October 2024 - talks - lists.cs.princeton.edu

CS Colloquium Speakers: week of March 4
by Emily C. Lawrence 03 Dec '24

03 Dec '24

CS Colloquium Speakers Speaker: Sewon Min, University of Washington Date: Monday, March 4 Time: 12:30pm EST Location: CS 105 Host: Danqi Chen Event page: https://www.cs.princeton.edu/events/26579 Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_5uVGEpXNTVe8eBNSLpxKtg Title: Rethinking Data Use in Large Language Models Abstract: Large language models (LMs) such as ChatGPT have revolutionized natural language processing and artificial intelligence more broadly. In this talk, I will discuss my research on understanding and advancing these models, centered around how they use the very large text corpora they are trained on. First, I will describe our efforts to understand how these models learn to perform new tasks after training, demonstrating that their so-called in context learning capabilities are almost entirely determined by what they learn from the training data. Next, I will introduce a new class of LMs—nonparametric LMs—that repurpose this training data as a data store from which they retrieve information for improved accuracy and updatability. I will describe my work on establishing the foundations of such models, including one of the first broadly used neural retrieval models and an approach that simplifies a traditional, two-stage pipeline into one. I will also discuss how nonparametric models open up new avenues for responsible data use, e.g., by segregating permissive and copyrighted text and using them differently. Finally, I will envision the next generation of LMs we should build, focusing on efficient scaling, improved factuality, and decentralization. Bio: Sewon Min is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Her research focuses on language models (LMs): studying the science of LMs, and designing new model classes and learning methods that make LMs more performant and flexible. She also studies LMs in information-seeking, legal, and privacy contexts. She is a co-organizer of multiple tutorials and workshops, including most recently at ACL 2023 on Retrieval-based Language Models and Applications and upcoming at ICLR 2024 on Mathematical and Empirical Understanding of Foundation Models. She won a paper award at ACL 2023, received a J.P. Morgan Fellowship, and was named an EECS rising star in 2022. Speaker: Lianmin Zheng, University of California, Berkeley Date: Tuesday, March 5 Time: 12:30pm EST Location: CS 105 Host: Ravi Netravali Event page: https://www.cs.princeton.edu/events/26593 Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_ghiT2HbhSPqKBouJ-VwzFg Title: Scalable and Efficient Systems for Large Language Models Abstract: Large Language Models (LLMs) have been driving recent breakthroughs in AI. These advancements would not have been possible without the support of scalable and efficient infrastructure systems. In this talk, I will introduce several underlying systems I have designed and built to support the entire model lifecycle, from training to deployment to evaluation. First, I will present Alpa, a system for large-scale model-parallel training that automatically generates execution plans unifying data, operator, and pipeline parallelism. Next, I will discuss efficient deployment systems, covering the frontend programming interface and backend runtime optimizations for high-performance inference. Finally, I will complete the model lifecycle by presenting our model evaluation efforts, including the crowdsourced live benchmark platform, Chatbot Arena, and the automatic evaluation pipeline, LLM-as-a-Judge. These projects have collectively laid a solid foundation for large language model systems, being widely adopted by leading LLM developers and companies. I will conclude by outlining some future directions of machine learning systems, such as co-optimizing across the full stack for building AI-centric applications. Bio: Lianmin Zheng is a Ph.D. student in the EECS department at UC Berkeley, advised by Ion Stoica and Joseph E. Gonzalez. His research interests include machine learning systems, large language models, compilers, and distributed systems. He builds full-stack, scalable, and efficient systems to advance the development of AI. He co-founded LMSYS.org, where he leads impactful open-source large language model projects such as Vicuna and Chatbot Arena, which have received millions of downloads and served millions of users. He also co-organized the Big Model Tutorial at ICML 2022. He has received a Meta Ph.D. Fellowship, an IEEE Micro Best Paper Award, and an a16z open-source AI grant. Speaker: Emma Dauterman, University of California, Berkeley Date: Thursday, March 7 Time: 12:30pm EST Location: CS 105 Host: Amit Levy Event page: https://www.cs.princeton.edu/events/26585 Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_soNesOKXSFml9MPaTx_1iw Title: Secure systems from insecure components Abstract: In many computer systems today, an attacker that breaks one system component can steal data from millions of users. In this talk, I will present two systems that can withstand component compromise. I will describe (1) a single sign-on system that protects user security and privacy from a compromised single sign-on server, and (2) a secure-hardware-based backup service that protects user backups from compromised secure hardware devices. These systems provide strong security and privacy properties while taking into account practical constraints such as compatibility requirements, hardware limitations, and user expectations. Each splits user secrets across different system components, using new cryptographic tools to provide necessary functionality while protecting user data. Bio: Emma Dauterman is a Ph.D. candidate at UC Berkeley where she is advised by Raluca Ada Popa and Ion Stoica. Her research interests include computer security, systems, and applied cryptography. She has received the Microsoft Research Ada Lovelace fellowship, the NSF graduate research fellowship, and a UC Berkeley EECS excellence award.

