March 2025 - talks - lists.cs.princeton.edu

CS Colloquium speakers: week of February 26
by Emily C. Lawrence 12 May '25

12 May '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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Ishita Chaturvedi will present her FPO "In the SHADOW of GhOSTs: Cross-Domain Lessons in Parallelism" on April 9, 2025 at 10am in Friend 007 and Zoom
by Gradinfo 28 Mar '25

28 Mar '25

Ishita Chaturvedi will present her FPO "In the SHADOW of GhOSTs: Cross-Domain Lessons in Parallelism" on April 9, 2025 at 10am in Friend 007 and Zoom. The members of her committee are as follows: Examiners: David August (adviser), Margaret Martonosi, David Wentzlaff Readers: David August, Niraj Jha, Sharad Malik Zoom link: [ https://princeton.zoom.us/my/ishita.c | https://princeton.zoom.us/my/ishita.c ] All are welcome to attend. Title: In the SHADOW of GhOSTs: Cross-Domain Lessons in Parallelism Abstract: Modern processors struggle to balance instruction-level parallelism (ILP) and thread-level parallelism (TLP) efficiently. GPUs rely on massive TLP to hide latency but suffer in workloads with low occupancy or control divergence due to limited ILP. CPUs, optimized for ILP, become bottlenecked in memory-bound workloads where TLP could improve resource utilization. This thesis challenges the rigid separation of ILP and TLP by introducing GhOST and SHADOW, two architectures that extend ILP in GPUs and TLP in CPUs to improve execution efficiency across diverse workloads. GhOST introduces lightweight out-of-order (OoO) execution to GPUs, enabling warp-level instruction reordering without speculative execution or register renaming. Its instruction buffer-based reordering mechanism allows instructions to execute as soon as operands are ready, significantly reducing stalls in low-occupancy scenarios and mitigating control divergence overhead. GhOST achieves a 6.9 \ % geometric mean speedup (up to 36%) with only 0.007% area overhead, demonstrating the feasibility of ILP-aware GPU architectures. SHADOW is the first asymmetric simultaneous multithreading (SMT) core that dynamically balances ILP and TLP by executing OoO and in-order (InO) threads simultaneously on the same core. By leveraging deep ILP in the OoO thread and high TLP in lightweight InO threads, SHADOW maximizes CPU utilization without sacrificing single-thread performance. Unlike conventional SMT, which uniformly shares resources, SHADOW dynamically reallocates execution resources based on workload behavior. It achieves up to 3.16x speedup and 1.38x geomean improvement over an OoO CPU , with just 1% area and power overhead }. demonstrating its ability to accelerate memory-bound workloads efficiently. This thesis demonstrates how cross-domain architectural techniques can improve execution efficiency by integrating ILP into GPUs and TLP into CPUs. GhOST and SHADOW reduce execution inefficiencies, enhancing performance in workloads that challenge conventional architectures.

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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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