- talks - lists.cs.princeton.edu

ECE/CS/CITP Seminar: Yongdae Kim, KAIST - Nov 11 at 4:30pm
by Emily C. Lawrence 11 Nov '22

11 Nov '22

ECE/CS/CITP Seminar Speaker: Yongdae Kim, KAIST Title: Unpatched Vulnerabilities in Cellular Standards Day: Friday, November 11 Time: 4:30pm Location: B205 Engineering Quadrangle Host: Maria Apostolaki and Prateek Mittal Abstract: In a couple of years, "study items" for the 6G security standard will be set. Security issues not included in these study items are unlikely to be standardized and patched even in 6G. Therefore, before these study items are set, the security research community needs to put in effort to find security vulnerabilities in cellular standards up to 5G. Furthermore, as a community, we need to find solutions to these vulnerabilities that are practical enough to be accepted by the standard bodies. In this talk, I will introduce unpatched design vulnerabilities and attacks in cellular standards up to 5G. I will also talk about potential defense mechanisms and reasons why they have not been accepted in 3GPP so far. Bio: Yongdae Kim is a Professor in the Department of Electrical Engineering and the Graduate School of Information Security and a head of Police Science and Technology Research Center at KAIST. He received a PhD degree from the computer science department at the University of Southern California in 2002. Before joining KAIST in 2012, he was a professor in the Department of Computer Science and Engineering at the University of Minnesota - Twin Cities for 10 years. He served as a KAIST Chair Professor between 2013 and 2016 and a director of Cyber Security Research Center between 2018 and 2020. He is currently serving as a steering committee member of ACM WISEC and served as a general chair for ACM CCS 2021, a program committee chair for ACM WISEC 2022, an associate editor for ACM TOPS and a steering committee member of NDSS. His main research interest is finding and fixing novel vulnerabilities for emerging technologies such as drones, self-driving cars, and cellular networks. This seminar is partially supported with funds from the Korhammer Lecture Series Sponsored by: Electrical and Computer Engineering, Computer Science, and the Center for Information Technology Policy

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Princeton Robotics Seminar - Andy Zeng, Fri. Nov 11
by Emily C. Lawrence 08 Nov '22

08 Nov '22

Princeton Robotics Seminar Speaker: Andy Zeng, Senior Research Scientist, Google Brain Date: Friday Nov 11, 2022 Time: 11am EST Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Language as Robot Middleware Abstract: We'd like to build robots that can help us with just about anything. Central to this is getting robots to build a general-purpose representation of the world from perception, and then use it to inform actions. Should this representation be 2D? or 3D? how do we "anchor" it onto a desired latent space? should it be an implicit representation? object-centric? can it be self-supervised? While many options exist out there, I'll talk about one in particular that's becoming my favorite – natural language. Partly motivated by the advent of large language models, but also motivated by recent work in multi-task learning. In the context of robots, I'll talk about: (i) why we're starting to think that it might actually be a good idea to revisit "language" as a symbolic representation to glue our systems together to do cool things, and (ii) in the process of building these systems, discovering various "gaps" in grounding language to control that I think we could really use your help in figuring out. Biography: Andy Zeng is a Senior Research Scientist at Google Brain working on vision and language for robotics. He received his Bachelors in Computer Science and Mathematics at UC Berkeley '15, and his PhD in Computer Science at Princeton University '19. Andy is a recipient of several awards including the Best Paper Award at T-RO '20, Best Systems Paper Awards at RSS '19 and Amazon Robotics '18, 1st Place (Stow) at the Amazon Picking Challenge '17, and has been finalist for Best Paper Awards at conferences CoRL '21, CoRL '20, ICRA '20, RSS '19, IROS '18. His research has been recognized through the Princeton SEAS Award for Excellence '18, NVIDIA Fellowship '18, and Gordon Y.S. Wu Fellowship in Engineering and Wu Prize '16, and his work has been featured in many popular press outlets, including the New York Times, BBC, and Wired. To learn more about Andy's work please visit [ https://andyzeng.github.io/ | https://andyzeng.github.io ]

