12:00PM Title: Cyber Physical Systems for Urban Mobility Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Desheng Zhang (Advisor) Professor Yongfeng Zhang Professor Deng Dong Professor Karl Stratos
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| Start Date: 17 May 2023; Start Time: 09:00AM - 11:00AM Title: Coherence as a Key Ingredient to Learn Effective Communication Strategies Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Matthew Stone (Chair) Professor Yongfeng Zhang Professor Karl Stratos Professor Matthew Purver (Queen Mary, University of London)
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| Start Date: 29 May 2023; Start Time: 11:00AM - 12:30PM Title: Defending against Backdoor Attacks on Deep Neural Networks Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Shiqing Ma (Advisor) Professor Dimitris Metaxas Professor Hao Wang Professor Professor Sepehr Assadi
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| Start Date: 05 Jun 2023; Start Time: 01:30PM - 03:00PM Title: Scaling Stateful Applications with Adaptive Scheduling Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Sudarsan Kannan (Advisor) Professor Richard Martin Professor Srinivas Ganapathy Professor James Abello
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| Start Date: 14 Jun 2023; Start Time: 10:00AM - 12:00PM Title: Context-Sensitive Narrative Generation for Virtual Populations and Application to Human-Building Interaction Bio: Speaker: Abstract: See above Location: Virtual Committee: Professor Mubbair Kapadia (Chair) Professor Mridul Aanjaneya Professor Jingjin Yu Professor Nuria Pelechano (Polytechnic University of Catalonia)
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| Start Date: 19 Jun 2023; Start Time: 04:00PM - 06:00PM Title: Some Problems on Multi-Sensor Layout Optimization Bio: Speaker: Abstract: See above Location: Virtual Committee: Prof. Jingjin Yu (Chair) Prof. Kostas Bekris Prof. Abdeslam Boularias Dr. Zherong Pan (Tencent America)
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| Start Date: 20 Jul 2023; Start Time: 04:00PM - 06:00PM Title: The Power of Low Associativity Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Martin Farach-Colton (Chair) Professor Aaron Bernstein Professor Sepehr Assadi Professor Dominik Kempa (Stonybrook University)
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| Start Date: 01 Aug 2023; Start Time: 12:00PM - 02:00PM Title: Program Compilation and Optimization Enhancement Through Graph Theoretical Methods Bio: Speaker: Abstract: See above Location: Virtual Committee: Professor Zheng Zhang (Chair) Professor Mario Szegedy Professor Ulrich Kremer Professor Fred Chong (University of Chicago)
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| Start Date: 03 Aug 2023; Start Time: 12:00PM - 02:00PM Title: Differentially Private Auditing and Monitoring Bio: Speaker: Abstract: See above Location: CoRE 301 Committee: Professor Anand Sarwate (Chair) Professor Santosh Nagarakatte Professor Rebecca Wright (Barnard College) Professor David Cash (University of Chicago
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| Start Date: 03 Aug 2023; Start Time: 01:00PM - 03:00PM Title: Multi-Object Manipulation Leveraging Object Dependencies Bio: Speaker: Abstract: See above Location: CoRE 305 Committee: Professor Kostas Bekris (Advisor) Professor Jingjin Yu Professor Matthew Stone Professor Jie Gao
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| Start Date: 21 Aug 2023; Start Time: 10:00AM - 12:00PM Title: Context-Sensitive Narrative Generation for Virtual Populations and Application to Human-Building Interaction Bio: Speaker: Abstract: See above Location: Virtual Committee: Professor Mubbasir Kapadia (Chair) Professor Mridul Aanjaneya Professor Jingjin Yu Professor Nuria Pelechano (Polytechnic University of Catalonia)
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| Start Date: 22 Aug 2023; Start Time: 03:00PM - 05:00PM Title: Self-Supervised Object-Centric Representations Learning of Computer Vision and Language Understanding Models Bio: Speaker: Abstract: See above Location: CoRe 301 and Virtual Committee: Professor Gerard De Melo (Chair) Professor Matthew Stone Professor Yongfeng Zhang Professor Daniel Khashabi (Johns Hopkins University)
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| Start Date: 31 Aug 2023; Start Time: 01:00PM - 03:00PM Title: Meta Complexity - Connections to One-way functions and Zero Knowledge protocols Bio: Speaker: Abstract: See above Location: CoRE 305 and Zoom Committee: Professor Eric Allender (Chair) Professor Mike Saks Professor Sepehr Assadi Professor Valentine Kabinets (Simon Fraser University)
