September 2023 - talks - lists.cs.princeton.edu

CS Distinguished Colloquium Speaker John Jumper: Monday, September 25 at 4:30
by Emily C. Lawrence 12 Feb '24

12 Feb '24

Speaker: John Jumper, DeepMind Date: Monday, September 25 Time: 4:30pm to 5:30pm Location: Friend Center 101 Host: Ellen Zhong Event page: [ https://www.cs.princeton.edu/events/26500 | https://www.cs.princeton.edu/events/26500 ] Title: Highly accurate protein structure prediction with deep learning Abstract: Our work on deep learning for biology, specifically the AlphaFold system, has demonstrated that neural networks are capable of highly accurate modeling of both protein structure and protein-protein interactions. In particular, the system shows a remarkable ability to extract chemical and evolutionary principles from experimental structural data. This computational tool has repeatedly shown the ability to not only predict accurate structures for novel sequences and novel folds but also to do unexpected tasks such as selecting stable protein designs or detecting protein disorder. In this lecture, I will discuss the context of this breakthrough in the machine learning principles, the diverse data and rigorous evaluation environment that enabled it to occur, and the many innovative ways in which the community is using these tools to do new types of science. I will also reflect on some surprising limitations -- insensitivity to mutations and the lack of context about the chemical environment of the proteins -- and how this may be traced back to the essential features of the training process. Finally, I will conclude with a discussion of some ideas on the future of machine learning in structure biology and how the experimental and computational communities can think about organizing their research and data to enable many more such breakthroughs in the future. Bio: John Jumper received his PhD in Chemistry from the University of Chicago, where he developed machine learning methods to simulate protein dynamics. Prior to that, he worked at D.E. Shaw Research on molecular dynamics simulations of protein dynamics and supercooled liquids. He also holds an MPhil in Physics from the University of Cambridge and a B.S. in Physics and Mathematics from Vanderbilt University. At DeepMind, John is leading the development of new methods to apply machine learning to protein biology.

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Edgar Minasyan will present his FPO "Beyond Linear Paradigms in Online Control" on Tuesday, October 17, 2023 at 1:00 PM in CS 201.
by Grad Info 29 Sep '23

29 Sep '23

Edgar Minasyan will present his FPO "Beyond Linear Paradigms in Online Control" on Tuesday, October 17, 2023 at 1:00 PM in CS 201. Location: CS 201 The members of Edgar's committee are as follows: Examiners: Elad Hazan (Adviser), Mark Braverman, Ran Raz Readers: Bernard Chazelle, Chi Jin A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: In the last century, the problem of controlling a dynamical system has been a core component in numerous applications in autonomous systems, engineering, and sciences. The theoretical foundations built so far include the field of classical control theory as well as recent advances in leveraging regret minimization techniques for guarantees in online control. In either case, however, a linearity assumption is prevalent to enable the derivation of provable guarantees and efficient algorithms. In this thesis, we present several directions to alleviate the linearity assumption in the problem of online control. At the core of all the results lies the regret minimization frame- work: the presented works design new, as well as heavily rely on existing, methods and notions in online convex optimization. The ultimate goal of this direction is to derive efficient algorithms that perform optimal online control of nonlinear dynamical systems. This goal in full generality is NP-hard hence certain concessions need to be made. We build upon the recent non- stochastic control framework that enables efficient online control of linear dynamical systems: our first advance is to consider time-varying linear dynamical systems which are commonly used to approximate nonlinear dynamics. We argue that the correct performance metric in this setting is the notion of adaptive regret developed for online learning in changing environments and proceed to design an efficient control method for known time-varying linear dynamics. The extension to unknown dynamics proves to be qualitatively harder: our upper/lower bound results show that the system variability of the unknown dynamics is a determining factor on whether meaningful (adaptive) regret guarantees can be achieved. The policies in the described advances enjoy linear/convex parameterizations which can be constraining for complex dynamical systems. Hence, we additionally explore the use of neural network-based policies in online episodic control and derive an efficient regret-minimizing algorithm that in- corporates the interpolation dimension as an expressivity metric for the policy class.

