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

CS Colloquium Speakers: week of March 4
by Emily C. Lawrence 09 Apr '24

09 Apr '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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DeCenter Seminar: Andrew Miller, 4/3/24 4:30pm
by Emily C. Lawrence 29 Mar '24

29 Mar '24

[ https://decenter.princeton.edu/ | The DeCenter ] is delighted to extend an invitation to a seminar featuring Andrew Miller on Wednesday April 3rd at 4:30pm . The seminar will be held in room 105 of the Computer Science Building . There will be a light reception to follow. Talk Title: [ https://decenter.princeton.edu/events/decenter-seminar-privacy-preserving-s… | Privacy Preserving Smart Contracts ] Abstract: While smart contracts have catalyzed an exciting burst of permissionless innovation, they are held back by their inability to operate on private data. Fortunately, there are several techniques for addressing this, including hardware (TEEs) and cryptography (MPC, FHE, ZKP). I’ll explain all of these and how they fit together to build private smart contracts, including how they’re used in upcoming projects, Flashbots SUAVE and Cycles credit network. Bio: Andrew Miller is an Associate Professor at the University of Illinois, Urbana-Champaign, in [ https://www.ece.illinois.edu/ | Electrical and Computer Engineering ] and affiliate in [ https://cs.illinois.edu/ | Computer Science ] . He is also an Associate Director of the [ http://initc3.org/ | Initiative for Cryptocurrencies and Contracts (IC3) ] and a board member of the [ http://z.cash.foundation/ | Zcash Foundation ] . Andrew received his Ph.D. from the University of Maryland [ http://www.cyber.umd.edu/ | Cybersecurity Center ] . Additionally, he is the Director of the [ https://decentralize.ece.illinois.edu/ | Decentralized Systems Lab at UIUC! ] His research interests are broadly in computer security and focused on the design of secure decentralized systems and cryptocurrencies. He combines techniques from programming languages, cryptography, and distributed computing.”

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William Pierson Field Lecture: Inioluwa Deborah Raji, Friday, Apr 5 at 1:30pm
by Emily C. Lawrence 28 Mar '24

28 Mar '24

William Pierson Field Lecture Speaker: Inioluwa Deborah Raji, University of California, Berkeley and the Mozilla Foundation Location: Robertson Hall 001 Date: Friday, April 5 Time: 1:30-2:50pm Host: Prof. Lydia Liu Event page: [ https://www.cs.princeton.edu/events/26615 | https://www.cs.princeton.edu/events/26615 ] Title: Audits and Accountability in the Age of Artificial Intelligence Inioluwa Deborah Raji is a Nigerian-Canadian computer scientist and activist who works on algorithmic bias, AI accountability, and algorithmic auditing. She is a Mozilla fellow and Ph.D. student in computer science at University of California, Berkeley, who is interested in questions on algorithmic auditing and evaluation. In the past, she worked closely with the Algorithmic Justice League initiative to highlight bias in deployed AI products. She has also worked with Googleʼs Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on various projects to operationalize ethical considerations in ML engineering practice. In 2000 was named to Forbes 30 Under 30 and MIT Tech Review 35 Under 35 Innovators. Raji’s lecture will be followed by a fireside chat with Lydia Liu, assistant professor of computer science.

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Physics Seminar: David Pfau, Friday, April 5 at 4:30pm
by Emily C. Lawrence 28 Mar '24

28 Mar '24

Physics Seminar Speaker: David Pfau, Google DeepMind Date: Friday, April 5 Time: 4:30pm Location: Jadwin Hall, room A10 Event page: [ https://ml.physics.princeton.edu/ | https://ml.physics.princeton.edu/ ] Title: Computing Quantum Excited States with Deep Neural Networks Abstract: Excited states of quantum systems are relevant for photochemistry, quantum dots, semiconductors and more, but remain extremely challenging to calculate by conventional computational methods. In recent years tools from machine learning have found useful application in computational quantum mechanics, especially in making ground state calculations much more accurate. In this talk I will present a new algorithm for estimating the lowest excited states of a quantum system which works extremely well with deep neural networks. The method has no free parameters and requires no explicit orthogonalization of the different states, instead transforming the problem of finding excited states of a given system into that of finding the ground state of an expanded system. Expected values of arbitrary observables can be calculated including off-diagonal expectations between different states such as the transition dipole moment. We show that by combining this method with the FermiNet and Psiformer Ansatze we can accurately recover vertical excitation energies and oscillator strengths on molecules as large as benzene. Beyond the examples on molecules presented here we expect this technique will be of great interest for applications to atomic, nuclear, and condensed matter physics. Bio: David Pfau is a staff research scientist at Google DeepMind and a visiting professor at Imperial College London in the Department of Physics, where he supervises work on applications of deep learning to computational quantum mechanics. His research interests span artificial intelligence, machine learning and scientific computing. Prior to joining DeepMind, he was a PhD student at the Center for Theoretical Neuroscience at Columbia, where I worked on algorithms for analyzing and understanding high-dimensional data from neural recordings with Liam Paninski and nonparametric Bayesian methods for predicting time series data with Frank Wood. He also had a stint as a research assistant in the group of Mike DeWeese at UC Berkeley, jointly between Physics and the Redwood Center for Theoretical Neuroscience. His current research interests include applications of machine learning to computational physics and connections between differential geometry and unsupervised learning.