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Machine Learning Seminar: Mark Goldstein visiting tomorrow, Nov 1st at 11am
by Emily C. Lawrence 31 Oct '24

31 Oct '24

Hello everyone ! Mark Goldstein is visiting us to give a talk about his research on diffusion models this Friday 11 AM — 12 PM at Friend Center 0 0 8 . Details below . Abstract: This talk will consist of two parts . First , we will discuss continuous diffusion models and the role of noising processes and base distributions , which govern the sequence of densities that a diffusion model must traverse to generate data . These details in turn govern problem difficulty . We will discuss choices that can improve models and reduce the amount of optimization required , in the context of natural images , video generation , Navier -Stokes equations , and protein design . We will then see model likelihood bounds that unify several variants of diffusion models , flow matching , etc . . . Second , we will discuss discrete diffusion models , which have been around in varying forms for a few years but have recently been formalized as proper non -autoregressive generative models for discrete data like text , images with integer pixels , atomic spins , and amino acid sequences . For discrete diffusion models , we will discuss limitations in current practice (sub -optimal factorization choices ) and possible routes and challenges involved in improving the models . Bio: Mark Goldstein is a PhD candidate in Computer Science at New York University working with Rajesh Ranganath , and a current Student Researcher at Google Deepmind . Mark 's work focuses on deep generative models for high -dimensional generation problems , with applications in health and science . Core to this work is rethinking fundamental choices in diffusion model training , and investigating how they affect performance and efficiency . Mark has studied these models in the context of video generation , Navier -Stokes equations , and medical imaging data . Previously , Mark completed a Bachelor 's of Music Composition at New England Conservatory of Music . Best, Wenzhe, Ahmed & Chi 1.

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NextG Webinar: Minjie Chen, Nov 5 at 12pm
by Emily C. Lawrence 29 Oct '24

29 Oct '24

Princeton Faculty Spotlight Series Featured Faculty: Professor Minjie Chen Date: November 5, 2024 Time: 12:00 pm Speaker: Minjie Chen, Princeton University Registration: Participate remotely by visiting [ https://princeton.zoom.us/meeting/register/tJIsfuCqqTotGdKxqOkFvfDbjh6uEDNp… | this link ] WEBINAR Followed by Live Q&A/Discussion. Register [ https://princeton.zoom.us/meeting/register/tJIsfuCqqTotGdKxqOkFvfDbjh6uEDNp… | here ] . Minjie Chen is an Associate Professor of Electrical and Computer Engineering and the Andlinger Center for Energy and the Environment at Princeton University. He received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2015 and his B.S. in Electrical Engineering from Tsinghua University in 2009, both with honors. He His research interests include high-frequency power electronics, power architecture, power magnetics, data-driven methods, and the design of high-performance power electronics for critical applications. Prof. Chen is a recipient of the IEEE PELS Richard M. Bass Outstanding Young Power Electronics Engineer Award, the Princeton SEAS Junior Faculty Award, the NSF CAREER Award, five IEEE Transactions on Power Electronics Prize Paper Awards, and the MIT EECS D. N. Chorafas Ph.D. Thesis Award. He was listed on the Princeton Engineering Commendation List for Outstanding Teaching multiple times. Dr. Chen is an IEEE senior member and the Vice Chair of IEEE PELS TC10 Design Methodologies. Professor Chen will present his research, followed by a Q&A/discussion period. The NextG Faculty Spotlight Series is hosted by Princeton NextG, an initiative of Princeton’s School of Engineering and Applied Sciences. The NextG Initiative at Princeton is creating the foundation for intelligent networks of the future across wireless, backbone networking, and cloud systems. The initiative focuses on cross-disciplinary approaches, bringing theory to practice, and covering the ‘full stack’ from the underlying technological fabric in integrated electronic and photonic circuits and systems, edge networks, IOT and cloud, to foundational theory, algorithms and AI approaches that make these networks scalable, efficient, secure, and accessible.

