October 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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Princeton Robotics Seminar: Russ Tedrake, Friday, Nov 3 at 11am
by Emily C. Lawrence 31 Oct '23

31 Oct '23

Speaker: [ https://groups.csail.mit.edu/locomotion/russt.html | Russ Tedrake ] , MIT Date: Friday, 11/03/23 Time: 11:00 AM ET Location: CS 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Dexterous Manipulation with Diffusion Policies Abstract: At the Toyota Research Institute (TRI), we've been working on behavior cloning for dexterous manipulation. Building on the Diffusion Policy framework that we've recently developed in collaboration with Shuran Song, we now have a very solid pipeline for taking ~50-100 bimanual haptic teleop demonstrations and turning that into a [ https://medium.com/toyotaresearch/tris-robots-learn-new-skills-in-an-aftern… | surprisingly effective visuomotor (+tactile) policy ] . Because there is no explicit state representation required, these skills work equally well manipulating deformable, liquid, or other difficult to model tasks as they do for more traditional rigid-object manipulation. We're actively scaling this up into the multi-task setting and now see a plausible path towards "Large Behavior Models." Bio: Russ Tedrake is the Toyota Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science, Mechanical Engineering, and Aero/Astro, and he is a member of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). He is also the Vice President of Robotics Research at Toyota Research Institute (TRI). He received a B.S.E. in Computer Engineering from the University of Michigan in 1999, and a Ph.D. in Electrical Engineering and Computer Science from MIT in 2004. Dr. Tedrake is the Director of the MIT CSAIL Center for Robotics and was the leader of MIT’s entry in the DARPA Robotics Challenge. He is a recipient of the NSF CAREER Award, the MIT Jerome Saltzer Award for undergraduate teaching, the DARPA Young Faculty Award in Mathematics, the 2012 Ruth and Joel Spira Teaching Award, and was named a Microsoft Research New Faculty Fellow. His research has been recognized with numerous conference best paper awards, including ICRA, Robotics: Science and Systems, Humanoids, Hybrid Systems: Computation and Control, as well as the inaugural best paper award from the IEEE RAS Technical Committee on Whole-Body Control.

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TOMORROW: LLM Forum with Meredith Whittaker (please RSVP)
by Emily C. Lawrence 24 Oct '23

24 Oct '23

Please join us for the first event in the Princeton LLM Forum Series: A Conversation with Meredith Whittaker (President of Signal) on the implications of LLM technology on society. This session's Princeton respondent will be Arvind Narayanan (Professor of Computer Science and Director of the Center for Information and Technology Policy). Wednesday, Oct 25, 4:30 – 6:00 pm Friend Center 101 RSVPs encouraged ( [ https://docs.google.com/forms/d/e/1FAIpQLSdSH04YBq2W-W1nRVxh5DmveM6qY7Khtis… | RSVP link ] ) R eception to follow [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | Add to Calendar ] | [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | More info ] Meredith Whittaker is the President of Signal. She is the current Chief Advisor, and the former Faculty Director and Co-Founder of the AI Now Institute. Her research and advocacy focus on the social implications of artificial intelligence and the tech industry responsible for it, with a particular emphasis on power and the political economy driving the commercialization of computational technology. Prior to founding AI Now, she worked at Google for over a decade, where she led product and engineering teams, founded Google’s Open Research Group, and co-founded M-Lab, a globally distributed network measurement platform that now provides the world’s largest source of open data on internet performance. She has advised the White House, the FCC, FTC, the City of New York, the European Parliament, and many other governments and civil society organizations on artificial intelligence, internet policy, measurement, privacy, and security. The Princeton LLM Forum Recent breakthroughs in Artificial Intelligence (AI) have produced a new class of neural networks called Large Language Models (LLMs) that demonstrate a remarkable capability to generate fluent, plausible responses to prompts posed in natural language. While LLMs have already revolutionized certain industry applications, the debut of ChatGPT has generated new anxiety and curiosity about machine intelligence, especially in the way we teach, research, tell stories, and report facts. Co-organized with the Department of Computer Science and supported by the Humanities Council, the Princeton LLM Forum brings together leading scholars and researchers to discuss the implications that LLMs have on our understanding of language, society, culture, and theory of mind. [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | Read more about the forum and upcoming events in the series. ]