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ECE Seminar: Gil Zussman, tomorrow at 4:30pm
by Emily C. Lawrence 27 Mar '24

27 Mar '24

ECE Seminar Speaker: Gil Zussman, Columbia University Day: March 28, 2024 Time: 4:30pm Location: B205, EQuad Hosts: Yasaman Ghasempour and Kaushik Sengupta Event Link [ https://ece.princeton.edu/events/cosmos-testbed-%E2%80%93-platform-advanced… | https://ece.princeton.edu/events/cosmos-testbed-%E2%80%93-platform-advanced… ] Title: The COSMOS Testbed – a Platform for Advanced Wireless, Edge Cloud, Optical, Smart Streetscapes, and International Experimentation Abstract: This talk will provide an overview of the COSMOS testbed (https://cosmos-lab.org), that is being deployed as part of the NSF Platforms for Advanced Wireless Research (PAWR) program, and briefly review various ongoing experiments in the areas of wireless, optical, edge cloud, and smart cities. COSMOS (Cloud-Enhanced Open Software-Defined Mobile-Wireless Testbed for City-Scale Deployment) is being deployed in West Harlem (New York City). It targets the technology "sweet spot" of ultra-high bandwidth and ultra-low latency, a capability that will enable a broad new class of applications including augmented/virtual reality and cloud-based autonomous vehicles. Realization of such high bandwidth/low latency wireless applications involves research not only on radio links, but also on the system as a whole including algorithmic aspects related to spectrum use, networking, and edge computing. We will present an overview of COSMOS' key enabling technologies, which include mmWave radios, software-defined radios, optical/SDN x-haul network, and edge cloud. We will then discuss the deployment and outreach efforts as well as the international component (COSMOS Interconnecting Continents - COSM-IC). Finally, we will describe various experiments that have been conducted in the testbed, including in the areas of edge cloud, mmWave wireless, full-duplex wireless, smart streetspaces, and optical communications/sensing. The COSMOS testbed design and deployment is joint work with the COSMOS team (https://cosmos-lab.org). Bio: Gil Zussman received the Ph.D. degree in Electrical Engineering from the Technion in 2004. Between 2004 and 2007 he was a Postdoctoral Associate at MIT. Since 2007 he has been with Columbia University where he is a Professor of Electrical Engineering and Computer Science (affiliated faculty), and member of Data Science Institute. His research interests are in the area of networking, and in particular in the areas of wireless, mobile, and resilient networks. Gil is an IEEE Fellow and received two Marie Curie fellowships, the Fulbright Fellowship, the DTRA Young Investigator Award, and the NSF CAREER Award. He is a co-recipient of seven best paper awards, including the ACM SIGMETRICS’06 Best Paper Award, the 2011 IEEE Communications Society Award for Advances in Communication, and the ACM CoNEXT’16 Best Paper Award.

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TOMORROW 3/26/2024: ECE SEMINAR Yuandong Tian, Meta AI
by Emily C. Lawrence 25 Mar '24

25 Mar '24

ECE SEMINAR Speaker: Yuandong Tian, Meta AI Title: Towards Efficient, Effective and Explainable AI-guided Decision Making Date: Tuesday, March 26, 2024 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Jason Lee Abstract: Decision-making is ubiquitous in our everyday life and plays an essential role throughout the entire human history. With the emergence of strong or even superhuman Artificial Intelligence (AI), AI-guided decision making becomes a viable choice in situations that require fast yet precise decisions with overwhelming, fast-changing or intricate input, and limited prior experience and resources. Such a high bar demands our AI-guided decision making to be more effective, efficient and explainable. In this talk, I will cover our work ranging from superhuman Go bot to strategy finding in various industrial-level applications such as Antenna Design and Network Planning, from standard RL and optimization formulation to effective representations beyond pre-defined state/action spaces, and from blackbox to explainable improvement for LLMs training and inference. Bio: Yuandong Tian is a Research Scientist and Senior Manager in Meta AI Research (FAIR), working on more efficient training and inference of Large Language Models (LLMs), understanding of LLM, optimization and reinforcement learning. He has been the main mentor of recent works StreamingLLM and GaLore that improves the training and inference of LLM, and the project lead for OpenGo that beats professional players with a single GPU during inference. He is the first-author recipient of 2021 ICML Outstanding Paper Honorable Mentions and 2013 ICCV Marr Prize Honorable Mentions, and also received the 2022 CGO Distinguished Paper Award. Prior to that, he worked in Google Self-driving Car team in 2013-2014 and received a Ph.D in Robotics Institute, Carnegie Mellon University in 2013. He has been appointed as area chairs for NeurIPS, ICML, AAAI, CVPR and AIStats.

