November 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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DATE CORRECTION: ML Theory Seminar on neural network approximation by Prof. Song Mei from UC Berkeley
by Emily C. Lawrence 30 Nov '23

30 Nov '23

ML Theory Seminar Prof. Song Mei from UC Berkeley Statistics is giving a seminar talk on neural network approximation theory in the modern age. The talk will be on Friday (Dec 8th) 12:30 pm -1:30 pm at Equad B205. =============================================================== Title: Revisiting neural network approximation theory in the age of generative AI Abstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it inadequately explains the impressive capabilities of modern generative AI models such as language models and diffusion models. This talk puts forth a refined perspective: neural networks often serve as algorithm approximators, going beyond mere function approximation. I will explain how this refined perspective offers a deeper insight into the success of modern generative AI models. Bio: Song Mei is an assistant professor of statistics and EECS at UC Berkeley. He received his Ph.D. from Stanford in June 2020. Song’s research lies at the intersection of statistics and machine learning. His recent research focuses on mathematics of deep learning and foundation models, theory of reinforcement learning, and high dimensional statistics.

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ML Theory Seminar on neural network approximation by Prof. Song Mei from UC Berkeley
by Emily C. Lawrence 29 Nov '23

29 Nov '23

ML Theory Seminar Prof. Song Mei from UC Berkeley Statistics is giving a seminar talk on neural network approximation theory in the modern age. The talk will be on Friday (Dec 1st) 12:30 pm -1:30 pm at Equad B205. =============================================================== Title: Revisiting neural network approximation theory in the age of generative AI Abstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it inadequately explains the impressive capabilities of modern generative AI models such as language models and diffusion models. This talk puts forth a refined perspective: neural networks often serve as algorithm approximators, going beyond mere function approximation. I will explain how this refined perspective offers a deeper insight into the success of modern generative AI models. Bio: Song Mei is an assistant professor of statistics and EECS at UC Berkeley. He received his Ph.D. from Stanford in June 2020. Song’s research lies at the intersection of statistics and machine learning. His recent research focuses on mathematics of deep learning and foundation models, theory of reinforcement learning, and high dimensional statistics.

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TODAY: CSML SEMINAR and ECE KORHAMMER SEMINAR SERIES: Haifeng Xu, University of Chicago
by Emily C. Lawrence 29 Nov '23

29 Nov '23

CSML SEMINAR ECE KORHAMMER SEMINAR SERIES Speaker: Haifeng Xu, University of Chicago Title: Algorithms and Incentives in Machine Learning Day: Wednesday, November 29, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Hosts: Chi Jin and Mengdi Wang Abstract: Machine learning designs approaches that transform data to predictions or estimations. The standard paradigm often assumes these data are objectively generated from distributions, without being affected by any human factors. However, this paradigm ceases to be true when our predictions or estimated parameters will in turn affect the data providers' welfare. In such situations, data providers have incentives to alter the data for their own benefits. Thus the design of any learning methods must account for potential data manipulations due to data providers' incentives. This talk will introduce a general ""incentive-aware"" framework for designing prediction methods. I will illustrate this design paradigm with two examples: (1) a very recent and timely application of eliciting authors' truthful private information for improving the peer review systems for today's massive scale machine learning conferences; (2) a very classic problem of PAC-learning classifiers but with strategic providers of data features. In both problems, I will illustrate how the presence of incentives can fundamentally change the problem's learning efficiency and how algorithms can help to overcome some statistic barriers. Bio: Haifeng Xu is an assistant professor in the Department of Computer Science and Data Science Institute at UChicago. He directs the Sigma Lab (Strategic IntelliGence for Machine Agents) which focuses on designing intelligent AI systems that can effectively learn and act in informationally complex multi-agent setups. His research has been recognized by a few awards including IJCAI early career spotlight, Google Faculty research award, the ACM SIGecom Dissertation Award and IFAAMAS Victor Lesser Distinguished Dissertation Award. This seminar is supported with funds from the Korhammer Lecture Series.

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Dr. Fei-Fei Li in Conversation with Provost Jennifer Rexford Wednesday, Nov 29 at 6pm
by Emily C. Lawrence 29 Nov '23

29 Nov '23

Wednesday, November 29 at 6pm Princeton Public Lectures presents, Dr. Fei-Fei Li in Conversation with Jennifer Rexford in-person event in McCosh 50 Free copies of Dr. Li's book, The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI, will be handed out to the first 300 in-person attendees. To participate virtually, you may either register to watch via [ https://princeton.zoom.us/webinar/register/WN_L58PDluYR_K6huylyTPiPg | Zoom Webinar ] , or Watch via Princeton’s Media Central (Live-Stream feed) by [ https://mediacentrallive.princeton.edu/ | clicking here ] . For more information about this event, [ https://lectures.princeton.edu/lectures/2023/dr-fei-fei-li | click here ] .

