Events Feed

Start Date: 11 Apr 2019;
Start Time: 01:00PM -
Title: Inferring 3D Shapes of Unknown Rigid Objects in Clutter through Inverse Physics Reasoning

Bio:
Speaker:

Changkyu Song


Abstract:
Location: 1 Spring Street, Room 403, New Brunswick
Committee:

Prof. Abdeslam Boularias (Chair), Prof. Kostas Bekris, Prof. Jingjin Yu, Prof. Richard Martin.

Start Date: 12 Apr 2019;
Start Time: 10:00AM -
Title: Scheduling edge and in-transit computing resources for stream processing applications

Bio:
Speaker:

Ali Reza Zamani


Abstract:
Location: CoRE A (301)
Committee:

Prof. Manish Parashar (Chair), Prof. Ulrich Kremer, Prof. Srinivas Narayana, Adrian Lebre, Inria

Start Date: 16 Apr 2019;
Start Time: 01:00PM -
Title: Neural Graph Reasoning for Explainable Decision Making

Bio:
Speaker:

Yikun Xian


Abstract:
Location: CoRE A (301)
Committee:

Prof. Shan Muthukrishnan (Chair), Prof. Yongfeng Zhang, Prof. Pranjal Awasthi, Prof. Amélie Marian

Start Date: 18 Apr 2019;
Start Time: 10:30AM -
Title: Weakly Supervised Nuclei Segmentation in Histopathology Images

Bio:
Speaker:

Hui Qu


Abstract:
Location: CBIM 22
Committee:

Profl. Dimitri Metaxas (Chair), Prof. Konstantinos Michmizos, Prof. Georgios Moustakides, Prof. Badri Nath

Start Date: 22 Apr 2019;
Start Time: 10:30AM -
Title: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration

Bio:
Speaker:

Yu Yang


Abstract:
Location: CoRE 305 (B)
Committee:

Prof. Desheng Zhang (Chair), Prof. Richard Martin, Prof. Yongfeng Zhang, Prof. Mubbasir Kapadia

Start Date: 22 Apr 2019;
Start Time: 03:30PM -
Title: Grounded and Interpretable Learning-Based Models for Visual Recognition Tasks

Bio:
Speaker:

Zachary Daniels


Abstract:
Location: CBIM 22
Committee:

Prof. Metaxas (Chair), Prof. Michmizos, Prof. Moustakides, Prof. Gerasoulis

Start Date: 23 Apr 2019;
Start Time: 10:00AM -
Title: Deep learning approaches in medical image analysis focusing on MRI reconstruction

Bio:
Speaker:

Qiaoying Huang


Abstract:
Location: CBIM 22
Committee:

Prof. Dimitris Metaxas (Chair), Prof. Georgios Moustakides, Prof. Desheng Zhang, Prof. Shubhangi Saraf

Start Date: 23 Apr 2019;
Start Time: 10:30AM -
Title: Cooperation in Humans and Machines

Bio:

Dr. Patrick Shafto is the Henry Rutgers Term Chair in Data Science and Associate Professor of Mathematics and Computer Science at Rutgers University - Newark. Research in his lab focuses on theoretical and empirical foundations of cooperation and learning in humans and machines. He has received numerous honors and awards including an NSF CAREER award and his research has formed the basis for successful data science start-up companies eventually acquired by Salesforce and Tableau. His research is supported by multiple NSF directorates (EHR, CISE, SBE), DARPA, DoD, the intelligence community, and the NIH.


Speaker:

Patrick Shafto


Abstract:
Location: CoRE A 301
Committee:

Matthew Stone

Start Date: 24 Apr 2019;
Start Time: 01:30PM -
Title: Instance Segmentation of Neural Cells

Bio:
Speaker:

Jingru Yi


Abstract:
Location: CBIM 22
Committee:

Prof. Dimitris Metaxas (Chair), Prof. Georgios Moustakides, Prof. Sungjin Ahn Srinivas, Prof. Narayana Ganapathy

Start Date: 25 Apr 2019;
Start Time: 11:00AM -
Title: Tables evidence grounding

Bio:
Speaker:

Mohamed Abdellatif


Abstract:
Location: CoRE B (305)
Committee:

Prof. Ahmed Elgammal, Prof. Bahman Kalantari, Prof. Abdeslam Boularias, Prof. Gerard De Melo