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Ludwig Princeton Branch Seminar - Michael Skinnider, Monday, Nov 7
by Emily C. Lawrence 07 Nov '22

07 Nov '22

Ludwig Princeton Branch Seminar Speaker: Michael Skinnider, The University of British Columbia Date: Monday, November 7, 2022 Time: 12:30pm Location: 101 Icahn Title: “Machine learning approaches to human disease” Abstract: Advances in genomic, transcriptomic, and proteomic technologies now enable the routine measurement of thousands of biomolecules within any biological sample. With the advent of single-cell techniques that multiplex these measurements over millions of cells, the scale of biological data generation is outpacing our human ability to comprehend this data. Approaches based on machine intelligence offer the possibility of augmenting our human capacity for understanding in order to enable biological discovery. In this seminar, I will explore applications of machine learning to enable biological discovery from genomic, proteomic, transcriptomic, and metabolomic datasets, and discuss implications for the diagnosis and treatment of human disease. Areas of focus will include the discovery of novel antibiotics from nature using genomic and metabolomic datasets; charting protein-protein interactions within mammalian tissues and across the tree of life using proteomic datasets; mapping neural circuits in high-throughput using single-cell and spatial transcriptomics datasets; and the identification of novel designer drugs of abuse from metabolomic datasets. Looking forward, I will explore how machine learning might bridge the most significant remaining gap in our ability to measure biological systems: namely, our ability to measure all of the small molecules within any biological sample. I will discuss the use of chemical artificial intelligence to identify unknown molecules within mass spectrometry data, and the implications of this technology for our understanding of cancer metabolism and germline cancer risk. Bio: Michael is an MD/PhD who has carried out groundbreaking work using artificial intelligence to infer the structures of small molecules, from metabolites to designer drugs, impacting human health.

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GEO/AOS/HMEI Seminar: Elizabeth Barnes - Thursday, Nov 10
by Emily C. Lawrence 04 Nov '22

04 Nov '22

GEO/AOS/HMEI Thursday Seminar Series Speaker: Elizabeth A. Barnes, Professor, Dept. of Atmospheric Science, Colorado State University Date: Thursday, November 10, 2022 Time: 12:30 p.m. Location: Guyot 220 Title: “Explainable AI for Climate Science: Detection, Prediction and Discovery” Abstract: Earth’s climate is chaotic and noisy. Finding usable signals amidst all of the noise can be challenging. Here, I will demonstrate how explainable artificial intelligence (XAI) techniques can sift through vast amounts of climate data and push the bounds of scientific discovery. Examples include extracting robust indicator patterns of climate change and identifying Earth system states that lead to more predictable behavior weeks-to-years in advance. But machine learning models are only as capable as the scientists designing them. I will further discuss how climate science requires the crafting of domain specific XAI methods, both to gauge the trustworthiness of the XAI’s predictions and quantify uncertainty, but also to uncover predictable signals we didn't know were there. Explainable AI can open doors to scientific understanding — supporting scientists as we ask new questions about the coupled human-Earth system.

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UPDATE: Math Department Colloquium - Wednesday, November 2, 2022
by Emily C. Lawrence 02 Nov '22

02 Nov '22

Today’s talk will be in-person only and will not be broadcasted over Zoom. [ https://www.math.princeton.edu/events/seminars/department-colloquium | DEPARTMENT COLLOQUIUM ] [ https://www.math.princeton.edu/events/algorithmic-spin-glass-theory-2022-11… | Andrea Montanari, Stanford University ] Algorithmic spin glass theory Wednesday, Nov 02 Fine Hall 314 / 4:30 PM - 5:30 PM *This Talk is In-Person Only* Mean field spin glasses are high-dimensional random functions with special exchangeability properties. These models were originally motivated by the study of disordered magnetic materials. However, it soon became clear that a large number of random optimization problems of interest in computer science and statistics fit this framework. Parisi's replica symmetry breaking (RSB) theory allows to determine the asymptotics of the optimal value of these problems. Can RSB shed light on relevant algorithmic questions as well? Here is a specific formalization of this question: Is there a polynomial time algorithm that outputs a feasible solution of these optimization problems whose value is, with high probability, within a factor \rho of the optimum? I will survey recent rigorous work that points at a remarkably precise answer for this question. Time permitting, I will talk about the problem of sampling from the Sherrington-Kirkpatrick Boltzmann measure. Based on joint work with Ahmed El Alaoui and Mark Sellke.