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Princeton Robotics Seminar: Sonia Chernova, Friday, Dec 1 at 11am
by Emily C. Lawrence 28 Nov '23

28 Nov '23

Speaker: Sonia Chernova, Georgia Tech Date: Friday, December 1, 2023 Time: 11:00 AM ET Location: CS 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Autonomy in the Human World: Developing Robots that Handle the Diversity of Human Lives Abstract: Reliable operation in everyday human environments – homes, offices, and businesses – remains elusive for today’s robotic systems. A key challenge is diversity, as no two homes or businesses are exactly alike. However, despite the innumerable unique aspects of any home, there are many commonalities as well, particularly about how objects are placed and used. These commonalities can be captured in semantic representations, and then used to improve the autonomy of robotic systems by, for example, enabling robots to infer missing information in human instructions, efficiently search for objects, or manipulate objects more effectively. In this talk, I will discuss recent advances in semantic reasoning, particularly focusing on semantics of everyday objects, household environments, and the development of robotic systems that intelligently interact with their world. Bio: Sonia Chernova is an Associate Professor in the College of Computing at Georgia Tech. She directs the Robot Autonomy and Interactive Learning lab, where her research focuses on the development of intelligent and interactive autonomous systems. Chernova’s contributions span robotics and artificial intelligence, including semantic reasoning, adaptive autonomy, human-robot interaction, and explainable AI. She also leads the NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), whose mission is to develop collaborative AI partners-in-care that help support a growing population of older adults, helping them sustain independence, improve quality of life, and increase effectiveness of care coordination across their care network.

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Udaya Ghai will present his FPO "A Game-theoretic Lens for Robustness in Control" on Monday, December 18, 2023 at 4:00 PM in CS 402.
by CS Grad Department 21 Nov '23

21 Nov '23

Udaya Ghai will present his FPO " A Game-theoretic Lens for Robustness in Control " on Monday, December 18, 2023 at 4:00 PM in CS 402 . Location: CS 402 The members of Udaya ’s committee are as follows: Examiners: Elad Hazan (Adviser), Ryan Adams , Naomi Leonard Readers: Sanjeev Arora , Anirudha Majumdar 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: The control of dynamical systems is a fundamental problem with a vast array of applications, from robotics to biological engineering. Recently, the game-theoretic primitive of regret minimization has been applied to control, yielding novel instance-optimal performance guarantees in more challenging non-stochastic control settings. This thesis further explores the benefits of a multi-agent perspective of control. Concretely, we begin with a new algorithm for generating disturbances for controller verification, which relies on recasting the players in the nonstochastic control game. Next, we provide a cooperative multi-agent extension of the nonstochastic control setting, involving a reduction from our multi-agent game to single agent regret minimization. Furthermore, we show new notions of robustness to failure can be attained through this perspective, even in a single-agent setting. While control is a powerful tool, it relies heavily on knowledge of the dynamics. The final chapters provide two very different approaches to robustness without such a model. The first approach extends the nonstochastic control methodology to model-free reinforcement learning. In an alternative approach, we consider unknown systems with dynamics that are \emph{approximately} linear using tools from classical control theory.

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Princeton Robotics Seminar: Stephanie Gil, Friday 11/17 at 11am
by Emily C. Lawrence 14 Nov '23

14 Nov '23

Speaker: [ https://gil.seas.harvard.edu/ | Stephanie Gil ] , Harvard Date: Friday, 11/17/23 Time: 11:00 AM ET Location: CS 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Resilient Coordination in Networked Multi-Robot Teams Abstract: Multi-robot systems are becoming more pervasive all around us, in the form of fleets of autonomous vehicles, future delivery drones, and robotic teammates for search and rescue. As a result, it becomes increasingly critical to question the robustness of their coordination algorithms to reliable information exchange, security threats and/or corrupted data. This talk will focus on the role of control and information exchange for enhancing situational awareness and security of multirobot systems. An example is the consensus problem where classical results hold that agreement cannot be reached when malicious agents make up more than half of the network connectivity; this quickly leads to limitations in the practicality of many multi-robot coordination tasks. However, with the growing prevalence of cyber-physical systems comes novel opportunities for detecting attacks by using cross-validation with physical channels of information. In this talk we consider the class of problems where the probability of a particular (i,j) link being trustworthy is available as a random variable. We refer to these as “stochastic observations of trust.” We show that under this model, strong performance guarantees such as convergence for the consensus problem can be recovered, even in the case where the number of malicious agents is greater than ½ of the network connectivity and consensus would otherwise fail. We will present both a theoretical framework, and experimental results, for provably securing multi-robot distributed algorithms through careful use of communication. Lastly, we will present promising results on new communication-centric methods for learning and sequential decision-making in tomorrow’s multi-robot systems. Bio: Stephanie is an Assistant Professor in the John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University. Her work centers around trust and coordination in multi-robot systems for which she has received the Office of Naval Research Young Investigator award (2021) and the National Science Foundation CAREER award (2019). She has also been selected as a 2020 Sloan Research Fellow for her contributions at the intersection of robotics and communication. She has held a Visiting Assistant Professor position at Stanford University during the summer of 2019, and an Assistant Professorship at Arizona State University from 2018-2020. She completed her Ph.D. work (2014) on multi-robot coordination and control and her M.S. work (2009) on system identification and model learning. At MIT she collaborated extensively with the wireless communications group NetMIT, the result of which were two U.S. patents recently awarded in adaptive heterogeneous networks for multi-robot systems and accurate indoor positioning using Wi-Fi. She completed her B.S. at Cornell University.

