- talks - lists.cs.princeton.edu

PPH seminar: Niraj Jha, "Bottom-up Medical Superintelligence” March 14 @ 12 pm, 252 Nassau
by Emily C. Lawrence 10 Mar '25

10 Mar '25

Please join the Princeton Precision Health (PPH) Initiative for the next talk of our spring semester series on Friday, March 14, at 12:00 pm, at 252 Nassau Street. Niraj Jha, PhD, Princeton University, will present a talk, “Bottom-up Medical Superintelligence.” The artificial intelligence (AI) industry is currently focused on achieving superintelligence in a top-down fashion by training a very large (hundreds of billions to trillions of parameters) omniscient multimodal model using a large language model (LLM) as the base. This approach is insatiably thirsty for data during training, leading to unsustainable CO2 emissions. Even after incurring such huge computational and energy costs, these models are known to hallucinate. This makes it difficult to employ such models in domains where accuracy is important, e.g., smart healthcare. We propose to take the opposite tack – build medical superintelligence bottom-up, modeled after how superintelligence is achieved in the human society. Each of us just has human intelligence, but a society of humans achieves superintelligence in a bottom-up fashion by looking at the problem from diverse angles. Could we build medical superintelligence in the same bottom-up fashion through a society of medical AI assistants and AI agents? The AI assistants will serve as aides to health professionals. AI agents will have more autonomy. They need to be accompanied by a robust reasoning framework, e.g., counterfactual (what-if) reasoning. The current top-down approach to developing AI agents is based on using LLMs for reasoning. However, LLMs exhibit a very uneven reasoning performance. Our medical superintelligence framework will take inspiration from neuroscience and include episodic and working memories to facilitate reasoning. The AI assistants in our superintelligence framework will be based on fine-tuned foundation models, targeted at various modalities, e.g., physiological signals, medical images, and medical text, that can be trained data-efficiently and are aligned with each other. The initial goals of the framework are accurate disease detection, individual well-being, interpretability of AI predictions, and personalized medical decision-making. In this talk, we will explore our initial progress towards realizing this vision. 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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CS Colloquium speakers: week of February 26
by Emily C. Lawrence 10 Mar '25