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Math Department Colloquium - Wednesday, November 2, 2022
by Emily C. Lawrence 31 Oct '22

31 Oct '22

MATH DEPARTMENT COLLOQUIUM Andrea Montanari, Stanford University Algorithmic spin glass theory Wednesday, Nov 02 Fine Hall 314 / 4:30 PM - 5:30 PM In-Person and Online Talk Zoom Link: [ https://princeton.zoom.us/j/97792754638?pwd=MGFNcU5tMzZGczc5N0hpSTB6Y0hEUT09 | https://princeton.zoom.us/j/97792754638?pwd=MGFNcU5tMzZGczc5N0hpSTB6Y0hEUT09 ] Mean field spin glasses are high-dimensional random functions with special exchangeability properties. These models were originally motivated by the study of disordered magnetic materials. However, it soon became clear that a large number of random optimization problems of interest in computer science and statistics fit this framework. Parisi's replica symmetry breaking (RSB) theory allows to determine the asymptotics of the optimal value of these problems. Can RSB shed light on relevant algorithmic questions as well? Here is a specific formalization of this question: Is there a polynomial time algorithm that outputs a feasible solution of these optimization problems whose value is, with high probability, within a factor \rho of the optimum? I will survey recent rigorous work that points at a remarkably precise answer for this question. Time permitting, I will talk about the problem of sampling from the Sherrington-Kirkpatrick Boltzmann measure. BASED ON JOINT WORK WITH AHMED EL ALAOUI AND MARK SELLKE.

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Princeton Robotics Seminar: Friday Oct 28 at 11am
by Emily C. Lawrence 25 Oct '22

25 Oct '22

Princeton Robotics Seminar Speaker: Tomás Lozano-Pérez, Professor of Computer Science & Engineering, MIT Date: Friday Oct 28, 2022 Time: 11am EDT Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Generalization in Planning and Learning for Robotic Manipulation Abstract: An enduring goal of AI and robotics has been to build a robot capable of robustly performing a wide variety of tasks in a wide variety of environments; not by sequentially being programmed (or taught) to perform one task in one environment at a time, but rather by intelligently choosing appropriate actions for whatever task and environment it is facing. This goal remains a challenge. In this talk I’ll describe recent work in our lab aimed at the goal of general-purpose robot manipulation by integrating task-and-motion planning with various forms of model learning. In particular, I’ll describe approaches to manipulating objects without prior shape models, to acquiring composable sensorimotor skills, and to exploiting past experience for more efficient planning. Biography: Tomás Lozano-Pérez is Professor in EECS at MIT, and a member of CSAIL. He was a recipient of the 2011 IEEE Robotics Pioneer Award and a co-recipient of the 2021 IEEE Robotics and Automation Technical Field Award. He is a Fellow of the AAAI, ACM, and IEEE.

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Yuanhao Wang will present his General Exam on Friday, October 21, 2022 at 12pm via Zoom.
by Nicki Mahler 20 Oct '22