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DeCenter Book Talk: Monday, Nov 4 at 4:30pm
by Emily C. Lawrence 29 Oct '24

29 Oct '24

On behalf of The Princeton Center for the Decentralization of Power Through Blockchain Technology ( [ https://decenter.princeton.edu/ | DeCenter ] ), The Griswold Center for Economic Policy Studies ( [ https://gceps.princeton.edu/ | GCEPS ] ), and the University Center for Human Values ( [ https://uchv.princeton.edu/ | UCHV ] ), we would like to invite you to an exciting book talk featuring the authors of [ https://www.resistance.money/ | Resistance Money: A Philosophical Case for Bitcoin. ] The event will take place on Monday, November 4th, at 4:30 PM in Robertson Hall’s Arthur Lewis Auditorium (Room 100) . It will be organized and moderated by our DeCenter steering committee faculty member, Andrew Chignell. For more information about the speakers and their bios, please visit our event website: [ https://decenter.princeton.edu/events/book-talk-resistence-money-a-philosop… | https://decenter.princeton.edu/events/book-talk-resistence-money-a-philosop… ] We look forward to your participation! Best, Madelyn LaBuda Administrative and Events Coordinator DeCenter Engineering Quad, C215 Princeton University E: ml2512 [ mailto:email@princeton.edu | @princeton.edu ] M: 570-599-5338

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ACEE Annual Meeting: Tuesday, Oct 29
by Emily C. Lawrence 25 Oct '24

25 Oct '24

Dear colleagues and friends: You are cordially invited to join us on October 29, Tuesday at Andlinger Center for Energy and the Environment 2024 Annual Meeting. This year we will focus on the synergy between Energy and AI. Registration Link: [ https://acee.princeton.edu/2024-annual-meeting/ | https://acee.princeton.edu/2024-annual-meeting/ ] The event is in-person only. Speaker Institutions - Academia: Princeton, Harvard, Clemson, NYU - Industry: Microsoft, Nvidia, Google, AMD, Meta, NEC, ECP - Government: FERC, DOE Speaker List: - Melanie Nakagawa, Chief Sustainability Officer, Microsoft - Jennifer Rexford, Provost, Princeton - Iain McCulloch, Director, ACEE, Princeton Panel #1: Energy and the Future of Computing - David Wentzlaff, Princeton - Tom Gray, NVIDIA - Stephen Kosonocky, AMD - Carole-Jean Wu, Meta - Mark Johnson, Clemson University Panel #2: Power for AI - Jesse Jenkins, Princeton - Allison Clements, Federal Energy Regulatory Commission - Matt DeNichilo, ECP (Energy Capital Partners) - Lucia Tian, Google Panel #3, AI for Power - Minjie Chen, Princeton - Na Li, Harvard University - Tassos Golnas, U.S. Department of Energy - Chris White, NEC Corporation - Egemen Kolemen, Princeton Panel #4, AI for Climate - Gabriel Vecchi, Princeton - Adji Bousso Dieng, Princeton - Reed Maxwell, Princeton - Laure Zanna, New York University -------------------------------- Minjie Chen 2024 ACEE Annual Meeting Faculty co-Chair Minjie Chen Associate Professor Associate Director of Research, ACEE Associate Director of Graduate Studies, ECE Princeton University Zoom: [ https://princeton.zoom.us/j/6177687869 | https://princeton.zoom.us/j/6177687869 ]

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ECE Seminar: Nov. 1: Sudipto Chakraborty, IBM Research
by Emily C. Lawrence 25 Oct '24