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Princeton AI Alignment and Safety Seminar (PASS): TOMORROW at 2pm
by Emily C. Lawrence 25 Mar '24

25 Mar '24

Princeton AI Alignment and Safety Seminar (PASS) YouTube Series: [ https://pli.princeton.edu/events/2024/ai-preparedness | AI Preparedness ] [ https://madry.mit.edu/ | Aleksander Mądry ] Tuesday, March 26 2:00 - 3:00 pm [ https://www.youtube.com/watch?v=pe3XF0Qhx1M | View the livestream ] LINK FOR AUDIENCE TO SUBMIT QUESTIONS: [ http://tinyurl.com/pass-question | tinyurl.com/pass-question ] Abstract: The dramatic increase in the capabilities of our AI systems gives rise to major opportunities but also to risks. How can we prepare to fully take advantage of the former and mitigate the latter? In this talk, I will present my perspective on this challenge and discuss some of the relevant work of OpenAI’s Preparedness team. Bio: [ https://madry.mit.edu/ | Aleksander Mądry ] is the Head of Preparedness at OpenAI. The Preparedness effort is tasked with tracking, evaluating, forecasting, and helping protect against catastrophic risks related to frontier AI models. Aleksander is also a Professor of Computing at MIT (currently on leave), where he has been serving as the Director of the MIT Center for Deployable Machine Learning and a Faculty Co-Lead of the MIT AI Policy Forum. Stay informed and receive seminar reminders by joining our mailing list: [ https://tinyurl.com/pass-mailing | https://tinyurl.com/pass-mailing ] Organized by: [ https://pli.princeton.edu/ | Princeton Language and Intelligence ]

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Kathy Chen will present her FPO "Decoding the sequence basis of gene regulation" on Tuesday, April 9, 2024 at 10:00 AM in Icahn 280 and Zoom.
by Grad Info 22 Mar '24

22 Mar '24

Kathy Chen will present her FPO "Decoding the sequence basis of gene regulation" on Tuesday, April 9, 2024 at 10:00 AM in Icahn 280 and Zoom. Location: Zoom link: https://princeton.zoom.us/j/95565860844?pwd=LzNkcHZ6bnVackxZRnZJSitXdW9NUT09 The members of Kathy's committee are as follows: Examiners: Olga Troyanskaya (Adviser), Mona Singh, Kai Li Readers: Ryan Adams, Jian Zhou (UT Southwestern) Everyone is invited to attend her talk. Abstract follows below: Deciphering the regulatory code of gene expression is a critical challenge in human genetics, instrumental to unlocking the potential of personalized medicine. Modern experimental technologies have resulted in an abundance of high-dimensional genome-wide data, revealing the complex system of epigenetic interactions encoded in the genome. The development of computational approaches which can leverage this vast data to model chromatin interactions globally offer a new understanding of how genomic sequences specify regulatory functions. Specifically, sequence-based deep learning models have become the de facto standard for learning the functional properties encoded in DNA sequences based on large sequencing datasets. These models are powerful tools for interpreting molecular and phenotypic effects, capable of predicting the impact of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterizing their consequences beyond what is tractable from experiments and quantitative genetics alone. In this thesis, we present two deep learning-based sequence models, which predict different epigenetic properties of the genome that contribute to transcriptional regulation. First, Sei is a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequences and variants based on diverse regulatory activities, such as cell type-specific enhancers. Next, we developed a model Hedgehog, which enables the quantification of variation on methylation sites. Hedgehog predicts 296 continuous-valued methylation profiles across a range of cell types and tissues. Hedgehog is complementary to Sei and reveals new insights into the relationship between DNA methylation and other epigenetic modifications. Finally, we show how deep learning-based methods can be applied to elucidate the regulatory basis of human health and disease. Specifically, we use Sei to study the contribution of noncoding mutations in cancer. Collectively, we demonstrate novel frameworks for modeling the sequence dependencies of the epigenome and the capability of such approaches to delineate the regulatory mechanisms underlying complex diseases.