20 Oct '22

Yuanhao Wang will present his General Exam on Friday, October 21, 2022 at 12pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/4269281435 | https://princeton.zoom.us/j/4269281435 ] The members of his committee are as follows: Chi Jin (adviser), Huacheng Yu, and Jason Lee Abstract Reinforcement learning (RL) is an interactive decision making problem where an agent tries to maximize its cumulative reward through interacting with an unknown environment. RL has seen a surge of interest over the past decade, fueled primarily by its empirical success in a wide range of tasks, including Atari games, Go, robotics and dialogue systems. Motivated by and complementing empirical research, theoretical understanding of RL has also made substantial progress in the past few years. In the most basic case, tabular RL, minimax optimal algorithms have been identified and analyzed. Recent focus of research in the tabular case has shifted toward instance dependent bounds, adversarial settings and multi-agent RL. However, the tabular case fails to capture many real-world problems where the number of states is huge or infinite, in which case function approximation is necessary. In contrast to the usual supervised learning case where complexity measures such as VC dimension are able to characterize the feasibility of learning, the search for structural assumptions that would empower efficient RL remains inconclusive. It has been conjectured and recently proven that realizability alone is insufficient even for linear function approximation, making stronger conditions necessary. Several candidates include low Bellman rank, low witness rank and low Bellman-Eluder dimension, yet the picture is far from complete. My research focus would be on improving the theoretical understanding of RL by addressing some of the aforementioned challenges, such as those about multi-agent learning and function approximation RL. On the multi-agent side, I would like to investigate which solution concepts, such as rationalizability, no-regret and various equilibria, are attainable in RL. On the function approximation side, I hope to identify minimal structural assumptions that permit efficient RL. Reading List: [ https://docs.google.com/document/d/14rchqe0CO9DI9IvmXnFkNX4lf8HahLWtn1tAU0G… | https://docs.google.com/document/d/14rchqe0CO9DI9IvmXnFkNX4lf8HahLWtn1tAU0G… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Civil & Environmental Engineering speaker: Andrea Castelletti, Tuesday, Nov 1
by Emily C. Lawrence 17 Oct '22

17 Oct '22

Civil & Environmental Engineering Speaker: Andrea Castelletti: Environmental Intelligence Lab, Politecnico di Milano Date: Tuesday, Nov 1, 2022 Time: 4:30PM Location: Louis Simpson, Room A71 Title: Machine Learning advancements for design of water and energy policies in a changing climate and society Abstract: Advances in environmental data monitoring and earth systems’ modeling have increasingly allowed us to accurately reproduce physical processes and their interactions at multiple scales, improving our ability to inform water and energy systems policy design. Yet, many process-based models are limited in predicting complex dynamics, which are the key to strategic planning, such as the impact of extreme weather and climate events or the mutual influence between humans and earth systems. Our work has shown that advanced data analytics and Machine Learning offer new opportunities to better characterize and model coupled human-earth system processes in a world in transition, ultimately supporting more equitable and efficient decision-making processes. In this talk, we provide an overview of recent advances in data-driven modeling and control of human-water-energy systems and showcase how Machine Learning techniques can help (i) infer natural and anthropogenic drivers of observed hydroclimatic patterns and improve their predictability in space and time, (ii) understand and conceptualize the mutual influences between human behaviors and water-energy systems; and (iii) design strategic planning and management policies optimizing multiple and conflicting objectives with different dynamics and informed by heterogeneous information. We recognize that this is a rapidly evolving research field, and we will also stimulate discussion around key challenges, including modeling decisions under uncertainty, model explain ability, data and computational requirements, model scalability, and transferability.

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Princeton Robotics Seminar: Sarah Tang, Fri Oct 14
by Emily C. Lawrence 12 Oct '22

12 Oct '22

Princeton Robotics Seminar Speaker: [ https://www.sarahtang.net/ | Sarah Tang ] , Staff Software Engineer in Planning, Waymo Date: Friday Oct 14, 2022 Time: 11:00am EDT Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Data-Centric ML for Autonomous Driving Abstract: Waymo is building the "World's Most Experienced Driver". This talk will discuss data challenges that arise when scaling machine learning systems with almost 1500 years worth of real-life human driving. Biography: Sarah is passionate about building robots that make intelligent decisions in complex, dynamic environments. She is currently a Staff Software Engineer on the Planner ML team at Waymo, where she is working to make driving safer, smarter, and more scalable with high-capacity models. In previous lives, she was the Tech Lead and Manager of the Motion Planning team at Nuro, where she led development of the decision making and trajectory optimization stack for major product milestones, including autonomous operation of their last-mile delivery robot without a driver nor a chase-car-operator in Arizona, California, and Texas. She was recognized in Business Insider’s 2021 list of “Rising stars in autonomous vehicles”. Sarah got her start in robotics as a member of Princeton's Great Class of 2013.

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