- talks - lists.cs.princeton.edu

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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Princeton Robotics Seminar: Lerrel Pinto, Friday Oct 6 at 11am
by Emily C. Lawrence 02 Oct '23

02 Oct '23

Speaker: [ https://www.lerrelpinto.com/ | Lerrel Pinto ] , NYU Date: Friday, October 06, 2023 Time: 11:00 AM ET Location: CS 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Three Lessons for Building General-Purpose Robots Abstract: Over the last decade, a variety of paradigms have sought to teach robots complex and dexterous behaviors in real-world environments. On one end of the spectrum we have nativist approaches that bake in fundamental human knowledge through physics models, simulators and knowledge graphs. While on the other end of the spectrum we have tabula-rasa approaches that teach robots from scratch. In this talk I will argue for the need for better constructivist approaches to robotics, i.e. techniques that take guidance from humans while allowing robots to continuously adapt in changing scenarios. The constructivist guide I propose will focus on three lessons. First, creating physical interfaces to allow humans to provide robots with rich and dexterous data. Second, developing adaptive learning mechanisms to allow robots to continually fine-tune in their environments. Third, architecting models that allow robots to learn from un-curated play. Applications of such a learning paradigm will be demonstrated on mobile manipulators in home environments, industrial robots on precision tasks, and multi-fingered hands on dexterous manipulation. Bio : Lerrel Pinto is an Assistant Professor of Computer Science at NYU. His research interests focus on creating general purpose robotic systems. He received a PhD degree from CMU in 2019; prior to that he received an MS degree from CMU in 2016, and a B.Tech in Mechanical Engineering from IIT-Guwahati. His work on robotics received paper awards at ICRA 2016 and RSS 2023, and finalist awards at IROS 2019 and CoRL 2022. He is a recipient of grants and awards from Amazon, Honda, Hyundai, Meta, LG and Google. More recently, he was named a TR35 innovator under 35 for 2023. Several of his works have been featured in popular media like TechCrunch, MIT Tech Review, Wired, and BuzzFeed among others. His recent work can be found on [ http://www.lerrelpinto.com/ | www.lerrelpinto.com ] .

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Math Dept: Minerva Mini-Course by Alex Lubotzky
by Emily C. Lawrence 02 Oct '23

02 Oct '23

Alex Lubotzky, Weizmann Institute, The Hebrew University, and Minerva Distinguished Visitor High dimensional expanders and Ramanujan complexes Wednesday, Oct 4 Fine Hall Common Room / 12:15 PM – 1:15 PM The seminal work of Howard Garland in the 60’s, proving the conjecture of Serre on vanishing cohomology of lattices in p-adic simple Lie groups reveals that high dimensional expanders (HDX) have a “local to global” phenomenon which does not exist in graphs. Furthermore, the work of L. Lafforgue, generalizing Drinfeld’s work from GL(2)(2) to GL(n)(), called for the construction of Ramanujan complexes. These high dimensional simplicial complexes led to the development of a rich theory with applications in CS and math.

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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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