Start Date: 25 Apr 2019;
Start Time: 02:00PM -
Title: Collaborative Urban-Scale Vehicle Sensing Based on Heterogeneous Fleets

Bio:
Speaker:

Xiaoyang Xie


Abstract:
Location: CoRE A (301)
Committee:

Prof. Desheng Zhang (Chair), Prof. Zheng Zhang, Prof. Yongfeng Zhang and Prof. Kostas Bekris

Start Date: 25 Apr 2019;
Start Time: 04:00PM -
Title: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks

Bio:
Speaker:

Guang Wang


Abstract:
Location: CoRE A (301)
Committee:

Prof. Desheng Zhang (Chair), Prof. Zheng Zhang, Prof. Yongfeng Zhang, Prof. Abdeslam Boularias

Start Date: 29 Apr 2019;
Start Time: 01:30PM -
Title: Optimal Topological Cycles and Their Application in Cardiac Trabeculae Restoration

Bio:
Speaker:

Pengxiang Wu


Abstract:
Location: CBIM 22
Committee:

Prof. Dimitris Metaxas (Chair), Prof. George Moustakides, Prof. Desheng Zhang, Prof. Sudarsun Kannan

Start Date: 29 Apr 2019;
Start Time: 03:30PM -
Title: Towards improved inference and generation for bidirectional generative adversarial models

Bio:
Speaker:

Yu Tian


Abstract:
Location: CBIM 22
Committee:

Prof. Dimitris Metaxas (Chair), Prof. Casimir Kulikowski, Prof. Yongfeng Zhang, Prof. Georgios Moustakides

Start Date: 30 Apr 2019;
Start Time: 10:00AM -
Title: EXIMIUS: A Measurement Framework for Explicit and Implicit Urban Traffic Sensing

Bio:
Speaker:

Zhou Qin


Abstract:
Location: CoRE B (305)
Committee:

Prof. Desheng Zhang (Chair), Prof. Richard Martin, Prof. Yongfeng Zhang, Prof. Konstantinos Michmizos.

Start Date: 30 Apr 2019;
Start Time: 01:00PM -
Title: Triangle Algorithm: A Fast Oracle for Convex Hull Membership Queries in Machine Learning and Computational Geometry Applications

Bio:
Speaker:

Yikai Zhang


Abstract:
Location: CoRE B (305)
Committee:

Prof. Bahman Kalantari (Chair), Prof. Pranjal Awasthi, Prof. William Steiger, Prof. Mridul Aanjaneya

Start Date: 02 May 2019;
Start Time: 02:00PM -
Title: Brain-morphic algorithms on Neuromorphic Hardware: Towards energy-efficient and versatile Neu-Robotics

Bio:
Speaker:

Guangzhi Tang


Abstract:
Location: CoRE A 301
Committee:

Prof. Konstantinos Michmizos (Chair), Prof. Kostas Bekris, Prof. Santosh Nagarakatte, Prof. Badri Nath

Start Date: 07 May 2019;
Start Time: 11:00AM -
Title: The Interesting Parts of Vision Happen Outside the Feedforward Pathway

Bio:

John Tsotsos is Distinguished Research Professor of Vision Science at York University. He received his doctorate in Computer Science from the University of Toronto. In 1980 he founded the Computer Vision Group at the University of Toronto, which he led for 20 years. In 2000, he moved to York University to become the Director of the Centre for Vision Research. He became the founding Director of York's Centre for Innovation in Computing in 2014. He has been a CIFAR Fellow, is an IEEE Fellow and Canada Research Chair in Computational Vision. Other honours include a 1987 inaugural Marr Prize citation, the 2006 Canadian Image Processing and Pattern Recognition Society Award for Research Excellence and Service, the 1st President’s Research Excellence Award by York University on the occasion of the University’s 50th anniversary in 2009, and the 2011 Geoffrey J. Burton Memorial Lectureship from the United Kingdom's Applied Vision Association for significant contribution to vision science. Elected as Fellow of the Royal Society of Canada in 2010, he became the first computer scientist to receive its 2015 Sir John William Dawson Medal for sustained excellence in multiple domains.