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Xiaoqi (Danny) Chen will present his FPO "Designing Compact Data Structures for Network Measurement and Control" on Wednesday, October 4, 2023 at 2pm in Friend Center 125 (and Zoom)
by Gradinfo 28 Sep '23

28 Sep '23

Xiaoqi (Danny) Chen will present his FPO "Designing Compact Data Structures for Network Measurement and Control" on Wednesday, October 4, 2023 at 2pm in Friend Center 125 (and Zoom). Zoom link: [ https://princeton.zoom.us/j/96617518355 | https://princeton.zoom.us/j/96617518355 ] The members of his committee are as follows: Examiners: Jennifer Rexford (adviser), David Walker, and Maria Apostolaki Readers: Mark Braverman and Minlan Yu (Harvard University) Talk abstract follows below. All are welcome to join. Abstract: This dissertation explores the implementation of network measurement and closed-loop control in the data plane of high-speed programmable switches. After discussing the algorithmic constraints imposed by the switch pipeline architecture, primarily stemming from the requirement of high-speed processing, we share our experience in tailoring algorithms for the data plane. Initially, we focus on efficient measurement algorithms, and present two works for detecting heavy hitters and executing multiple distinct-count queries; both require designing novel approximate data structures to meet the tight memory access constraints. Subsequently, we pivot towards using real-time, closed-loop control in the data plane for performance optimization, and present two works for mitigating microbursts and enforcing fair bandwidth limits; both require approximated computation and exploit the sub-millisecond reaction latency unattainable through conventional control planes. We hope by sharing our experience and techniques, which are widely applicable to various algorithms and other data-plane hardware targets, we can lay the foundation for future innovations in the field of network programming for researchers and practitioners alike.

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Math Dept: Minerva Mini-Course by Alex Lubotzky
by Emily C. Lawrence 25 Sep '23

25 Sep '23

Math Department MINERVA MINI-COURSE Alex Lubotzky, Weizmann Institute, The Hebrew University, and Minerva Distinguished Visitor Expanders and Ramanujan graphs Wednesday, Sep 27 Fine Hall Common Room 12:15 PM – 1:15 PM [ https://www.math.princeton.edu/events/expanders-and-ramanujan-graphs-2023-0… | https://www.math.princeton.edu/events/expanders-and-ramanujan-graphs-2023-0… ] Expander graphs are of fundamental importance in computer science and in recent years, also in several areas of mathematics. A brilliant insight of Margulis showed their connection with representation theory (Kazhdan Property T) and paved the way to use the theory of automorphic forms to construct ``optimal" expanders (a.k.a. Ramanujan graphs) based on the work of Deligne and Drinfeld on the Ramanujan conjecture for GL(2)(2).

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Keller Center Innovation Forum: Friday, Sept 29
by Emily C. Lawrence 22 Sep '23

