- talks - lists.cs.princeton.edu

ECE Seminar: Jakub Szefer, Friday, Nov 18 at 12:30 pm
by Emily C. Lawrence 16 Nov '22

16 Nov '22

ECE Seminar Speaker: Jakub Szefer, Associate Professor, Dept. of Electrical Engineering, Yale University Day: November 18, 2022 Time: 12:30 pm EST Location: EQuad B205 Host: David Wentzlaff Title: Quantum Computer Cybersecurity Abstract: Quantum computers are becoming larger every year, and are already today easily accessible thanks to different cloud-based services from companies such as IBM Quantum, Microsoft Azure, or Amazon Braket. Increasing quantum computer sizes and computational abilities bring closer the potential for these computers to be able to generate novel solutions and results in chemistry, medicine, or material science, for example. However, as these computers promise to generate novel results and data, they will become targets of cyber attackers, who may want to steal information, influence the computation, or cause disruption to the operation of the quantum computers. The security threat-level is heightened by the fact that these computers are network-connected and are open to untrusted cloud users running malicious code on them. In this talk, we will present some of the first research on security evaluation of real, cloud-based quantum computers. We will first discuss potential “software” threats to quantum computers, by demonstrating insecurity of some of the basic gates, or operations, that are today available on the quantum computers. We will also demonstrate potential security threats considering future, multi-tenant use of the quantum computers where multiple users share a single quantum computer. The potential threats were found and evaluated by collecting data and running quantum programs on real, cloud-based quantum computers. The talk will also touch upon defenses we have proposed. In addition, the talk will aim to motivate the need to study cybersecurity of quantum computers and include security protections in their design. Today, large amounts of money and effort are put into development of bigger and more capable quantum computers, quantum software frameworks, and cloud infrastructures for these computers. Meanwhile, their security analysis and protections are lagging much behind. Bio: Prof. Jakub Szefer is currently an Associate Professor of Electrical Engineering at Yale University, where he leads the Computer Architecture and Security Laboratory (CASLAB). Jakub’s research encompasses secure processor architectures, cloud and device security, fast implementation of post-quantum cryptographic algorithms, as well as quantum computer cybersecurity. Among others, Jakub is the author of first book focusing on processor architecture security: “Principles of Secure Processor Architecture Design”, published in 2018. Most recently, Jakub is a recipient of the 2021 Ackerman Award for Teaching and Mentoring from Yale University.

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CSML Lunch & Learn Seminar, Nov 21 at 12:30
by Emily C. Lawrence 15 Nov '22

15 Nov '22

CSML Lunch and Learn SEMINAR Speaker: Mert Gurbuzbalaban, Rutgers University Title: Robust and Risk-Averse Accelerated Gradient Methods Day: November 21, 2022 Time: 12:30 PM, Lunch from 12:15 RSVP to [ mailto:csml@princeton.edu | csml(a)princeton.edu ] Location: CSML, Classroom 103 Host: Peter Ramadge, Director CSML Abstract: In the context of first-order algorithms subject to random gradient noise, we study the trade-offs between the convergence rate (which quantifies how fast the initial conditions are forgotten) and the "risk" of suboptimality, i.e., deviations from the expected suboptimality. We focus on a general class of momentum methods (GMM) that recover popular methods such as gradient descent (GD), accelerated gradient descent (AGD), and the heavy-ball (HB) method as special cases depending on the choice of GMM parameters. We use well-known risk measures "entropic risk" and "entropic value at risk" to quantify the risk of suboptimality. For strongly convex smooth minimization, we first obtain new convergence rate results for GMM with a unified theory that applies to both AGD and HB, improving some of the existing results for HB. We then provide explicit bounds on the entropic risk and entropic value at risk of suboptimality at a given iterate which also provides explicit bounds on the probability that the suboptimality exceeds a given threshold based on Chernoff's inequality. Our results unveil fundamental trade-offs between the convergence rate and the risk of suboptimality and result in Heisenberg-style uncertainty principles. We then plug the entropic risk and convergence rate estimates we obtained in a computationally tractable optimization framework and propose entropic risk-averse GMM (RA-GMM) and entropic risk-averse AGD (RA-AGD) methods that can select the GMM parameters to systematically trade-off the entropic value at risk with the convergence rate. We show that RA-AGD and RA-GMM improve performance on quadratic optimization and logistic regression problems compared to the standard choice of parameters. To our knowledge, our work is the first to resort to coherent measures to design the parameters of momentum methods systematically. We will then discuss how these ideas can be leveraged to develop efficient algorithms for non-convex optimization and min-max optimization problems, including those arising in distributionally robust stochastic optimization. Bio: Mert Gurbuzbalaban is an associate professor at Rutgers University. Previously, he was an assistant professor at Rutgers University and 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 network science and data science. He received his B.Sc. degrees in Electrical Engineering and Mathematics as a valedictorian from Bogazici University, Istanbul, Turkey, the “Diplôme d’ingénieur” degree from École Polytechnique, France, and the M.S. and Ph.D. degrees in Mathematics from the Courant Institute of Mathematical Sciences, New York University. Dr. Gurbuzbalaban is a recipient of the Dean's Research Award, Dean's Young Researcher Award, and Dean's Summer Fellowship Award at the Rutgers Business School in 2021 and a co-recipient of the Honorable Mention for the Best Paper Award at the International Conference in Machine Learning (ICML 2019). He also received the Kurt Friedrichs Prize (given by the Courant Institute of New York University for an outstanding thesis) in 2013, the Bronze Medal in the École Polytechnique Scientific Project Competition in 2006, the Nadir Orhan Bengisu Award (given by the electrical-electronics engineering department of Bogazici University to the best graduating undergraduate student) in 2005.

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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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