Speaker:

John Tsotsos


Abstract:
Location: CoRE Auditorium
Committee:

Dimitris Metaxas

Start Date: 07 May 2019;
Start Time: 03:00PM -
Title: Brain-informed learning models of movement primitives for neurorehabilitation robots

Bio:
Speaker:

Neelesh Kumar


Abstract:
Location: CBIM 22
Committee:

Prof. Konstantinos Michmizos (Chair), Prof. Tomasz Imielinski, Prof. Ahmed Elgammal, Prof. Srinivas Narayana

Start Date: 09 May 2019;
Start Time: 10:00AM -
Title: Performance Profilers and Debugging Tools for OpenMP programs

Bio:
Speaker:

Nader Boushehrinejad


Abstract:
Location: CoRE 305
Committee:

Prof Santosh Nagarakatte (Chair), Prof Badri Nath, Prof Srinivas Narayana, Prof Martha Kim (Columbia University)

Start Date: 09 May 2019;
Start Time: 10:30AM -
Title: A new approach for prediction of pedestrian trajectories based only on images.

Bio:
Speaker:

Blerta Lindqvist


Abstract:
Location: CoRE A 301
Committee:

Prof. Kristin Dana, Prof. Casimir Kulikowski, Prof. Kostas Bekris and Prof. Desheng Zhang

Start Date: 09 May 2019;
Start Time: 12:30PM -
Title: Towards Less Artificial Intelligence: Recurrent Spiking Neuronal-Astrocytic Networks Operating at the Edge of Chaos

Bio:
Speaker:

Vladimir Ivanov


Abstract:
Location: CBIM 22
Committee:

Professor Konstantinos Michmizos (Chair), Professor Casimir Kulikowski, Professor Thu Nguyen, Professor Eric Allender

Start Date: 09 May 2019;
Start Time: 01:00PM -
Title: Performance and Correctness Debugging Techniques for Task Parallel Programs

Bio:
Speaker:

Adarsh Yoga


Abstract:
Location: CoRE B 305
Committee:

Prof. Santosh Nagarakatte (Chair), Prof. Ulrich Kremer, Prof. Sudarsun Kannan, and Dr. Madanlal Musuvathi (Microsoft Research)

Start Date: 09 May 2019;
Start Time: 02:30PM -
Title: Automated analysis of hatching lines in drawings for attribution and authentication using convolutional neural networks

Bio:
Speaker:

Shahrzad Ziaee


Abstract:
Location: CoRE A (301)
Committee:

Prof. Ahmed Elgammal (Chair), Prof. Alex Borgida, Prof. Casimir Kulikowski, Prof. Uli Kremer

Start Date: 10 May 2019;
Start Time: 10:00AM -
Title: Scheduling edge and in-transit computing resources for stream processing applications

Bio:
Speaker:

Ali Reza Zamani Zadeh Najari


Abstract:
Location: CoRE A 301
Committee:

Prof. Manish Parashar (Chair), Prof. Ulrich Kremer, Prof. Srinivas Narayana, Prof. Adrien Lebre (IMT Atlantique)

Start Date: 14 May 2019;
Start Time: 12:00PM -
Title: Multi-Agent Narrative Generation With Commonsense Knowledge-Based Environment Modeling

Bio:
Speaker:

Xun Zhang


Abstract:
Location: CBIM 22
Committee:

Prof. Mubbasir Kapadia (Chair), Prof. Mridul Aanjaneya, Prof. Jingjin Yu, Prof. Sudarsun Kannan

Start Date: 16 May 2019;
Start Time: 02:00PM -
Title: Computational Astrocyence: Astrocytic Learning in Spiking Neural Networks

Bio:
Speaker:

Ioannis Polykretis


Abstract:
Location: CBIM 22
Committee:

Prof. Konstantinos Michmizos, Prof. Casimir Kulikowski, Prof. Dimitri Metaxas, Prof. Bahman Kalantari

Start Date: 28 May 2019;
Start Time: 03:00PM -
Title: Representation Learning for Predicting Information Spread in Dynamic Heterogeneous Networks

Bio:
Speaker:

Vahid Azizi


Abstract:
Location: CoRE B (305)
Committee:

Prof. Mubbasir Kapadia (Chair), Prof. Gerald De Melo, Prof. Mridul Aanjaneya, Prof. Aaron Bernstein

Start Date: 03 Jun 2019;
Start Time: 11:00AM -
Title: Effective Medical Image Segmentation for Osteoarthritis Analysis.