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Our AI Futures: Critical Humanistic Perspectives - today at 5pm
by Emily C. Lawrence 13 Nov '23

13 Nov '23

[ https://anthropology.princeton.edu/ | Anthropology@Princeton Seminar ] Monday, Nov 13, 2023 5:00 pm – 6:30 pm Covocation Room, 113 Friend Center Speakers: Alondra Nelson, Harold F. Linder Professor of Social Science, Institute for Advanced Study Alex Hanna, Distributed AI Research Institute Widely known for her pioneer research at the intersection of science, technology, and politics, Alondra Nelson holds the Harold F. Linder Chair in the School of Social Science of the Institute for Advanced Study. From 2021–23, she was deputy assistant to President Joe Biden, and acting director and principal deputy director for science and society of the White House Office of Science and Technology Policy. She is the past president of the Social Science Research Council and the first Social Science Dean at Columbia University. In 2023, she was included in the inaugural TIME100 list of the most influential people in AI. Dr. Alex Hanna is Director of Research at the Distributed AI Research Institute (DAIR). A sociologist by training, her work centers on the data used in new computational technologies, and the ways in which these data exacerbate racial, gender, and class inequality. She also works in the area of social movements, focusing on the dynamics of anti-racist campus protest in the US and Canada. In conversation with Anthropology Assistant Professor Beth Semel, Princeton University Event page: [ https://anthropology.princeton.edu/events/our-ai-futures-critical-humanisti… | https://anthropology.princeton.edu/events/our-ai-futures-critical-humanisti… ]

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ECE Seminar: Udit Gupta, Nov 10 at 2pm
by Emily C. Lawrence 07 Nov '23

07 Nov '23

ECE Seminar [ https://ece.princeton.edu/events/sustainable-computing-implications-challen… | Sustainable Computing: Implications, Challenges, Opportunities | Electrical and Computer Engineering (princeton.edu) ] Speaker: Udit Gupta, Cornell Tech. Title: Sustainable Computing: Implications, Challenges, Opportunities Day: November 10, 2023 Time: 2:00 – 3:30pm Location: Room 004, Friend Center. Host: David Wentzlaff Abstract: Computing has a substantial environmental impact, with far-reaching consequences. Information and Communication Technologies (ICT) alone contribute to 4% of global carbon emissions, a figure equivalent to the aviation industry's carbon footprint. This impact is poised to increase as the demand for computing continues to surge, particularly at the edge and within massive hyperscale data centers. Creating environmentally sustainable computing solutions presents unique challenges that go beyond energy efficiency optimization. It necessitates a holistic approach that considers various factors. For example, sustainable computing devices must account for the carbon emissions generated during the manufacturing process, the influence of renewable energy sources that fluctuate by location and time on a system's operational emissions, and the intricate interplay between embodied and operational emissions. In this presentation, we will delve into recent breakthroughs and the research challenges we confront as we strive to transition to environmentally responsible computing systems. With a primary focus on the carbon footprint of computing, we will explore recent initiatives aimed at characterizing and comprehending the environmental impact of computing. We will also introduce new carbon accounting models and frameworks, highlighting their application in pertinent domains, such as AI, to promote the development of sustainable AI solutions. Bio: Udit Gupta is an Assistant Professor at Cornell Tech, where his research is dedicated to pioneering the development of cutting-edge and ethically responsible AI platforms through the innovation of novel computer systems and hardware. His recent endeavors are centered around the design and optimization of data center-based, deep learning-driven personalized recommendation systems and championing eco-friendly computing by meticulously considering the ecological implications associated with the entire hardware life cycle. Udit's groundbreaking research has been tested and validated within production data centers at a large scale but has also found its place in established benchmarks and infrastructures that are widely embraced by the research community. His contributions were acknowledged with an honorable mention as an IEEE MICRO Top Picks selection in 2020 and further distinguished with an IEEE MICRO Top Picks award in 2021 and 2022. His work earned nominations for the Best Paper awards at PACT 2019 and DAC 2018. Furthermore, his doctoral research received the prestigious SIGARCH Outstanding Ph.D. Dissertation award honorable mention and the SIGMICRO Outstanding Ph.D. Dissertation award honorable mention.

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