10 Mar '25

CS Colloquium speakers Speaker: Jialin Ding, Amazon Web Services Date: Monday, February 26 Time: 12:30pm EST Location: CS 105 Host: Wyatt Lloyd Event page: https://www.cs.princeton.edu/events/26578 Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_10z_l8MYRr21ZOuPqA8Odw Title: Instance-Optimization: Rethinking Database Design for the Next 1000X Abstract: Modern database systems aim to support a large class of different use cases while simultaneously achieving high performance. However, as a result of their generality, databases often achieve adequate performance for the average use case but do not achieve the best performance for any individual use case. In this talk, I will describe my work on designing databases that use machine learning and optimization techniques to automatically achieve performance much closer to the optimal for each individual use case. In particular, I will present my work on instance-optimized database storage layouts, in which the co-design of data structures and optimization policies improves query performance in analytic databases by orders of magnitude. I will highlight how these instance-optimized data layouts address various challenges posed by real-world database workloads and how I implemented and deployed them in production within Amazon Redshift, a widely-used commercial database system. Bio: Jialin Ding is an Applied Scientist at AWS. Before that, he received his PhD in computer science from MIT, advised by Tim Kraska. He works broadly on applying machine learning and optimization techniques to improve data management systems, with a focus on building databases that automatically self-optimize to achieve high performance for any specific application. His work has appeared in top conferences such as SIGMOD, VLDB, and CIDR, and has been recognized by a Meta Research PhD Fellowship. To learn more about Jialin’s work, please visit [ https://jialinding.github.io/ | https://jialinding.github.io/ ] . CSML/CS/PSY Colloquium Speaker: Brenden Lake, New York University Date: Tuesday, February 27 Time: 12:30pm EST Location: CS 105 Host: Tom Griffiths Event page: https://csml.princeton.edu/events/csmlpsycs-seminar Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_aUfLfVQFSIqV2RDSYu4big Title: Towards more human-like learning in machines: Bridging the data and generalization gaps Abstract: There is an enormous data gap between how AI systems and children learn: The best LLMs now learn language from text with a word count in the trillions, whereas it would take a child roughly 100K years to reach those numbers through speech (Frank, 2023, "Bridging the data gap"). There is also a clear generalization gap: whereas machines struggle with systematic generalization, children can excel. For instance, once a child learns how to "skip," they immediately know how to "skip twice" or "skip around the room with their hands up" due to their compositional skills. In this talk, I'll describe two case studies in addressing these gaps. 1) The data gap: We train deep neural networks from scratch (using DINO, CLIP, etc.), not on large-scale data from the web, but through the eyes and ears of a single child. Using head-mounted video recordings from a child as training data (<200 hours of video slices over 26 months), we show how deep neural networks can perform challenging visual tasks, acquire many word-referent mappings, generalize to novel visual referents, and achieve multi-modal alignment. Our results demonstrate how today's AI models are capable of learning key aspects of children's early knowledge from realistic input. 2) The generalization gap: Can neural networks capture human-like systematic generalization? We address a 35-year-old debate catalyzed by Fodor and Pylyshyn's classic article, which argued that standard neural networks are not viable models of the mind because they lack systematic compositionality -- the algebraic ability to understand and produce novel combinations from known components. We'll show how neural networks can achieve human-like systematic generalization when trained through meta-learning for compositionality (MLC), a new method for optimizing the compositional skills of neural networks through practice. With MLC, neural networks can match human performance and solve several machine learning benchmarks. Given these findings, we'll discuss the paths forward for building machines that learn, generalize, and interact in more human-like ways based on more natural input. Related articles: Vong, W. K., Wang, W., Orhan, A. E., and Lake, B. M (2024). Grounded language acquisition through the eyes and ears of a single child. Science, 383, 504-511. Orhan, A. E., and Lake, B. M. (in press). Learning high-level visual representations from a child’s perspective without strong inductive biases. Nature Machine Intelligence. Lake, B. M. and Baroni, M. (2023). Human-like systematic generalization through a meta-learning neural network. Nature, 623, 115-121. Bio: Brenden M. Lake is an Assistant Professor of Data Science and Psychology at New York University. He received his M.S. and B.S. in Symbolic Systems from Stanford University in 2009, and his Ph.D. in Cognitive Science from MIT in 2014. He was a postdoctoral Data Science Fellow at NYU from 2014-2017. Brenden is a recipient of the Robert J. Glushko Prize for Outstanding Doctoral Dissertation in Cognitive Science and the MIT Technology Review 35 Innovators Under 35. His research was also selected by Scientific American as one of the 10 most important advances of 2016. Brenden's research focuses on computational problems that are easier for people than they are for machines, such as learning new concepts from just a few examples, learning by asking questions, learning by generating new goals, and learning by producing novel combinations of known components. CS Colloquium Speaker: Eric Mitchell, Stanford University Date: Thursday, February 29 Time: 12:30pm EST Location: CS 105 Host: Karthik Narasimhan Event page: https://www.cs.princeton.edu/events/26587 Register for live-stream online here: TBA Talk info TBA

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AI Lab Distinguished Lecture, Professor Eric Xing, TODAY March 7th
by Emily C. Lawrence 07 Mar '25

07 Mar '25

Please join us TODAY for our Distinguished Lecture Series: Eric Xing , President of Mohamed bin Zayed University of Artificial Intelligence, Professor of Computer Science at Carnegie Mellon University, Founder of Petuum Inc., and Co-Founder & Chief Scientist of GenBio AI 2-3:30 p.m., TODAY, March 7, 2025 Maeder Auditorium Reception after the talk [ https://docs.google.com/forms/d/e/1FAIpQLScAuQM_UOPkXY0psLTfHmth8XQSUJCexw4… | RSVP ] Professor Eric P. Xing is the President of the Mohamed bin Zayed University of Artificial Intelligence, Professor of Computer Science at Carnegie Mellon University, Founder of Petuum Inc., and Co-Founder & Chief Scientist of GenBio AI His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in artificial, biological, and social systems. Prof. Xing has served on the editorial boards of leading scientific journals including the Journal of the American Statistical Association, Annals of Applied Statistics, PLOS Journal of Computational Biology, IEEE Journal of Pattern Analysis and Machine Intelligence, Machine Learning Journal, and Journal of Machine Learning Research. He was elected Fellows AAAI, ACM, ASA, IEEE, and IMS. Talk Title: “Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating, and Programming Biology at All Levels” Abstract : At the core of medicine, pharmacy, public health, longevity, agriculture, and environment, is biology at work. Biology in the physical world is too complex to manipulate and always expensive and risky to tamper with. In this talk, I present a vision of using AI to model and simulate biology and life. I will layout an engineering viable approach to construct an AI-Driven Digital Organism (AIDO), leveraging self-supervised pretraining and adaption of large-scale foundation models, and I introduce some early results including 5 SOTA foundation models for DNA, RNA, Protein, Structure, and Single Cell, respectively, and their abilities to tackle biological problems at the full spectrum of granularities, from sequence, to structure, to network, to phenotype, to diseases, and to drug responses. We envision that AIDO opens up a safe, affordable and high-throughput alternative platform for predicting, simulating and programming biology at all levels from molecules to cells to individuals, and is poised to trigger a new wave of better-guided wet-lab experimentation and better-informed first-principle reasoning, which can eventually help us better decode and improve life. [ https://invent.ai.princeton.edu/about/get-involved | AI² Mailing List ] [ https://invent.ai.princeton.edu/about/get-involved | Get Involved ] We are rapidly developing AI for Accelerated Invention activities and programs. Sign up for the mailing list and reach out to us with your questions.