Bio:
Speaker:

Chaowei Tan


Abstract:
Location: CBIM 22
Committee:

Dimitris Metaxas (Chair), Prof. Kang Li, Prof. Jingjin Yu, Dr. Xin Dou (SenseBrain Technology Limited, Princeton)

Start Date: 13 Jun 2019;
Start Time: 09:30AM -
Title: Trajectory Interpolation and Scenario Generalization in Crowd Simulation

Bio:
Speaker:

Gang Qiao


Abstract:
Location: CoRE A (301)
Committee:

Prof. Vladimir Pavlovic (Chair), Prof. Santosh Nagarakatte, Prof. Yongfeng Zhang, Prof. Mubbasir Kapadia

Start Date: 13 Jun 2019;
Start Time: 11:00AM -
Title: Joint Representation Learning of Images and Unstructured Text with Application to Dynamic Multimodal Social Networks

Bio:
Speaker:

Hareesh Ravi


Abstract:
Location: CoRE A (301)
Committee:

Prof. Mubbasir Kapadia (Chair), Prof. Gerard De Melo, Prof. Dimitris Metaxas, Prof. Amelie Marian

Start Date: 13 Jun 2019;
Start Time: 02:00PM -
Title: Meal Image Generation from Recipe

Bio:
Speaker:

Fangda Han


Abstract:
Location: CoRE A (301)
Committee:

Prof. Vladimir Palvovic (Chair), Prof. Dimitri Metaxas, Prof. Ahmed Elgammal, Prof. Ulrich Kremer

Start Date: 14 Jun 2019;
Start Time: 09:30AM -
Title: Learning Latent Representation: A Key to Domain Adaptation and Disentangling

Bio:
Speaker:

Pritish Sahu


Abstract:
Location: CoRE A (301)
Committee:

Prof. Vladimir Pavlovic (Chair), Prof. Dimitri Metaxas, Prof. Yongfeng Zhang, Prof. Swastik Kopparty

Start Date: 21 Aug 2019;
Start Time: 10:30AM -
Title: Scalable and Congestion-aware Routing for Autonomous Mobility-On-Demand via Frank-Wolfe Optimization

Bio:

Kiril Solovey is a Postdoctoral Scholar supported by the Fulbright Scholars Program at the Autonomous Systems Lab, Department of Aeronautics & Astronautics, Stanford University, hosted by Prof. Marco Pavone. His research focuses on algorithmic aspects of robotics. He is particularly interested in the design and analysis of techniques for robot motion planning, multi-robot systems, and autonomous mobility on demand (AMoD). Prior to Stanford, Kiril was a Ph.D. student at Tel Aviv University working with Prof. Dan Halperin in the Computational Geometry Lab supported by the Clore Israel Foundation.


Speaker:

Kiril Solovey


Abstract:
Location: 1 Spring street, New Brunswick, Room 204
Committee:

Kostas Bekris

Start Date: 27 Aug 2019;
Start Time: 10:00AM -
Title: Taming Combinatorial Challenges in Clutter Removal

Bio:
Speaker:

Wei Tang


Abstract:
Location: 1 Spring St., New Brunswick, NJ 08901/Room 403
Committee:

Prof. Jingjin Yu (Chair), Prof. Kostas Bekris, Prof. Abdeslam Boularias, Prof. William Steiger

Start Date: 27 Aug 2019;
Start Time: 03:00PM -
Title: Providing Concurrency in Direct Access File Systems

Bio:
Speaker:

Yujie Ren


Abstract:
Location: CoRE B 305
Committee:

Prof. Sudarsun Kannan (Chair), Prof. Santosh Nagarakatte, Prof. Thu Nguyen, Prof. Ulrich Kremer and Prof. Jingjin Yu

Start Date: 04 Sep 2019;
Start Time: 11:00AM -
Title: New Results on Projections

Bio:
Speaker:

Guy Moshkovitz


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing

Start Date: 06 Sep 2019;
Start Time: 01:00PM -
Title: Searching Heterogeneous Personal Data

Bio:
Speaker:

Daniela Vianna


Abstract:
Location: CoRE A (301)
Committee:

Prof. Amelie Marian (Chair), Prof. Thu Nguyen, Prof. Yongfeng Zhang, and Dr. Divesh Srivastava