25 Oct '24

Speaker: Sudipto Chakraborty, IBM Research Date: November 1, 2024 Time: 4:30 p.m. Location: B205 Engineering Quadrangle Hosts: Kaushik Sengupta Event Page: [ https://ece.princeton.edu/events/challenges-and-opportunities-ultra-low-pow… | https://ece.princeton.edu/events/challenges-and-opportunities-ultra-low-pow… ] Title: Challenges and Opportunities for Ultra-low Power Design for Quantum Computing Applications Abstract: This talk will cover practical challenges for cryogenic CMOS designs for next generation quantum computing. Starting from system level, it will detail the design considerations for a non-multiplexed, semi-autonomous, transmon qubit state controller (QSC) implemented in 14nm CMOS FinFET technology. The QSC includes an augmented general-purpose digital processor that supports waveform generation and phase rotation operations combined with a low power current-mode single sideband upconversion I/Q mixer-based RF arbitrary waveform generator (AWG). Implemented in 14nm CMOS FinFET technology, the QSC generates control signals in its target 4.5GHz to 5.5 GHz frequency range, achieving an SFDR > 50dB for a signal bandwidth of 500MHz. With the controller operating in the 4K stage of a cryostat and connected to a transmon qubit in the cryostat’s millikelvin stage, measured transmon T1 and T2 coherence times were 75.5μS and 73 μS, respectively, in each case comparable to results achieved using conventional room temperature controls. In further tests with transmons, a qubit-limited error rate of 7.76x10-4 per Clifford gate is achieved, again comparable to results achieved using room temperature controls. The QSC’s maximum RF output power is -18 dBm, and power dissipation per qubit under active control is 23mW. Bio: Sudipto Chakraborty (B. Tech, IIT, Kharagpur, 1998, Ph.D from GaTech, 2002) was with Texas Instruments till 2016 where he designed low power IC for >10 product families in automotive/wireless/ medical/microcontrollers. Since 2017 he led the low power circuit design for next generation quantum computing applications in IBM research using nanometer CMOS. He has authored or co-authored >80 papers, two books and 93 US patents. He has served in the TPC including ISSCC, CICC, RFIC, IMS, and is an IBM master inventor. He serves as an AE of the (TCAS – I) and distinguished lecturer in the IEEE CASS and SSCS.

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NextG Distinguished Seminar Series: Arjuna Madanayake, NEW DATE Oct 31
by Emily C. Lawrence 24 Oct '24