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Princeton Robotics Seminar: Henny Admoni, Friday Oct 20
by Emily C. Lawrence 17 Oct '23

17 Oct '23

Princeton Robotics Seminar Speaker: [ https://hennyadmoni.com/ | Henny Admoni ] , CMU Date: Friday, 10/20/23 Time: 11:00 AM ET Location: CS 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Robots that Learn From and Collaborate with People Abstract: Robots hold the promise of serving human needs, like helping older adults live independently at home or assisting drivers in preventing crashes. For these robots to integrate seamlessly into people's lives, they must provide proactive assistance that is responsive to their human partners' needs. Often, these needs are a result of underlying mental states like intent or awareness. Conversely, it is also useful for people to have an accurate mental model of their robot assistant's policy and knowledge. Mental states may be revealed implicitly through actions the agents take, such as gazing at a certain object or moving in a certain way. This talk describes research on developing collaborative robots that infer people's needs through interaction, adapt to people's individual preferences, and communicate their own models to make the interaction more explainable. These robots are evaluated in a range of human-robot interaction domains, such as manipulation and driving. Bio: Dr. Henny Admoni is an Associate Professor in the Robotics Institute at Carnegie Mellon University, where she leads the Human And Robot Partners (HARP) Lab. Dr. Admoni’s research interests include human-robot interaction, assistive robotics, and nonverbal communication. Dr. Admoni holds a PhD in Computer Science from Yale University, and a BA/MA joint degree in Computer Science from Wesleyan University.

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ECE KORHAMMER SEMINAR SERIES: Wenzhen Yuan, UIUC (Mon., Oct. 23, 2023 @ 4:30 PM, E-Quad B205)
by Emily C. Lawrence 16 Oct '23

16 Oct '23

Speaker: Wenzhen Yuan, UIUC Date: Monday, October 23, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Pramod Viswanath and Jaime Fernández Fisac Event page: [ https://ece.princeton.edu/events/making-sense-physical-world-high-resolutio… | https://ece.princeton.edu/events/making-sense-physical-world-high-resolutio… ] Title: Making Sense of the Physical World with High-Resolution Tactile Sensing Abstract: In this talk, I will introduce the development of a high-resolution robotic tactile sensor GelSight, and how it can help robots understand and interact with the physical world. GelSight is a vision-based tactile sensor that measures the geometry of the contact surface with a spatial resolution of around 25 micrometers, and it also measures the shear forces and torques at the contact surface. With the help of high-resolution information, a robot could easily detect the precise shape and texture of the object surfaces and therefore recognize them. But it can help robots get more information from contact, such as understanding different physical properties of the objects and assisting with manipulation tasks. The talk will cover our work on using GelSight to detect slip during grasping and perceiving object properties such as hardness and viscosity of the liquid. I will also present our work in simulating the tactile sensor and using the simulated sensor input to boost the robot’s capability to perform perception and grasping tasks in reality. These simulation tools can also help us rethink the sensor design challenge and how tactile sensors can be used for various types of robots. Bio: Wenzhen Yuan is an assistant professor at the Computer Science Department at University of Illinois Urbana-Champaign and the director of the CMU RoboTouch Lab. She is a pioneer in high-resolution tactile sensing for robots, and she also works in multi-modal robot perception, soft robots, robot manipulation, and haptics. Yuan received her Master of Science and PhD degrees from MIT and her Bachelor of Engineering degree from Tsinghua University. She also worked as a Postdoctoral researcher at Stanford University and an assistant professor at Carnegie Mellon University. This seminar is supported with funds from the Korhammer Lecture Series.