Start Date: 16 Sep 2019;
Start Time: 01:00PM -
Title: Parallelism-Driven Performance Analysis Techniques for Task Parallel Programs

Bio:
Speaker:

Adarsh Yoga


Abstract:
Location: CoRE A (301)
Committee:

Prof. Santosh Nagarakatte (Chair), Prof. Ulrich Kremer, Prof. Sudarsun Kannan, and Dr. Madanlal Musuvathi (Microsoft Research)

Start Date: 19 Sep 2019;
Start Time: 10:00AM -
Title: Computational Analysis of Fine Art Paintings

Bio:
Speaker:

Diana Kim


Abstract:
Location: CoRE B (305)
Committee:

Prof. Ahmed Elgammal (Chair), Prof. Michmizos, Prof. Gerard De Melo, Prof. Zhang.

Start Date: 24 Sep 2019;
Start Time: 10:30AM -
Title: Semantic Graph Convolutional Networks for 3D Human Pose Regression

Bio:
Speaker:

Long Zhao


Abstract:
Location: CBIM 22
Committee:

Prof. Dimitris N. Metaxas (Chair); Prof. Mubbasir Kapadia; Prof. Yongfeng; Zhang Prof. Dong Deng

Start Date: 25 Sep 2019;
Start Time: 10:00AM -
Title: A Model for Concurrent Priority Queue on Manycore Architectures

Bio:
Speaker:

Chaozhang Huang


Abstract:
Location: Core 305
Committee:

Professor Zheng Zhang (chair) Professor Desheng Zhang Professor Yongfeng Zhang

Start Date: 25 Sep 2019;
Start Time: 11:00AM -
Title: How to Store a Random Walk

Bio:

Huacheng Yu is an associate research scholar in the department of computer science at Princeton University. Prior to Princeton, he was a postdoc in the Theory of Computation group at Harvard University, after receiving his PhD from Stanford University in 2017.


Speaker:

Huacheng Yu


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

Start Date: 30 Sep 2019;
Start Time: 11:00AM -
Title: An Effective Graph Edge Partition Model for GPU Computing

Bio:
Speaker:

Yanhao Chen


Abstract:
Location: CoRE B (305)
Committee:

Prof. Zheng Zhang (Chair), Prof. Desheng Zhang, Prof. Ulrich Kremer , Prof. Martin Farach-Colton

Start Date: 30 Sep 2019;
Start Time: 01:30PM -
Title: Effective Medical Image Segmentation for Osteoarthritis Analysis

Bio:
Speaker:

Chaowei Tan


Abstract:
Location: CBIM 22
Committee:

Prof. Dimitris N. Metaxas (Chair),Prof. Kang Li, Prof. Jingjin Yu, Dr. Xin Dou (SenseBrain Technology Limited, Princeton)

Start Date: 02 Oct 2019;
Start Time: 09:45AM -
Title: Implicit Regularization for Optimal Sparse Recovery

Bio:
Speaker:

Varun Kanade


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DiMACS Theory of Computing Seminar

Start Date: 02 Oct 2019;
Start Time: 11:00AM -
Title: Small-loss bounds for online learning with partial information

Bio:

Thodoris Lykouris is a Postdoctoral Researcher in the Machine Learning group of Microsoft Research NYC. He recently graduated with a Ph.D. in Computer Science from Cornell University where he was advised by Eva Tardos. His research spans across the areas of machine learning, algorithms, optimization, and economics, with a particular emphasis on enhancing online decision-making in complex multi-agent systems. Thodoris is a recipient of the 2018 Google Ph.D. Fellowship on "Algorithms, Optimization, and Markets" and a finalist in the 2018 INFORMS Nicholson and 2017 Applied Probability Society Student Paper Competitions. He was a research intern at Microsoft Research Redmond (summer 2018), Google Research NYC (summer 2017), and Microsoft Research India (summer 2015). He was also a visiting student at TTI-Chicago (March-May 2018) and the Simons Institute at Berkeley (fall 2015, spring 2018). Thodoris received his Diploma from the EECS department of the National Technical University of Athens.