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DeCenter Seminar: Eric Budish, Wed. March 27 at 4:30pm
by Emily C. Lawrence 21 Mar '24

21 Mar '24

The DeCenter is delighted to extend an invitation to a seminar featuring Eric Budish on March 27th at 4:30pm. The seminar will be held in the Friend Center Convocation Room, followed by a light reception. Talk Title : [ https://decenter.princeton.edu/events/decenter-seminar-4/ | Trust at Scale: The Economic Limits of Cryptocurrencies and Blockchains ] Abstract : Satoshi Nakamoto (2008) invented a new kind of economic system that does not need the support of government or rule of law. Trust and security instead arise from a combination of cryptography and economic incentives, all in a completely anonymous and decentralized system. This paper uses a simple three-equation argument to show that this new form of trust, while ingenious, is deeply economically limited. A zero-profits condition on the providers of blockchain trust and an incentive-compatibility condition on the system’s security against attack together imply an equilibrium constraint that the recurring, “flow” cost of blockchain trust must be large relative to the one-off, “stock”-like benefit of attacking the system. Moreover, this equilibrium cost of blockchain trust scales linearly with the value secured — which means that if cryptocurrencies and blockchains were to become more economically useful than they have been to date, then their costs would have to grow to absurd levels. There is a way out of the flow-stock argument, but it is premised on the risk of economic collapse, which is itself a serious problem – pick your poison. The key contrast between Nakamoto trust and traditional trust grounded in rule of law, and complementary sources such as reputations, relationships and collateral, is the economies of scale that arise from credible deterrence as in Hayek (1960) and Becker (1968): Society or a firm pays a fixed cost to enjoy trust over a large quantity of economic activity at low or zero marginal cost. Bio : Eric Budish is the Paul G. McDermott Professor of Economics and Entrepreneurship at the University of Chicago, Booth School of Business. He is also a Research Associate at the National Bureau of Economic Research, Co-Director of the Clark Center for Global Markets at Chicago Booth, and the Co-Director of the Chicago Booth Economics PhD Program. Budish is a leading researcher in the academic field of market design. He is best known for his market design inventions for financial exchanges and matching markets. He has also conducted influential research on market design theory, blockchains and cryptocurrencies, patents and innovation, event ticket markets, and various aspects of Covid-19 economic policy. Budish teaches an MBA class on competitive strategy and PhD classes on market design and the research and writing process. He received his PhD in Business Economics from Harvard University, his MPhil in Economics from Oxford (Nuffield College), and his BA in Economics and Philosophy from Amherst College. Budish gives frequent keynotes on his research and has received numerous academic awards including the Marshall Scholarship, the Sloan Fellowship, giving the joint AEA/AFA luncheon address, the Arrow award, and the AQR insight award.”

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Princeton AI Alignment and Safety Seminar (PASS) with Audience Question Link
by Emily C. Lawrence 19 Mar '24

19 Mar '24

Princeton AI Alignment and Safety Seminar (PASS) Catastrophic misalignment of large language models [ https://paulfchristiano.com/ | Paul Christiano, Alignment Research Center ] Tuesday, March 19 2:00 - 3:00 pm [ https://bit.ly/3IHlz4h | View the livestream ] Link for Audience to Submit Questions: [ http://tinyurl.com/pass-question | tinyurl.com/pass-question ] LINK FOR AUDIENCE TO SUBMIT QUESTIONS: [ http://tinyurl.com/pass-question | tinyurl.com/pass-question ] Abstract: I’ll discuss two possible paths by which AI systems could be so misaligned that they attempt to deceive and disempower their human operators. I’ll review the current state of evidence about these risks, what we might hope to learn over the next few years, and how we could become confident that the risk is adequately managed. Bio: I run the [ http://alignmentresearchcenter.org/ | Alignment Research Center ] . I previously ran the language model alignment team at [ https://openai.com/ | OpenAI ] , and before that received my PhD from the [ http://theory.cs.berkeley.edu/ | theory group ] at UC Berkeley. You may be interested in my [ https://ai-alignment.com/ | writing about alignment ] , my [ http://sideways-view.com/ | blog ] , my [ https://scholar.google.com/citations?hl=en&user=B7oP0bIAAAAJ&view_op=list_w… | academic publications ] , or [ https://paulfchristiano.com/fun-and-games/ | fun and games ] . I am an advisor and board member at [ https://metr.org/ | METR ] , an external advisor to the [ https://www.gov.uk/government/publications/ai-safety-institute-overview/int… | UK AI Safety Institute ] , and a trustee of [ https://www.anthropic.com/index/anthropics-responsible-scaling-policy | Anthropic’s Long-Term Benefit Trust ] . Stay informed and receive seminar reminders by joining our mailing list: [ https://tinyurl.com/pass-mailing | https://tinyurl.com/pass-mailing ] Organized by: [ https://pli.princeton.edu/ | Princeton Language and Intelligence ]

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