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Devon Loehr will present his FPO "Lucid: A High-Level, Easy-To-Use Dataplane Programming Language" on Friday, December 1, 2023 in CS 302 at 1pm and Zoom
by Nicki Mahler 11 Oct '23

11 Oct '23

Devon Loehr will present his FPO "Lucid: A High-Level, Easy-To-Use Dataplane Programming Language" on Friday, December 1, 2023 in CS 302 at 1pm and Zoom Zoom link: [ https://princeton.zoom.us/j/97653187562?pwd=TmFNVjl1TWJDMkk5bW5Eaitxb05qUT09 | https://princeton.zoom.us/j/97653187562?pwd=TmFNVjl1TWJDMkk5bW5Eaitxb05qUT09 ] The members of his committee are as follows: Readers: Aarti Gupta and Andrew Appel Examiners: David Walker (Adviser), Jennifer Rexford, and Amit Levy Abstract : The introduction of programmable switches and the P4 language for programming them has made it possible for network operators to write ever-more sophisticated network applications, and execute them at high speed. However, possible is not the same as easy. The de facto standard dataplane programming language, P4, is akin to an “assembly language” for programmable switches: it provides a powerful but low-level interface to switch hardware. While this gives the programmer fine-grained control, it makes it diﬀicult to write and reason about programs with complicated high-level behavior. Furthermore, modern hardware is heavily specialized for packet processing. While this specialization allows blazing fast speeds, it also heavily restricts the sorts of programs a programmable switch can run. Unfortunately, such restrictions are poorly-represented in modern dataplane languages. Should a program violate one or more of these restrictions, it will fail to compile, often with an arcane or unhelpful error message. This dissertation presents Lucid, a high-level, event-based language for programmable switches. Lucid raises the level of abstraction for data-plane program- ming in several ways. Control flow is represented by events, each of which has an associated handler that is executed when the event is generated. Different threads of control may be interleaved easily by writing them as handlers for separate events. Lucid provides high-level representations of switch hardware restrictions, which are enforced by syntactic checks, including a novel type system for detecting ordering violations. Lucid is compiled to P4 for the Intel Tofino, and we find that Lucid programs typically contain around 10 times fewer lines of code than equivalent P4 programs. Furthermore, the Lucid interpreter may be used to quickly evaluate different configurations of a Lucid program, in order to automatically optimize a single program for different networking environments.

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Yebin Chon will present her General Exam "Augmenting Alias Analysis With An Enhanced KillFlow Algorithm" on Wednesday, October 11th, 2023 at 3pm in Friend Center 202
by Gradinfo 06 Oct '23

06 Oct '23

Yebin Chon will present her General Exam "Augmenting Alias Analysis With An Enhanced KillFlow Algorithm" on Wednesday, October 11th, 2023 at 3pm in Friend Center 202. The members of her committee are as follows: David August (adviser), Zachary Kincaid, and Amit Levy Title "A Unified API for Memory Dependence Analysis Tools" Abstract: Stronger memory dependence analysis creates additional and more efficient options for automatic parallelization. CAF, the collaborative analysis framework, developed at Princeton, is to the best of our knowledge the strongest open source memory analysis engine for automatic parallelization of general purpose programs. Using memory profiling, we identify areas of weakness in the CAF memory dependence analysis engine. Despite CAF’s precision, our investigation reveals that its KillFlow module, while effective in many scenarios, faces limitations when dealing with certain loop patterns found in many applications. We propose a stronger KillFlow algorithm that is able to handle dependences involving inner loops, and show how it disproves many dependences not already disproven by CAF. Links to Abstract and Reading List: https://docs.google.com/document/d/1Sd5IqojKpFB24PLRgmyqdrxPRrmodts-0TF_w2K… https://docs.google.com/document/d/1m0vGIihX2A60Z1xRnkTnH7XxSWf7Wn669k9A5tb…

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Julian Ost will present his General Exam "Inverse Neural Rendering for 3D Perception" on Monday, October 9, 2023 at 1:00 PM in CS 301 and via zoom.
by CS Grad Department 04 Oct '23