Speaker:

Thodoris Lykouris


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:
Speaker:

Shay Moran


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:
Speaker:

Rob Robere


Abstract:
Location: CoRE 431
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:
Speaker:

Jeroen Zuiddam


Abstract:
Location: CoRE 431
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:
Speaker:

Jarosław Błasiok


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:
Speaker:

Dor Minzer


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:

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

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


Speaker:

Mert Gurbuzbalaban


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:
Speaker:

Juba Zian


Abstract:
Location: CoRE A 301
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:
Speaker:

Sahil Singla


Abstract:
Location: CoRE 431
Committee:

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:

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


Speaker:

Yiannis Aloimonos


Abstract:
Location: CoRE A 301
Committee:

Dimitris Metaxas

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

Bio:

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

References:
https://faculty.ist.psu.edu/suh972

https://scholar.google.com/citations?user=iTtzc1UAAAAJ&hl=en


Speaker:

Dr. Sharon Xiaolei Huang


Abstract:
Location: CoRE A 301
Committee:

Dimitris Metaxas

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

Bio:
Speaker:

Shuai Han


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

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

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

Bio:

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


Speaker:

Gerard de Melo


Abstract:
Location: CoRE A 301
Committee:

Matthew Stone

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

Bio:

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


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

Matthew Stone

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

Bio:
Speaker:

Irit Dinur


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

Rutgers/DIMACS Theory of Computing Seminar

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

Bio:
Speaker:

Liu Liu


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

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

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

Bio:
Speaker:

Eduard Gibert Renart


Abstract: Due to the proliferation of the Internet of Things (IoT), the number of devices connected to the Internet is growing. These devices are generating unprecedented amounts of data at the edge of the infrastructure. Although the generated data provides great potential, identifying and processing relevant data points hidden in streams of unimportant data, and doing this in near real time, remains a significant challenge. The prevalent model of moving data from the edge to the cloud of the network is quickly becoming unsustainable. Resulting in an impact on latency, network congestion, storage cost, and privacy, limiting the potential impact of IoT. To address these challenges, this dissertation presents an IoT Edge Framework, called R-Pulsar, that extends cloud capabilities to local devices and provides a programming model for deciding what, when, where and how data get collected and processed. This thesis makes the following contributions: (1) A content- and location-based programming abstraction for specifying what data gets collected and where the data gets analyzed. (2) A rule-based programming abstraction for specifying when to trigger data-processing tasks based on data observations. (3) A programming abstraction for specifying how to split a given dataflow and place operators across edge and cloud resources. (4) An operator placement strategy that aims to minimize an aggregate cost which covers the end-to-end latency (time for an event to traverse the entire dataflow), the data transfer rate (amount of data transferred between the edge and the cloud) and the messaging cost (number of messages transferred between edge and the cloud). (5) Performance optimizations on the data-processing pipeline in order to achieve real-time performance on constrained devices.
Location: CoRE 305
Committee:

Manish Parashar (Advisor), Ulrich Kremer, Srinivas Narayana

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

Bio:
Speaker:

Zakary Littlefield


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

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

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

Bio:
Speaker:

Bingchen Liu


Abstract:
Location: CoRE 301
Committee:

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

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

Bio:
Speaker:

Arnold Filtser (Columbia)


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

Bio:

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


Speaker:

Edith Elkind, University of Oxford


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

Bio:
Speaker:

Patrick Shafto


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

Bio:

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


Speaker:

Kuldeep Meel, National University of Singapore


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

Bio:
Speaker:

Victor Amelkin (Penn) 


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

Bio:
Speaker:

Ariel Schvartzman (Princeton)


Abstract: We consider a revenue-maximizing seller with n items facing a single buyer. We introduce the notion of symmetric menu complexity of a mechanism, which counts the number of distinct options the buyer may purchase, up to permutations of the items. Our main result is that a mechanism of quasi-polynomial symmetric menu complexity suffices to guarantee a .99-approximation when the buyer is unit-demand over independent items, even when the value distribution is unbounded, and that this mechanism can be found in quasi-polynomial time. Our key technical result is a polynomial-time, (symmetric) menu-complexity-preserving black-box reduction from achieving a .99-approximation for unbounded valuations that are subadditive over independent items to achieving a .99-approximation when the values are bounded (and still subadditive over independent items). We further apply this reduction to deduce approximation schemes for a suite of valuation classes beyond our main result. Finally, we show that selling separately (which has exponential menu complexity) can be approximated up to a .99 factor with a menu of efficient-linear symmetric menu complexity. Joint work with Pravesh Kothari (CMU), Divyarthi Mohan, Sahil Singla and S. Matthew Weinberg (Princeton).
Location: CoRE A 301
Committee:
Start Date: 25 Feb 2020;
Start Time: 10:30AM -
Title: Emerging Architectures for Humanity's Grand Challenges