24 Oct '24

NextG Distinguished Seminar Series Speaker: Arjuna Madanayake, Florida International University Day: October 31, 2024 Time: 10 :30 a m Location: B205 Engineering Quadrangle Hosts: Yasaman Ghasempour and Kaushik Sengupta Event Page: [ https://ece.princeton.edu/events/spectre-signal-processing-efficient-comput… | https://ece.princeton.edu/events/spectre-signal-processing-efficient-comput… ] Title: SPECTRE: Signal Processing with Efficient Compute for Transmit and Receive Environments Abstract: Wideband radio frequency (RF) array processors form an integral building block of modern wireless communications, location, sensing, and electronic warfare systems. There is interest in multifunctional and frequency agile antenna arrays with fully digital beam processing and machine learning (ML) algorithms at the backend that operate across a wide span of frequencies starting from VHF/UHF up to mm-waves and even sub-THz ranges. Digital signal processing (DSP) across massive bandwidths – today up to about 30 GHz for some sub-THz applications – requires careful consideration of multiple design constraints. Most array DSP operations consist of typical functions, such as discrete Fourier transforms (DFTs), finite impulse response (FIR) filters, cross-correlators, multiply-accumulators, and linear algebra (LA) functions like matrix inversion. Arithmetic complexity of basic DSP algorithms must be carefully factored into their design. A classic example is the use of sparse matrix factorizations on the DFT matrix leading to the suite of fast algorithms known as Fast Fourier Transforms (FFTs) of which the most famous is the Cooley-Tukey FFT invented at Princeton and IBM. The claim to fame for the FFT is its enormously useful factorizations having low arithmetic complexity at O(N log2 N), which is tiny compared to the case of the DFT. The DFT is computed brute-force at arithmetic complexity O(N2) where N is the number of points used. The theoretical lower bounds of DFT complexity have been rigorously proven decades in the past by Heideman and Burrus. Luckily, we can get around these bounds via a sequence of approximations. We achieve O(N) complexity for FFT-like operations by recognizing how real-world RF systems have multiple sources of error. The theoretical complexity bounds of the DFT could be circumvented by computing judiciously tailored low-complexity DFT matrix approximations capable of closely preserving mathematical properties of the exact DFT. In this talk, we discuss the use of approximate DFTs (ADFT) with corresponding sparse factorizations that are free of multiplications. The author and his collaborator RJ Cintra have developed an approach for superfast wideband RF signal processing for FFT applications, including multi-beam spacetime orthogonal beamforming, orthogonal frequency division multiplexing (OFDM) and maximally-decimated uniform-DFT polyphase FIR filterbanks, that replace O(N log2 N) FFTs of Cooley-Tukey fame with factorized ADFTs that are within 2 dB of performance of exact DFT but show much lower arithmetic complexity at just O(N). This extra log2 N reduction beyond what is possible from FFTs – which of course comes at a small price – has massive benefits in both chip area and power consumption for many wideband RF systems. In this talk, the author discusses our recent DARPA, ONR, and NSF funded research activities surrounding low-complexity low-power DSP for real-world RF multi-beam beamforming, low-SWaP spatio-temporal multidimensional spectrum sensing, and low-complexity adaptive digital null-forming using Howells-Applebaum adaptive arrays that make use of the O(N) ADFTs. The talk covers the basic mathematical theory, algorithms, RF/microwave systems, digital compute and FPGA/RF-SoC and chiplet based CMOS realizations underway at Florida International University. Bio: Arjuna Madanayake is an Associate Professor of Electrical and Computer Engineering at Florida International University (FIU), Miami, Florida. He runs the RAND Lab which has 15 PhD students at present. His area of specialization is multidimensional systems and signal processing. Dr. Madanayake’s wider research interests span one- and multi-dimensional signal processing and filter design (both analog and digital), RF/microwave systems, analog-CMOS and analog computer architecture, digital systems and FPGA/RF-SoC systems, fast algorithms and digital ASIC, digital hardware based ``software defined’’ radios, electromagnetics and sub-THz nearfield processing, wireless communications, 5G/6G and mm-wave systems, spectrum, RF artificial intelligence and machine learning (AI/ML) and semiconductor technologies.

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PPH Seminar: “AI in Medical Care: Deus or Machina?” TODAY at 12pm
by Emily C. Lawrence 18 Oct '24

18 Oct '24

Please join the Princeton Precision Health (PPH) Initiative for the next talk of our fall semester series on Friday, October 18, at 12:00 pm, at 252 Nassau Street. William Hanson, MD, Chief Medical Information Officer and Vice President, Hospital of the University of Pennsylvania, will present a talk, “AI in Medical Care: Deus or Machina?” Abstract: In the last 20 years, spurred by governmental incentives (American Recovery and Reinvestment) the digitization of devices and widespread adoption of electronic record keeping systems has transformed medical care for better (and some would argue for worse). Medical care and research has spawned the development of vast troves of digitized clinical and research data. Medical information flows through countless visible and invisible conduits, and has become the new oil, coveted by large companies, research initiatives and governments. This talk will describe the digitization of medicine, the players, the intended and unintended consequences and the potential benefits of AI and precision medicine. It will offer a perspective on what has gone right, what wrong and what entities like Princeton Precision Health can bring to the table. Lunch will be provided. Please note that getting to the seminar space currently requires that you climb a set of stairs. If an accommodation is needed, please contact PPH in advance at: [ mailto:princetonPPH@princeton.edu | princetonPPH(a)princeton.edu ]

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Matthew Weaver will present his FPO "Bicubical Directed Type Theory" on October 31, 2024 at 11am in CS 301.
by Gradinfo 17 Oct '24

