- talks - lists.cs.princeton.edu

CS Colloquium Speaker: Yuke Zhu - TODAY at 12:30pm
by Emily C. Lawrence 27 Feb '25

27 Feb '25

CS Colloquium Speaker Speaker: Yuke Zhu, University of Texas, Austin Date: Thursday, February 27 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: https://www.cs.princeton.edu/events/pathway-generalist-robot-autonomy-data-… Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_p4RA6oDFTiCVquo7_GGJdg Title: Pathway to Generalist Robot Autonomy — A Data-Centric Approach Abstract: In an era of rapid AI progress, leveraging accelerated computing … [View More]

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Please join us TODAY for AI² Startup Founders Series Livestream Event Featuring Rocky Yu from AGI House. AGI House: “Founding Story and Mission”
by Emily C. Lawrence 26 Feb '25

26 Feb '25

Please join us TODAY for AI² Startup Founders Series Livestream event featuring Rocky Yu from [ https://agihouse.org/ | AGI House ] . AGI House: “Founding Story and Mission” Wednesday, February 26th, 2025, 4:30PM EST [ https://princeton.zoom.us/j/91744912750#success | Zoom Webinar Link ] [ https://www.youtube.com/@princetonai2 | YouTube channel ] [ https://invent.ai.princeton.edu/events | AI² Events ] [ https://invent.ai.princeton.edu/about/get-involved | AI² Mailing List ] AI for … [View More]

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AI² Research Talk Series - TODAY - “Understanding Last Layer Retraining Methods for Fair Classification: Theory and Algorithms” with Lalitha Sankar
by Emily C. Lawrence 25 Feb '25

25 Feb '25

Please join us: AI² Research Talk Series Tuesday, February 25th, 2025 Lunch at 12:15 p.m., seminar starts at 12:30 p.m. Robertson Hall, 002 “Understanding Last Layer Retraining Methods for Fair Classification: Theory and Algorithms” Lilitha Sankar, professor in the School of Electrical, Computer and Energy Engineering at Arizona State University [ https://invent.ai.princeton.edu/events | AI² Events ] [ https://invent.ai.princeton.edu/about/get-involved | AI² Mailing List ] [ https://… [View More]

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CS Colloquium Speaker: Zhen Dong - TODAY at 12:30pm
by Emily C. Lawrence 25 Feb '25

25 Feb '25

CS Colloquium Speaker Speaker: Zhen Dong, University of California, Berkeley Date: Tuesday, February 25 Time: 12:30pm EST Location: CS 105 Host: Kai Li Event page: https://www.cs.princeton.edu/events/make-ai-more-accessible-and-run-faster Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_yrwYu8XfT2-UxV-2RVKGvg Title: Make AI More Accessible and Run Faster Abstract: LLMs and diffusion models have achieved great success in recent years. However, many … [View More]

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Divya Raghunathan will present her FPO "Layered Abstractions for Network Verification and Performance Analysis" on February 24, 2025 at 3pm in CS 302 (and Zoom)
by Gradinfo 20 Feb '25