04 Oct '23

Julian Ost will present his General Exam " Inverse Neural Rendering for 3D Perception " on Monday, October 9, 2023 at 1:00 PM in CS 301 and via zoom. Zoom link: https://princeton.zoom.us/j/94486582110 Committee Members: Felix Heide (advisor), Szymon Rusinkiewicz , Jia Deng Abstract: The most successful methods for image understanding tasks rely on feed-forward neural networks. But despite their empirical accuracy and efficiency, prediction using deep neural networks is not without fundamental disadvantages. Large parts of the networks are often difficult to analyze in failure or borderline cases. This is true in particular when attempting to perceive 3D information from 2D images. Formulating perceptive tasks as a probabilistic generation of world representations has been explored for years under the paradigm of analysis by synthesis, where models of the world help interpret observations. In this exam I present 3D perception tasks recast as an Inverse Neural Rendering problem, reconstructing complex 3D environments to reason about the underlying world captured by images and videos. Since this is particularly important for safety-critical robotics applications like autonomous driving, this will be the focus of the presented work with applications on object detection and 3D multi-object tracking. Recent progress in Neural Rendering and generative models enable the synthesis of plausible representations from input images. But synthesizing dynamic, large-scale outdoor scenes for autonomous driving comes with its own set of challenges that a lot of approaches did not handle. Neural Scene Graphs and Point Light Fields present two solutions to those aspects. Through the integration of generative priors this can be transferred from reconstruction of a single scene to similar scenes, exemplified with work that uses diffusion probabilistic models to generate intermediate layout representation suited to detect objects in a scene and generative object models to efficiently reconstruct objects in a scene and allow more interpretable object tracking utilizing the underlying shape and texture. Reading List: https://docs.google.com/document/d/1e4qtySO8em4Wtjc_VwMfCcsRLqFgJqAAAhl2PLV… 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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DeCenter seminar: Wed Oct 11 at 4:30pm
by Emily C. Lawrence 04 Oct '23

04 Oct '23

DeCenter Seminar: [ https://decenter.princeton.edu/events/decenter-seminar-maria-apostolaki/ | https://decenter.princeton.edu/events/decenter-seminar-maria-apostolaki/ ] Title: Blockchain does not operate in a vacuum: an Internet perspective Speaker: Maria Apostolaki, Princeton University Date: Wednesday, October 11, 2023 - 4:30 pm Location: Friend Center Convocation Room (Friend 113) Reception following talk. [ https://forms.gle/qYVwpKtXmJpXesVD7 | REGISTER ] Abstract: While public blockchains like Bitcoin and Ethereum are celebrated for their security, and decentralized nature, they're built on the insecure and best-effort structures of the Internet. In this talk, we dive into the often overlooked issues arising from the Internet layer, uncovering how centralization and certain assumptions can make blockchains vulnerable. Bad actors can exploit these weak spots, to hinder blockchain functionality, compromise security and user privacy. Beyond these challenges, we'll also spotlight the growing centralization concerns and their implications. But it's not all cautionary tales! We'll share deployable solutions and strategies to fortify blockchains against these threats. Bio: Maria Apostolaki, assistant professor of electrical and computer engineering, joined the Princeton faculty in August 2022 after a one-year postdoctoral position at Carnegie Mellon University. She earned her Ph.D. from ETH Zurich in 2021. Her research draws from networking, security, blockchain, and machine learning. Overall, her goal is to design and build networked systems that are secure, reliable and performant. She was named a Rising Star in Computer Networking and Communications by N2Women. She is a recipient of the Google Research Scholar Award recognizing her work on improving network monitoring.

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Nikhil Pimpalkhare will present his General Exam "Procedure Summarization via Vector Addition Systems and Inductive Linear Bounds" on Thursday, October 5, 2023 at 12pm in CS 301 and Zoom.
by Gradinfo 02 Oct '23

02 Oct '23

Nikhil Pimpalkhare will present his General Exam "Procedure Summarization via Vector Addition Systems and Inductive Linear Bounds" on Thursday, October 5, 2023 at 12pm in CS 301 and Zoom. Zoom link: [ https://princeton.zoom.us/j/3307689819?pwd=SUxlMGtlV2E2bG8wY3lUTC8wbVN3UT09 | https://princeton.zoom.us/j/3307689819?pwd=SUxlMGtlV2E2bG8wY3lUTC8wbVN3UT09 ] The members of his committee are as follows: Zachary Kincaid (adviser), Aarti Gupta, David Walker Abstract and Reading List can be found here: [ https://docs.google.com/document/d/1BjD8cjgEYaMkU6dHA5MIH8PhX_olTwKpbzjTQY3… | https://docs.google.com/document/d/1BjD8cjgEYaMkU6dHA5MIH8PhX_olTwKpbzjTQY3… ]

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