17 Oct '24

Matthew Weaver will present his FPO "Bicubical Directed Type Theory" on October 31, 2024 at 11am in CS 301. The members of his committee are as follows: Andrew Appel (Advisor) Daniel Licata (Advisor/Wesleyan University) Zachary Kincaid (Reader) David Walker (Examiner) Aarti Gupta (Examiner) Title: Bicubical Directed Type Theory Directed type theory is an analogue of homotopy type theory where types represent categories, generalizing groupoids. A classical, bisimplicial approach to directed type theory, developed by Riehl and Shulman, is based on equipping each type with both a notion of path and a separate notion of directed morphism. In this setting, a directed analogue of the univalence axiom asserts that there is a universe of covariant discrete fibrations whose directed morphisms correspond to functions—a higher-categorical analogue of the category of sets and functions. While this approach suffices for formalizing mathematics, for applications to computer science we would like a computational interpretation of directed type theory. In this thesis we give a constructive model of a directed type theory with directed univalence in bicubical sets. We formalize much of this in Agda following the approach of Orton and Pitts. First, building on the cubical techniques used to give computational models of homotopy type theory, we show that there is a universe of covariant discrete fibrations with a partial directed univalence principle asserting that functions are a retract of morphisms in this universe. To complete this retraction into an equivalence, we refine the model using Coquand, Ruch, and Sattler’s work on constructive sheaf models. We introduce the cobar modality and by restricting to fibrant types that are also cobar modal, we are able to complete our construction of directed univalence. We then describe a generalization of the fibrant Riehl-Shulman extension types. We prove this in the setting of an arbitrary presheaf category with respect to a new notion of fibrancy that is given by a generic filling problem. This abstraction is general enough to capture all of the current presheaf models of type theories and their classifications of types specified by filling problems. In addition, this result extends the potential syntax of these type theories to be able to internally express any of these filling problems as fibrant types. We use this to then define a type theory in which the user can internally define classifying universes for any such filling problem. Lastly, we overview our implementation of bicubical directed type theory.

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NextG Webinar: Maria Apostolaki, Oct 22 at 12pm
by Emily C. Lawrence 15 Oct '24

15 Oct '24

Faculty Spotlight Series Featured Faculty: Professor Maria Apostolaki Date: October 22, 2024 Time: 12:00 pm Speaker; Maria Apostolaki, Princeton University Registration: Participate remotely by visiting [ https://princeton.zoom.us/meeting/register/tJYucOGqrD4oGdU6LE3hruA8tNc_RD3J… | this link ] Maria Apostolaki is an Assistant Professor of Electrical and Computer Engineering at Princeton University. Her research draws from networking, security, and machine learning. Overall, her goal is to design and build networked systems that are secure, reliable, and performant. Maria has been named a rising star in Computer Networking and Communications and has received the Google Research Scholar Award, Commendation for Outstanding Teaching, and the IETF/IRTF Applied Networking Research Prize. Maria completed her PhD in 2021 at ETH Zurich. During her studies, she was a visiting student at MIT and worked at Microsoft Research and Google. After her PhD, she spent a year at Carnegie Mellon University as a postdoctoral researcher. Prior to her PhD, she earned her diploma in Electrical and Computer Engineering at the National Technical University of Athens, Greece. Professor Apostolaki will present her research on: “Combining ML and Formal Methods for network management.” The presentation will be followed by a Q&A/discussion period open to the audience. The NextG Faculty Spotlight Series is hosted by Princeton NextG, an initiative of Princeton’s School of Engineering and Applied Sciences. The NextG Initiative at Princeton is creating the foundation for intelligent networks of the future across wireless, backbone networking, and cloud systems. The initiative focuses on cross-disciplinary approaches, bringing theory to practice, and covering the ‘full stack’ from the underlying technological fabric in integrated electronic and photonic circuits and systems, edge networks, IOT and cloud, to foundational theory, algorithms and AI approaches that make these networks scalable, efficient, secure, and accessible. Zoom registration required : [ https://princeton.zoom.us/meeting/register/tJYucOGqrD4oGdU6LE3hruA8tNc_RD3J… | https://princeton.zoom.us/meeting/register/tJYucOGqrD4oGdU6LE3hruA8tNc_RD3J… ]

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