- talks - lists.cs.princeton.edu

PPH Lunch Seminar: Matt Salganik and Mark Verhagen, tomorrow at 12pm
by Emily C. Lawrence 27 Mar '25

27 Mar '25

Please join the Princeton Precision Health (PPH) Initiative for the next talk of our spring semester series on Friday, March 28 , at 12:00 pm , at 252 Nassau Street. Matt Salganik (Princeton University) and Mark Verhagen (Oxford University) National population registries, large language models, and precision health This talk will present our interdisciplinary, international collaboration exploring the genetic, social, and environmental factors shaping human health. Our approach leverages two innovations: 1) secure access to the Netherlands’ complete population registries within a high-performance computing environment, and 2) open-weight large language models (LLMs). We’ll share our journey from award-winning LLM-based predictive modeling with Dutch registry data to our current work fusing these government records with the Netherlands Twin Registry. This fusion aims to create the world’s largest twin study with comprehensive health information, enabling 1) high-throughput heritability estimates across various health conditions and 2) estimates of the social and environmental contributions to disease by studying identical twins who have different health outcomes. Looking ahead, we plan to incorporate genetic and other “omic” data from 150,000 people to further clarify genetic contributions. Our work operates within a robust ethical and legal framework, which helps foster diverse and growing collaborations across disciplines. Supported in part by the New Jersey Alliance for Clinical and Translational Science. 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 ] Check out our website for our full speaker lineup: [ https://pph.princeton.edu/events | https://pph.princeton.edu/events ]

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March 28 PPH Lunch Seminar: Matt Salganik and Mark Verhagen National population registries, large language models, and precision health
by Emily C. Lawrence 25 Mar '25

25 Mar '25

Please join the Princeton Precision Health (PPH) Initiative for the next talk of our spring semester series on Friday, March 28 , at 12:00 pm , at 252 Nassau Street. Matt Salganik (Princeton University) and Mark Verhagen (Oxford University) National population registries, large language models, and precision health This talk will present our interdisciplinary, international collaboration exploring the genetic, social, and environmental factors shaping human health. Our approach leverages two innovations: 1) secure access to the Netherlands’ complete population registries within a high-performance computing environment, and 2) open-weight large language models (LLMs). We’ll share our journey from award-winning LLM-based predictive modeling with Dutch registry data to our current work fusing these government records with the Netherlands Twin Registry. This fusion aims to create the world’s largest twin study with comprehensive health information, enabling 1) high-throughput heritability estimates across various health conditions and 2) estimates of the social and environmental contributions to disease by studying identical twins who have different health outcomes. Looking ahead, we plan to incorporate genetic and other “omic” data from 150,000 people to further clarify genetic contributions. Our work operates within a robust ethical and legal framework, which helps foster diverse and growing collaborations across disciplines. Supported in part by the New Jersey Alliance for Clinical and Translational Science. 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 ] Check out our website for our full speaker lineup: [ https://pph.princeton.edu/events | https://pph.princeton.edu/events ]

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AI² Research Talk Series-Join us! March 24th, 12 p.m., "Physical Computing in Elastic Metamaterials", Marc Serra-Garcia, AMOLF
by Emily C. Lawrence 24 Mar '25

24 Mar '25

Please join us! 12 p.m., Monday, March 24 th AI² Research Talk Series Bendheim House, Classroom 103 Lunch available at 11:45 a.m. [ https://forms.gle/bbUbLvZ14CDbHvAW9 | Kindly RSVP ] Marc Serra-Garcia (Manresa, 1987) is a tenure-track group leader at the AMOLF research institute in Amsterdam. After a BSc in Physics at Universitat Autònoma de Barcelona , he did a MSc degree at Caltech, and a PhD at ETH Zurich. His research is currently focused on novel (beyond-CMOS) approaches to information processing, with the goal of using a richer and more diverse set of physical phenomena to implement relevant information-processing tasks with low power. The research has been recognized by an ERC Starting grant, and various other grants from the Dutch Research Council and Swiss National Science Foundation. “Physical Computing in Elastic Metamaterials” There is a significant range of physical phenomena—from nonlinear elasticity, to symmetry, noise, topology, and disorder — that are rarely utilized in traditional computing paradigms. Yet these phenomena can unlock new efficiencies, by directly processing signals in their natural domain, and by bypassing the traditional abstraction stack associated with digital CMOS technology. However, building physical computers is challenging. Information processing tasks generally involve complex input-output relations, thus requiring designs that are highly expressive; and for these designs, the relation between function and structure is nontrivial, complicating the simulation, design, and fabrication of devices. In my talk, I will illustrate our journey towards using metamaterials for physical computing, with two recent examples. First, I will talk about our results in passive speech recognition, where we leverage a phononic metamaterial to implement wake-up-word detection with zero standby power consumption. Second, I will discuss our ongoing work in self-learning materials, that autonomously adapt to improve their performance—driven by their ability to form long-term memories in response to examples and external feedback. [ 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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PPH seminar: Niraj Jha, "Bottom-up Medical Superintelligence” TOMORROW @ 12 pm, 252 Nassau
by Emily C. Lawrence 13 Mar '25

13 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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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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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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