22 Sep '23

The Keller Center at Princeton University is hosting an Innovation Forum on campus September 29. This competition and networking event showcases Princeton STEM and Humanities research with potential for commercialization, cultural, or societal impact. Our own [ https://www.wtang.page/ | Wei Tang ] will be presenting their research " [ https://kellercenter.princeton.edu/people/startups-teams/arqitech | ArQitech: A software platform that combines quantum and classical computing. ] " Princeton faculty, postdoctoral researchers, and graduate students will present their innovations in 3-minute pitches. A panel of experts will determine the top entries, prizes totalling $60,000 will be awarded. The event includes a keynote and networking demo-station reception. [ https://kellercenter.princeton.edu/people/teams-startups-filtered?program-f… | Details about all the presenters ] . This event is a unique opportunity to get a glimpse of the extraordinary innovations currently being developed at Princeton. We hope you can join us. Innovation Forum is free and open to the public. [ https://www.eventbrite.com/e/18th-annual-innovation-forum-tickets-653116287… | Register to attend ] . The [ https://kellercenter.princeton.edu/ | Keller Center ] is based in Princeton University's School of Engineering and Applied Science and shares the school's vision for bridging disciplines to ensure that all students are prepared to put science and technology to use in solving critical societal challenges. The Keller Center thus serves as a hub, connecting students in engineering, the humanities, arts, social sciences, and natural sciences with each other, as well as connecting them with the broader campus community and beyond. We hope you can join us as we spend the day celebrating impactful innovations created at Princeton. [ https://kellercenter.princeton.edu/events/innovation-forum-2023 | Details and registration for Innovation Forum 2023 ] . [ https://kellercenter.princeton.edu/events/opportunities-and-challenges-driv… | Details and registration for Opportunities and Challenges of Driving Impact in Innovation ] .

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Friday September 22 - Pulkit Agrawal - Princeton Robotics Seminar
by Emily C. Lawrence 20 Sep '23

20 Sep '23

Speaker: [ https://people.csail.mit.edu/pulkitag/ | Pulkit Agrawal ] , MIT Date: Friday, September 22, 2023 Time: 11am ET Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Physical Intelligence as API Abstract: Large Language Models (LLMs) are unprecedented in their ability to go beyond application-specific software and promise a one-stop solution to several digital tasks. With such advances, robotic agents are able to convert complex natural language commands into step-wise instructions. However, accurate and reliable execution of sensorimotor skills (e.g., locomotion, opening doors, object manipulation, etc.) remains elusive. I will discuss a framework that allows robots to learn new, complex, and generalizable behaviors while reducing the human effort in designing such behaviors to easily scale to many tasks. This framework is a stepping stone towards building Physical Intelligence as API -- a one-stop robotics solution (i.e., an API) that can perform many manipulation and locomotion tasks that humans perform. I will elaborate on the framework using the following case studies: (i) a dexterous manipulation system capable of re-orienting novel objects and tool use such as peeling vegetables. (ii) a quadruped robot capable of fast locomotion and manipulation on diverse natural terrains. (iii) object re-arrangement system tested on manipulating out-of-distribution object configurations. Bio: Pulkit Agrawal is an Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT, where he directs the Improbable AI Lab. His research interests span robotics, computer vision, and reinforcement learning. Pulkit's work received the Best Paper Award at Conference on Robot Learning 2021 and the Best Student Paper Award at the Conference on Computer Supported Collaborative Learning 2011. He is a recipient of the Sony Faculty Research Award, Salesforce Research Award, Amazon Research Award, a Fulbright fellowship, etc. Before joining MIT, he received his Ph.D. from UC Berkeley and Bachelor's degree from IIT Kanpur, where he was awarded the Directors Gold Medal.

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TIME CHANGE -- ECE Korhammer Seminar: Meir Feder, Tel Aviv University (Wed., Sept. 20, 2023 @ 12:30pm, B205)
by Emily C. Lawrence 15 Sep '23