20 Feb '25

BQ_BEGIN Divya Raghunathan will present her FPO "Layered Abstractions for Network Verification and Performance Analysis" on February 24, 2025 at 3pm in CS 302 and Zoom. Zoom link: Here is the zoom link: [ https://princeton.zoom.us/j/97963140078 | https://princeton.zoom.us/j/97963140078 ] Her committee is as follows: Examiners: Aarti Gupta (Adviser), Zachary Kincaid, and Mae Milano Readers: David Walker and Maria Apostolaki Title: "Formal Methods for Networks: Verification, … [View More]Compilation, and Performance Analysis" Abstract: The scale and complexity of computer networks make their correct operation and analysis challenging. Networks are difficult to configure correctly: router configuration bugs are common, and often cause severe outages or security breaches. Network management tasks (e.g., ensuring compliance with service-level agreements) are challenging, given the diversity of network traffic and limited monitoring information. Formal verification provides tools to improve network reliability, but large network sizes and the vast space of network environments make scalable verification challenging, especially for approaches using Satisfiability Modulo Theories (SMT). Abstractions can improve verification scalability by simplifying the system being verified. Abstractions that over-approximate system behavior enable sound verification that never misses property violations, but could report spurious violations (false positives). Designing effective abstractions that enable scalable verification but rarely report false positives is challenging. This thesis introduces novel abstractions and verification methods for network control plane verification and network performance analysis. Our abstract verification procedures are effective (low false positive rate) and significantly improve verification scalability over state-of-the-art. Our network control plane verifier, ACORN, checks routing-related properties like reachability given network router configurations. ACORN leverages our novel Nondeterministic Routing Choice (NRC) abstractions to simplify the network control plane. We prove that the NRC abstractions are sound for verification, i.e., never miss property violations. With our NRC abstractions, ACORN successfully verifies reachability for up to 28000 routers (in a FatTree topology) within an hour, while state-of-the-art tools do not scale beyond a few thousand routers. Our network performance analysis approach, QuASI, answers performance queries (e.g., burst occurrence) under given measurements. QuASI's first layer, QuASI-approx, uses three novelties: our enqueue-rate abstraction, our reduction over abstract packet traces, and an efficient matrix-based consistency check. We prove QuASI-approx will never incorrectly report a query is unsatisfiable. QuASI's second layer uses SMT constraints to model switch behavior under measurements, leveraging the enqueue-rate abstraction to speed up verification without loss of precision for supported queries. QuASI answers queries up to six orders of magnitude faster than an existing performance analysis tool. BQ_END [View Less]

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Join us TODAY for AI² Startup Founders Series Livestream event featuring Rocky Yu from AGI House
by Emily C. Lawrence 19 Feb '25

19 Feb '25

Please join us TODAY for AI² Startup Founders Series Livestream event featuring Rocky Yu from [ https://agihouse.org/ | AGI House ] . AGI House: “Founding Story and Mission” Wednesday, February 19th, 2025, 4:30PM EST [ https://www.youtube.com/@PrincetonAI2 | https://www.youtube.com/@PrincetonAI2 ] AGI House is a premier hub for AI innovation located in San Francisco, bringing together leading researchers, industry pioneers, and visionary founders to advance Artificial General … [View More]

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DeCenter Seminar - Elaine Shi TODAY at 3:30pm
by Emily C. Lawrence 19 Feb '25

19 Feb '25

TODAY, Wednesday, February 19th, at 3:30 PM , the DeCenter Seminar Series continues with Elaine Shi , a professor of Computer Science and Electrical & Computer Engineering at Carnegie Mellon University. She is interested in cryptography, game theory, algorithms, and the foundations of blockchains. She will present a seminar titled "Decentralized Mechanism Design: Cryptography Meets Game Theory." The event will take place in the Computer Science Building Room 105 . Abstract: In classical … [View More]auction design, we take it for granted that the auctioneer is trusted and always implements the auction’s rules honestly. This assumption, however, no longer holds in modern auctions based on blockchains, or those mediated by third-party platforms such as Google. For example, in blockchain-based auctions, the consensus nodes that partly serve as the auctioneer are incentivized to deviate from honest behavior if profitable. Third-party auction platforms such as Google have also been involved in high-profile anti-trust lawsuits for manipulating their auctions. In this talk, I will describe our recent work on decentralized mechanism design, where we aim to build a new scientific foundation for emerging auctions that are not backed by a trusted auctioneer. I will characterize the mathematical landscape of decentralized mechanism design, by showing several infeasibility and feasibility results. I will also highlight how cryptography can play an essential role for bypassing impossibility results in decentralized mechanism design, leading to a new class of auctions that not only incentivize bidders to act honestly, but also incentivize the auctioneer to play by the book. Bio: Elaine Shi is a Professor at CMU. Prior to joining CMU, she was an assistant professor in UMD and an associate professor in Cornell. Her research interests include cryptography, security, algorithms, game theory, and foundations of blockchains. Elaine’s work has successfully transitioned cryptography and privacy algorithms from on-paper theory to large-scale real-world deployment. For example, her Oblivious RAM algorithm now serves as the backbone of Signal’s private contact discovery service and has also been implemented by Meta. Her differential privacy algorithm has been improved and implemented by companies such as Google and JP Morgan and is a core to Google’s Spanish Gboard. Elaine is a Packard Fellow, a Sloan Fellow, and an ACM Fellow. She has also received several awards such as an ONR YIP award, the NSF CAREER award, and various best paper awards such as the CCS Test of Time award, the NSA Best Scientific Cybersecurity paper, Top Picks in Hardware and Embedded Security, ASPLOS best paper, and others. We hope to see you there! [View Less]