Bio:

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


Speaker:

Yipeng Huang, Princeton University


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

Bio:
Speaker:

Omri Weinstein


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

Bio:

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


Speaker:

Dr. Xinyu Xing, Pennsylvania State University


Abstract: Despite the best efforts of developers, software inevitably contains flaws that may be leveraged as security vulnerabilities. Modern operating systems integrate various security mechanisms to prevent software faults from being exploited. To bypass these defenses and hijack program execution, an attacker needs to constantly mutate an exploit and make many attempts. While in their attempts, the exploit triggers a security vulnerability and makes the running process abnormally terminate. After a program has crashed and abnormally terminated, it typically leaves behind a snapshot of its crashing state in the form of a core dump. While a core dump carries a large amount of information, which has long been used for software debugging, it barely serves as informative debugging aids in locating software faults, particularly memory corruption vulnerabilities. As such, previous research mainly seeks fully reproducible execution tracing to identify software vulnerabilities in crashes. However, such techniques are usually impractical for complex programs. Even for simple programs, the overhead of fully reproducible tracing may only be acceptable at the time of in-house testing. In this talk, I will discuss how we tackle this issue by bridging program analysis with artificial intelligence (AI). More specifically, I will first talk about the history of postmortem program analysis, characterizing and disclosing their limitations. Second, I will introduce how we design a new reverse-execution approach for postmortem program analysis. Third, I will discuss how we integrate AI into our reverse-execution method to escalate its analysis efficiency and accuracy. Last but not least, as part of this talk, I will demonstrate the effectiveness of this AI-assisted postmortem program analysis framework by using massive amounts of real-world programs.
Location: CoRE A 301
Committee:
Start Date: 02 Mar 2020;
Start Time: 10:30AM -
Title: Democratizing Error-Efficient Computing

Bio:

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


Speaker:

Radha Venkatagiri, University of Illinois at Urbana-Champaign


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

Bio:
Speaker:

Zuohui Fu


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

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

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

Bio:
Speaker:

Fotis Iliopoulos (IAS)


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

Bio:

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


Speaker:

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


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

Bio:

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


Speaker:

Meng Xu, Georgia Tech


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

Bio:
Speaker:

Rahul Shome


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

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

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

Bio:

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


Speaker:

 Muthu Venkitasubramaniam, University of Rochester


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

Bio:
Speaker:

Chen Wang (Rutgers)


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

Bio:

Akanksha Jain is a Research Associate at the University of Texas 

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


Speaker:

Akanksha Jain, University of Texas at Austin

     


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

Bio:
Speaker:

Rushin Gindra


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

Prof. Dimitris N. Metaxas (Advisor)

Prof. Konstantinos Michmizos

Prof. Karl Stratos

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

Bio:
Speaker:

Eduard Gibert Renart


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

Advisor: Manish Parashar 

Ulrich Kremer

Srinivas Narayana Ganapathy

External member: Otto J. Anshus 

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

Bio:
Speaker:

Xiaoli He


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

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

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

Bio:
Speaker:

Aditya Potukuchi


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

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

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

Bio:
Speaker:

Ji Zhang


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

Prof. Ahmed Elgammal (Advisor)

Prof. Yongfeng Zhang

Prof. Gerard de Melo

Dr. Ang Li (External member)

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

Bio:

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


Speaker:

Paul Grubbs, Cornell University


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

Bio:
Speaker:

Zhihan Fang


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

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

 

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

Bio:

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

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


Speaker:

Noah Stephens-Davidowitz, Simons Institute in Berkeley


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

Bio:
Speaker:

Kun Wang


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

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

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

Bio:
Speaker:

Honglu Zhou


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

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

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

Bio:
Speaker:

Abhishek Bhrushundi


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

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

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

Bio:
Speaker:

Zachary Daniels

 


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

Prof. Dimitris Metaxas (Chair)

Prof. Konstantinos Michmizos

Prof. George Moustakides

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