15 Sep '23

ECE KORHAMMER SEMINAR SERIES Speaker: Meir Feder, Tel Aviv University Title: Multiple and Hierarchical Universality Day: September 20, 2023 Time: 12 :30pm Location: B205 Engineering Quadrangle Hosts: H. Vincent Poor and Andrea Goldsmith Registration Required: [ https://forms.gle/jEZviiAmp7c1NzvG6 | https://forms.gle/jEZviiAmp7c1NzvG6 ] Abstract: Universal coding, prediction and learning usually consider the case where the data generating mechanism is unknown or non-existent, and the goal of the universal scheme is to compete with the best hypothesis from a given hypothesis class, either on the average or in a worst-case scenario. Multiple universality considers the case where the hypothesis class is also unknown: there are several hypothesis classes with possibly different complexities. In hierarchical universality, the simpler classes are nested within more complex classes. The main challenge is to correctly define the universality criterion so that the extra “regret” for not knowing the class is monitored. We propose several possible definitions and derive their min-max optimal solutions. Interestingly, the proposed approach can be used to obtain Elias codes for universal representation of the integers. We further utilize this approach for variable-memory Markov models (unifilar models), presenting a new interpretation for the bound over the regret of the celebrated context-tree weighting algorithm, and suggest a multiple universality approach for general linear models, including linear regression, and logistic regression and Perceptrons. Finally, we conjecture that multiple universality, with its non-uniform convergence and regret bounds, can be applied to explain and design learning schemes in general “over-parameterized” model classes such as deep neural networks, transformers and so on. Joint work with Yaniv Fogel. Bio: Meir Feder received the B.Sc and M.Sc degrees in Electrical Engineering in 1980 and 1984 from Tel-Aviv University and the Sc.D degree in Electrical Engineering and Ocean Engineering in 1987 from the Massachusetts Institute of Technology (MIT) and the Woods Hole Oceanographic Institution (WHOI). After being a Research Associate and a Lecturer in MIT, he joined the School of Electrical Engineering, Tel-Aviv University, where he is now the Jokel Chaired Professor and the head of the newly established Tel-Aviv university center for Artificial intelligence and Data science (TAD). He is also a Visiting Professor with the Department of EECS, MIT. Parallel to his academic career, he is closely involved with the high-tech industry. He founded 5 companies, among them Peach Networks that developed an interactive TV solution (Acq: MSFT) and Amimon that provided the highest quality, robust, no latency wireless high-definition A/V connectivity (Acq:LON.VTC). Recently, with his renewed interest in machine learning and AI, he cofounded Run:ai, a virtualization, orchestration, and acceleration platform for AI infrastructure. He is also an active angel investor and serves on the board/advisory board of several US and Israeli companies. Prof. Feder received several academic and professional awards including the IEEE Information Theory Society best paper award for his work on universal prediction, the “creative thinking” award of the Israeli Defense Forces and the Research Prize of the Israeli Electronic Industry, awarded by the President of Israel. For the development of Amimon’s chip-set, that uses a unique MIMO implementation of joint source-channel coding for wireless video transmission, he received the 2020 Scientific and Engineering Award of the Academy of Motion Picture Arts and Sciences (Oscar). This seminar is supported with funds from the Korhammer Lecture Series.

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DeCenter Seminar Series: Wednesday, September 20 at 4:30
by Emily C. Lawrence 13 Sep '23

13 Sep '23

[ https://decenter.princeton.edu/ | DeCenter Seminar Series ] Title: An Overview of Mechanism Design for Blockchain Applications Speaker: Matt Weinberg, Princeton University Date: Wednesday, September 20, 2023 - 4:30 pm Location: Friend Center Convocation Room (Friend 113) Reception following talk. [ https://decenter.princeton.edu/events/decenter-seminar-an-overview-of-mecha… | https://decenter.princeton.edu/events/decenter-seminar-an-overview-of-mecha… ] Abstract: Blockchains rely on an underlying consensus protocol to ensure consistency of their ledger. Classically, these protocols are proven secure assuming that an attacker controls at most (say) 33% of the network, and the remaining portion of the network honestly follows the protocol. This analysis is necessary but overlooks an important aspect: participants earn rewards by participating, and all participants, honest or not, will happily deviate from the protocol if it yields greater rewards. In this talk, I'll overview several challenges to designing consensus protocols that correctly incentivize participants to follow them. This talk will not assume any particular technical background, although it will have technical content. Bio: Matt Weinberg is an Associate Professor of Computer Science at Princeton University. His primary research interest is in algorithmic mechanism design: algorithm design in settings where users have their own incentives. He is also interested more broadly in algorithmic game theory, algorithms under uncertainty, and theoretical computer science in general. Before joining the faculty at Princeton, Weinberg spent two years as a postdoc in Princeton’s CS theory group and was a research fellow at the Simons Institute during the Fall 2015 (economics and computation) and Fall 2016 (algorithms and uncertainty) semesters. He completed his PhD in 2014 at MIT, where he was advised by Costis Daskalakis. Prior to that, he graduated from Cornell University with a BA in Math in 2010, where he worked with Bobby Kleinberg. If you need an accommodation for a disability, please contact [ mailto:decenter@princeton.edu | decenter(a)princeton.edu ] at least one week before the event.