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AI Lab Distinguished Lecture Series: Eric Xing- RSVP!
by Emily C. Lawrence 06 Mar '25

06 Mar '25

We invite you to attend the AI Lab’s upcoming Distinguished Lecture Series, with the first event being held on March 7, 2025. Distinguished Lecture Series Eric Xing, Professor and President of Mohamed bin Zayed University of Artificial Intelligence March 7, 2025 2pm-3:30pm Maeder Auditorium [ https://docs.google.com/forms/d/e/1FAIpQLScAuQM_UOPkXY0psLTfHmth8XQSUJCexw4… | RSVP ] Talk Title: Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating, and Programming Biology at All Levels Abstract: At the core of medicine, pharmacy, public health, longevity, agriculture, and environment, is biology at work. Biology in the physical world is too complex to manipulate and always expensive and risky to tamper with. In this talk, I present a vision of using AI to model and simulate biology and life. I will layout an engineering viable approach to construct an AI-Driven Digital Organism (AIDO), leveraging self-supervised pretraining and adaption of large-scale foundation models, and I introduce some early results including 5 SOTA foundation models for DNA, RNA, Protein, Structure, and Single Cell, respectively, and their abilities to tackle biological problems at the full spectrum of granularities, from sequence, to structure, to network, to phenotype, to diseases, and to drug responses. We envision that AIDO opens up a safe, affordable and high-throughput alternative platform for predicting, simulating and programming biology at all levels from molecules to cells to individuals, and is poised to trigger a new wave of better-guided wet-lab experimentation and better-informed first-principle reasoning, which can eventually help us better decode and improve life. Gina Palmisano Office and Events Coordinator Princeton Laboratory for Artificial Intelligence (AI Lab)

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AI² Research Talk Series - Join Us! TODAY, March 5th, 2025: “Generative AI for Functional Protein Design” with Lei Li
by Emily C. Lawrence 05 Mar '25

05 Mar '25

Please join us: 4:30 p.m., TODAY, March 5th AI² Research Talk Series Bendheim House, Classroom 103 Lei Li is an Assistant Professor in Language Technologies Institute at Carnegie Mellon University. His research focuses on machine translation, agentic LLM, AI security, and AI-powered drug discovery. He received Ph.D. from CMU School of Computer Science in 2011. He is a recipient of the ACL 2021 Best Paper Award and the 2012 ACM SIGKDD dissertation award (runner-up), and is recognized as Notable Area Chair of the ICLR 2023. Previously, he was an associate professor (tenured) at UC Santa Barbara, the Founding Director of ByteDance AI Lab, a principal scientist at Baidu, and a postdoc researcher at UC Berkeley. He developed key technologies deployed in VolcTrans, Xiaomingbot, and TikTok, serving over a billion users. Web: [ https://www.cs.cmu.edu/~leili/ | https://www.cs.cmu.edu/~leili/ ] “Generative AI for Functional Protein Design” Abstract: Generative AI is not only revolutionizing language processing but is also transforming scientific fields such as biology and chemistry, as exemplified by the 2024 Nobel Prize in Chemistry. In this talk, we explore three paradigms for functional protein design: sequence-based, geometry-based, and sequence/structure co-design. We introduce three advanced generative AI models following these paradigms—IsEMPro, SurfPro, and EnzyGen—along with their sample-efficient learning methodologies. Additionally, we highlight four impactful applications: designing enzymes with biocatalytic functions, optimizing proteins for specific fitness goals, developing protein-protein binders, and creating antimicrobial peptides. By emphasizing the powerful synergies between AI and molecular sciences, this talk seeks to inspire collaboration and drive advancements at the forefront of drug discovery, material innovation, and sustainable chemical manufacturing. [ https://invent.ai.princeton.edu/about/get-involved | AI² Mailing List ] [ https://invent.ai.princeton.edu/about/get-involved | Get Involved ] We are rapidly developing AI for Accelerated Invention activities and programs. Sign up for the mailing list and reach out to us with your questions.