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Indu Panigrahi will present "Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations" on Thurs. February 27, 2025 at 12:30pm in CS 402.
by Gradinfo 18 Feb '25

18 Feb '25

Indu Panigrahi will present "Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations" on Thurs. February 27, 2025 at 12:30pm in CS 402. Adviser: Parastoo Abtahi; Reader: Ruth Fong * Date : Thursday, February 27th, 2025 * Time : 12:30pm * Location : CS 402 * Title : Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations * Abstract : Explanations for … [View More]

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Ilya Chugunov will present his FPO "Neural Field Representations of Mobile Computational Photography" on Friday, February 21, 2025 at 2:00 PM in CS 302.
by CS Grad Department 11 Feb '25

11 Feb '25

Ilya Chugunov will present his FPO " Neural Field Representations of Mobile Computational Photography " on Friday, February 21, 2025 at 2:00 PM in CS 302 . Location: CS 302 The members of Ilya ’s committee are as follows: Examiners: Felix Heide (Adviser), Szymon Rusinkiewicz , Jia Deng Readers: Adam Finkelstein , Ellen Zhong 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 … [View More]

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Divya Raghunathan will present her FPO "Layered Abstractions for Network Verification and Performance Analysis" on February 24, 2025 at 3pm in CS 302 (CORRECTION)
by Gradinfo 10 Feb '25

10 Feb '25

CORRECTION: Divya Raghunathan will present her FPO "Layered Abstractions for Network Verification and Performance Analysis" on February 24, 2025 at 3pm in CS 302. Her committee is as follows: Examiners: Aarti Gupta (Adviser), Zachary Kincaid, and Mae Milano Readers: David Walker and Maria Apostolaki Title: "Formal Methods for Networks: Verification, Compilation, and Performance Analysis" Abstract: The scale and complexity of computer networks make their correct operation and … [View More]analysis challenging. Networks are difficult to configure correctly: router configuration bugs are common, and often cause severe outages or security breaches. Network management tasks (e.g., ensuring compliance with service-level agreements) are challenging, given the diversity of network traffic and limited monitoring information. Formal verification provides tools to improve network reliability, but large network sizes and the vast space of network environments make scalable verification challenging, especially for approaches using Satisfiability Modulo Theories (SMT). Abstractions can improve verification scalability by simplifying the system being verified. Abstractions that over-approximate system behavior enable sound verification that never misses property violations, but could report spurious violations (false positives). Designing effective abstractions that enable scalable verification but rarely report false positives is challenging. This thesis introduces novel abstractions and verification methods for network control plane verification and network performance analysis. Our abstract verification procedures are effective (low false positive rate) and significantly improve verification scalability over state-of-the-art. Our network control plane verifier, ACORN, checks routing-related properties like reachability given network router configurations. ACORN leverages our novel Nondeterministic Routing Choice (NRC) abstractions to simplify the network control plane. We prove that the NRC abstractions are sound for verification, i.e., never miss property violations. With our NRC abstractions, ACORN successfully verifies reachability for up to 28000 routers (in a FatTree topology) within an hour, while state-of-the-art tools do not scale beyond a few thousand routers. Our network performance analysis approach, QuASI, answers performance queries (e.g., burst occurrence) under given measurements. QuASI's first layer, QuASI-approx, uses three novelties: our enqueue-rate abstraction, our reduction over abstract packet traces, and an efficient matrix-based consistency check. We prove QuASI-approx will never incorrectly report a query is unsatisfiable. QuASI's second layer uses SMT constraints to model switch behavior under measurements, leveraging the enqueue-rate abstraction to speed up verification without loss of precision for supported queries. QuASI answers queries up to six orders of magnitude faster than an existing performance analysis tool. [View Less]

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