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Princeton Precision Health Fall Symposium September 29 at the Friend Center
by Emily C. Lawrence 12 Sep '23

12 Sep '23

Princeton Precision Health (PPH) is holding an all-day symposium on interdisciplinary, date-driven approaches to precision medicine on September 29 at the Friend Center. Provost Jennifer Rexford will give opening remarks and keynote speakers include Hana El-Samad, Senior Vice President and Director of Science Integration, Innovation & Insights, Altos Labs and Garret FitzGerald, MD, Robert L. McNeil, Jr. Professor in Translational Medicine and Therapeutics, Associate Dean for Translational Research, University of Pennsylvania Perelman School of Medicine. Registration is free but required for an accurate head count for breakfast, lunch, and closing reception: [ https://pph.princeton.edu/form/fall-symposium-registration | https://pph.princeton.edu/form/fall-symposium-registration ] The full program can be viewed here: [ https://pph.princeton.edu/fall-symposium-program | https://pph.princeton.edu/fall-symposium-program ] Hope to see you there! Olga *************************************************************** Olga Troyanskaya Director, Princeton Precision Health Professor of Computer Science and the Lewis-Sigler Institute for Integrative Genomics Princeton University

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Princeton Robotics Seminar: Vikas Sindhwani, Friday June 9 at 11am
by Emily C. Lawrence 12 Sep '23

12 Sep '23

Speaker: Vikas Sindhwani, Senior Staff Research Scientist, Google Deepmind Date: Friday, June 9, 2023 Time: 11am ET Location: Virtual, Zoom link: [ https://princeton.zoom.us/my/robotics | https://princeton.zoom.us/my/robotics ] Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Large Language Models with Eyes, Arms and Legs Abstract: To become useful in human-centric environments, robots must demonstrate language comprehension, semantic understanding and logical reasoning capabilities working in concert with low-level physical skills. With the advent of modern "foundation models" trained on massive datasets, the algorithmic path to developing general-purpose “robot brains” is (arguably) becoming clearer, though many challenges remain. In the first part of this talk, I will attempt to give a flavor of how state-of-the-art multimodal foundation models are built, and how they can be bridged with low-level control. In the second part of the talk, I will summarize a few surprising lessons on control synthesis observed while solving a collection of Robotics benchmarks at Google. I will end with some emerging open problems and opportunities at the intersection of dynamics, control and foundation models. Bio: Vikas Sindhwani is Research Scientist at Google Deepmind in New York where he leads a research group focused on solving a range of planning, perception, learning, and control problems arising in Robotics. His interests are broadly in core mathematical foundations of statistical machine learning, and in end-to-end design aspects of building large-scale and robust AI systems. He received the best paper award at Uncertainty in Artificial Intelligence (UAI-2013), the IBM Pat Goldberg Memorial Award in 2014, and was finalist for Outstanding Planning Paper Award at ICRA-2022. He serves on the editorial board of Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Pattern Analysis and Machine Intelligence; he has been area chair and senior program committee member for NeurIPS, International Conference on Learning Representations (ICLR) and Knowledge Discovery and Data Mining (KDD). He previously headed the Machine Learning group at IBM Research, NY. He has a PhD in Computer Science from the University of Chicago and a B.Tech in Engineering Physics from Indian Institute of Technology (IIT) Mumbai. His publications are available at: http://vikas.sindhwani.org/

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