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| Start Date: 08 Sep 2023; Start Time: 04:00PM - 06:00PM Title: Image Generation for Healthcare Applications Bio: Speaker: Abstract: The rapid advancement of artificial intelligence (AI) and machine learning (ML) techniques has opened new horizons for their application in healthcare, notably in the domain of image generation. The capacity to synthetically generate medical images can aid in various areas, including model training, disease diagnosis, treatment planning, and patient education. Techniques such as Generative Adversarial Networks (GANs) and Diffusion Model have demonstrated significant proficiency in generating high-resolution, realistic medical images. These synthetically generated images can bolster the available dataset, especially in cases where real medical images are scarce or privacy concerns inhibit sharing. This can be especially crucial in rare disease diagnosis where sample images may be limited. Moreover, generating images helps improving the resolution of medical images, which can potentially reveal missing information that is crucial to diagnosing disease. In this talk, I will review two work of mine that utilize different methods to solve the lack of data in healthcare settings. The first work tries to lift the barrier of data sharing in healthcare industry through utilizing federated learning. The second work improves the patient’s cine magnetic resonance imaging (cMRI) spatial resolution so that it could be used for downstream cardiac disease diagnostic. Both experiment results shows a superior performance compared to previous methods. Location: Core 301 Committee: Professor Dimitris Metaxas (Chair) Professor Konstantinos Michmizos Professor Hao Wang Professor David M Pennock |
| Start Date: 11 Sep 2023; Start Time: 10:30AM - 11:30AM Title: Fast Algorithms for Massive Graphs Bio: Aaron Bernstein is an assistant professor at Rutgers University working on graph algorithms. He is funded by an NSF CAREER grant on sublinear algorithms and a Google Research Grant, and he is the recipient of the 2023 Presburger Award for distinguished young scientist in theoretical computer science. Speaker: Abstract: In this talk, I will discuss my recent work on fast algorithms for graphs, especially algorithms for alternative models of computation that address the challenges of processing very large graphs. I will focus on two branches of my work. The first topic is new algorithmic tools for processing directed graphs, which are used to represent asymmetric relationships between objects; such graphs are much more difficult to process because they do not permit the natural notions of clustering that are widely used in undirected graphs. The second topic is fast algorithms for finding a large matching in a graph. Location: Core 301 Committee: |
| Start Date: 12 Sep 2023; Start Time: 10:30AM - 11:30AM Title: Trustworthy AI for Human and Science Bio: Yongfeng Zhang is an Assistant Professor in the Department of Computer Science at Rutgers University. His research interest is in Machine Learning, Machine Reasoning, Information Retrieval, Recommender Systems, Natural Language Processing, Explainable AI, and Fairness in AI. His research works appear in top-tier computer science conferences and journals such as SIGIR, WWW, KDD, ICLR, RecSys, ACL, NAACL, CIKM, WSDM, AAAI, IJCAI, TOIS, TORS, TIST, etc. His research is generously supported by funds from Rutgers, NSF, NIH, Google, Facebook, eBay, Adobe, and NVIDIA. He serves as Associate Editor for ACM Transactions on Information Systems (TOIS), ACM Transactions on Recommender Systems (TORS), and Frontiers in Big Data. He is a Siebel Scholar of the class 2015 and an NSF career awardee in 2021. Speaker: Abstract: Artificial Intelligence (AI) has been an essential part of our society, and it is widely adopted in both human-oriented tasks and science-oriented tasks. However, irresponsible use of AI techniques may bring counter-effects such as compromised user trust due to non-transparency and unfair treatment of different populations. In this talk, we will introduce our recent research on Trustworthy AI with a focus on explainability, fairness, robustness, privacy, and controllability as well as their implications, which are some of the most important perspectives to consider when building Trustworthy AI systems. We will introduce Trustworthy AI in terms of both methodology and application. On methodology, we will introduce causal and counterfactual reasoning, neural-symbolic reasoning, knowledge reasoning, explainable graph neural networks, and large language models for building Trustworthy AI systems. On application, we will cover both human-oriented tasks such as search engine, recommender systems and e-commerce, and science-oriented tasks such as molecule analysis, drug design and protein structure prediction. Location: Core 301 Committee: |
| Start Date: 15 Sep 2023; Start Time: 10:30AM - 11:30AM Title: Towards Designing Generalized Constitutive Models for Versatile Physics Simulation and Inverse Learning 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, where he was advised by Prof. Eftychios Sifakis. He obtained his Ph.D. in Computer Science from Stanford University under the supervision of Prof. Ronald Fedkiw. While at Stanford, he also worked as a consultant in the Spatial Technologies team at the Nokia Research Center for two years. His research lies at the intersection of Computer Graphics, Scientific Computing, and Computational Physics, with the overarching goal of designing scalable physics engines for applications in engineering and the physical sciences. His research is supported by the National Science Foundation. He is a recipient of the Ralph E. Powe Junior Faculty Enhancement Award 2019, sponsored by Oak Ridge Associated Universities (ORAU), and the NSF CAREER