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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 and big data has unlocked new possibilities to develop general-purpose AI models. As AI systems like ChatGPT showcase remarkable performance in the digital realm, we are compelled to ask: Can we achieve similar breakthroughs in the physical world — to create generalist robots capable of performing everyday tasks? In this talk, I will present my data-centric research principles and methodologies for building general-purpose robot autonomy in open-world environments. I will discuss my recent work on building compositional robot autonomy stacks with diverse data sources. I will also present a human-in-the-loop framework for trustworthy robot deployment and continual learning. Combining these advances with cutting-edge developments in humanoid robotics, I will outline a roadmap toward the next generation of autonomous robots. Bio: Yuke Zhu is an Assistant Professor in the Computer Science Department of UT-Austin, where he directs the Robot Perception and Learning (RPL) Lab. He also co-leads the Generalist Embodied Agent Research (GEAR) lab at NVIDIA Research, which builds foundation models for embodied agents in virtual and physical worlds, particularly for humanoid robots. He focuses on developing intelligent algorithms for generalist robots and embodied agents to reason about and interact with the real world. His research spans robotics, computer vision, and machine learning. He received his Master's and Ph.D. degrees from Stanford University. His work has won various awards and nominations, including the Best Conference Paper Award in ICRA 2019, 2024, the Outstanding Learning Paper Award at ICRA 2022, and the Outstanding Paper Award at NeurIPS 2022. He received the NSF CAREER Award and faculty awards from Amazon, JP Morgan, and Sony Research.

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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 Accelerating Invention [ https://invent.ai.princeton.edu/about/get-involved | Get Involved ] We are rapidly developing AI for Accelerated Invention activities and programs. Sign up for the mailing list and reach out to us with your questions.

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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://invent.ai.princeton.edu/about/get-involved | Get Involved ] We are rapidly developing AI for Accelerated Invention activities and programs. Sign up for the mailing list and reach out to us with your questions.

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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 AI models, particularly those with state-of-the-art performance, have a high computational cost and memory footprint. This impedes the development of pervasive AI in scenarios lacking sufficient computational resources (e.g., IoT devices, lunar rovers), requiring ultra-fast inference (e.g., AI4Science), or demanding real-time interaction under constrained computation (e.g., AR/VR, Embodied AI). Model compression (quantization, pruning, distillation, etc) and hardware-software co-design are promising approaches to achieving Efficient AI, which makes AI more accessible and run faster. In this talk, I will first introduce my work on 1) mixed-precision quantization based on Hessian analysis (HAWQv1v2, ZeroQ, Q-BERT) and 2) hardware-software co-design (HAWQv3, CoDeNet, HAO). Then I will talk about my ongoing and future works in the era of LLMs and GenAI, including SqueezeLLM, Q-Diffusion, efficient AI agent systems, advanced CoT distillation, efficient deep thinking for OpenAI-o1 and Deepseek-R1, etc. My research vision is that efficient AI is becoming indispensable both at the edge where increasingly powerful sensors with diverse modalities generate huge volumes of local data, and in the cloud where reducing costs is essential to bridge the speed gap between inference scaling laws and Moore’s law for hardware. Bio: Dr. Zhen Dong is currently a Postdoc at UC Berkeley. He obtained his Ph.D. from Berkeley AI Research advised by Prof. Kurt Keutzer. Before Berkeley, Zhen received B.E. from Peking University. Zhen’s research focuses on efficient AI, model compression, hardware-software co-design, and AI systems. Zhen has received Berkeley University Fellowship and SenseTime Scholarship. Zhen has published over 10 papers as the first or co-first author at top AI conferences. He won the best paper award at AAAI Practical-DL workshop, and Zhen is also a winner of the DAC 2024 PhD forum and CVPR 2024 doctoral consortium.

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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, 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

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