Award 2023. Speaker: Abstract: Physics simulation is an active area of research in computer graphics but has now started being used in many other fields for inverse learning purposes. Many of these applications cannot impose the assumptions that are typically used in forward simulation methods and require "generalized" models that can allow for achieving different physical behaviors by changing the values of appropriate parameters. In this talk, I will explain the steps taken by my research group for designing such generalized constitutive models. The key idea is to exploit non-local modeling techniques that have the potential to unify seemingly disjoint and complex physical processes under one umbrella. This effort has also revealed the striking promise of providing possible explanations for some real-world observations that cannot be described by existing scientific theories. Location: Core 301 Committee: |
| Start Date: 18 Sep 2023; Start Time: 10:30AM - 11:30AM Title: Tackling Mapping and Scheduling Problems for Quantum Program Compilation Bio: Zheng (Eddy) Zhang is an Associate Professor at Rutgers University. Her research is in compilers, systems, and quantum computing. A central tenet of her research is to develop efficient compiler techniques for emerging computing architectures such as many-core GPUs and quantum processing units. Her recent work focuses on the synergistic interaction between algorithms, programming languages, intermediate representation, and micro-architectures for near-term intermediate scale (NISQ) computing devices. She will be talking about mapping and scheduling problems that arise in the compilation process of quantum programs in the NISQ era. Speaker: Abstract: We are at the verge of quantum revolution. Google has demonstrated supremacy with less than 100 qubits by performing a specific calculation (on a random number generator) that is beyond reach even for the best classical supercomputer. Quantum computers may soon be able to solve large scale problems in chemistry, physics, cryptography, machine learning, and database search. However, there is a significant gap between the quantum algorithms and the physical devices that can support them. Most well-known quantum algorithms are designed with perfect hardware in mind. But hardware has constraints. A compiler framework is needed for efficiently converting quantum algorithm in high level specification to that in hardware-compliant code. This talk will focus on mapping and scheduling problems in the compilation process for superconducting quantum computers. Tackling these problems not improves the performance but also the fidelity of the quantum programs. Location: Core 301 Committee: |
| Start Date: 21 Sep 2023; Start Time: 01:30PM - 03:00PM Title: Multi-Modal Vector Query Processing Bio: Speaker: Abstract: In recent years, various machine learning models, e.g., word2vec , doc2vec, and node2vec, have been developed to effectively represent real-world objects such as images, documents, and graphs as high-dimensional feature vectors. Simultaneously, these real-world objects frequently come with structured attributes or fields, such as timestamps, prices, and quantities. Many scenarios need to jointly query the vector representations of the objects together with their associated attributes.In this talk, I will outline our research efforts in the domains of range-filtering approximate nearest neighbor search (ANNS) and the construction of all-range approximate K-Nearest Neighbor Graphs (KNNG). In the context of range-filtering ANNS, queries are characterized by a query vector and a specified range within which the attribute values of data vectors must fall. We introduce an innovative indexing methodology addressing this challenge, encompassing ANNS indexes for all the potential query ranges. Our approach facilitates the retrieval of corresponding ANNS index within the query range, thereby improving query processing efficiency. Furthermore, we design an index to take a search key range as the query input and generate a KNNG graph composed of vectors falling within that specified query range. Looking ahead, our future work aims to develop a comprehensive database management system for vector data. This system will integrate all of our indexing techniques, providing durable storage and efficient querying capabilities. Location: Core 301 Committee: Professor Dong Deng (Advisor) Professor Yongfeng Zhang Professor Amélie Marian Professor Karl Stratos |
| Start Date: 27 Sep 2023; Start Time: 03:00PM - 05:00PM Title: Hybrid CPU-GPU Architectures for Processing Large-Scale Data on Limited Hardware Bio: Speaker: Abstract: In the dynamic field of data processing, efficiently managing large-scale data, both offline and in real-time, is a growing challenge. With the limitations of hardware as a focal concern, this dissertation introduces hybrid CPU-GPU frameworks. These are designed specifically to meet the computational needs of data-intensive environments in real-time. A central feature of these designs is a unique shared-memory-space approach, which is effective in facilitating data transfers and ensuring synchronization across multiple computations. The research highlights the increasing trend towards swift processing of large-scale data. In sectors like distributed fiber optic sensing, there's a consistent demand for immediate real-time data processing. These designs combine the advantages of both CPU and GPU components, effectively handling fluctuating workloads and addressing computational challenges. Designed for optimal performance in diverse computing environments with limited hardware, the system architecture offers scalability, adaptability, and increased efficiency. Key components of the design, such as shared memory space utilization, process replication, CPU-GPU synchronization, and real-time visualization capabilities, are thoroughly analyzed to demonstrate its capability in real-time data processing. Location: Core 301 Committee: Prof. Badri Nath (Chair) Prof. Srinivas Narayana Prof. Zheng Zhang Prof. Kazem Cheshmi ( McMaster University) |
| Start Date: 28 Sep 2023; Start Time: 10:30AM - 11:30AM Title: The versatile platelet: a bridge to translational medicine Bio: Anandi Krishnan is a translational scientist and principal investigator at Stanford University School of Medicine. Dr. Krishnan’s current research focuses on transcriptional and epigenetic mechanisms of blood cell function and dysfunction in human disease. In particular, she is interested in expanding our understanding of the multifaceted function of blood platelets in cancer, inflammation, or immunity, beyond their classical role in hemostasis and thrombosis. Her work integrates omics-based discovery (from large clinical cohorts) with experimental and computational systems biology approaches toward a deeper understanding of disease mechanisms, risk stratification, and novel therapeutic strategies. Recent findings have outlined a number of heretofore unrecognized platelet mechanisms that are central to platelet response in disease.
Her interest in the field was primarily influenced by her experiences at the Duke Translational Research Institute, studying RNA-based aptamer-antidote molecules for antithrombotic therapy (laboratory of Drs. Bruce Sullenger, PhD and Richard Becker, MD) and her doctoral work at Penn State Biomedical Engineering (with Dr. Erwin Vogler, PhD) establishing the biophysical mechanisms of contact activation in blood coagulation. Funding for Anandi’s research includes her current NIH NHGRI genomic medicine career development award, MPN Research Foundation Challenge grant, multiple Stanford internal awards and NIH NCATS diversity/research re-entry award. Speaker: Abstract: Evolving evidence suggests that blood platelets have cross-functional roles beyond their traditional function in hemostasis, and therefore, that their molecular signatures may be altered in diverse settings of cancer, heart disease, metabolic or neurogenerative disorders. This lecture will present recent data from multi-omic profiling of platelets from patients with chronic bone marrow disorders (myeloproliferative neoplasms). Emphasis will be on demonstrating the translational relevance of platelet-omics and systems biology approaches, and their possible bench-to-bedside utility in patient care. Methods of platelet RNA/protein sequencing and associated analyses, and application of predictive machine learning algorithms will be discussed. Extending this work on omics-based discovery to ongoing and future research on molecular, cellular, and computational validation approaches will also be discussed. Location: Core 301 Committee: |
| Start Date: 29 Sep 2023; Start Time: 02:30PM - 03:30PM Title: Simulation of Diffusion Effects with Physics-Based Methods Bio: Speaker: Abstract: Physics-based simulation has yielded numerous vivid and realistic results as a research area in computer graphics, and the simulation of diffusion effects applies across diverse problems. In this talk, I will present two works centered on simulating diffusion effects. First, I'll discuss our introduction of the C-F diffusion model to computer graphics. This model enhances the commonly used Fick’s/Fourier’s law and allows for a finite propagation speed for diffusion. It captures characteristic visual aspects of diffusion-driven physics, such as hydrogel swelling and snowflake formation. Then, I will discuss our Lagrangian particle-based model for fracture and diffusion in thin membranes, such as aluminum foil, rubbery films, and seaweed flakes. The deformation-diffusion coupling framework generates a detailed and heterogeneous growth of fractures for both in-plane and out-of-plane motions. To the best of our knowledge, our work is the first to simulate the complex fracture patterns of single-layered membranes in computer graphics and introduce heterogeneity induced by the diffusion process, which generates more geometrically rich fracture patterns. Location: CBIM #17 Committee: Assistant Professor Mridul Aanjaneya Professor Kostas Bekris Associate Professor Abdeslam Boularias Assistant Professor Peng Zhang |
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Upcoming Events
| 21 Sep 2023; - 01:30PM - 03:00PM Multi-Modal Vector Query Processing |
| 27 Sep 2023; - 03:00PM - 05:00PM Hybrid CPU-GPU Architectures for Processing Large-Scale Data on Limited Hardware |
| 28 Sep 2023; - 10:30AM - 11:30AM The versatile platelet: a bridge to translational medicine |
Department of Computer Science