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DTSTART;TZID=America/Los_Angeles:20260304T140000
DTEND;TZID=America/Los_Angeles:20260304T150000
DTSTAMP:20260528T155810
CREATED:20260226T194755Z
LAST-MODIFIED:20260226T194755Z
UID:10000550-1772632800-1772636400@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Eddy Keming Chen - AI Meets Philosophy of Science: Towards a Foundation of AI
DESCRIPTION:Speaker:Eddy Keming Chen\nDate & Time: Wednesday March 4nd\, 2:00pm\nLocation: HDSI Multipurpose Room 123 \nTitle:  AI Meets Philosophy of Science: Towards a Foundation of AI\n\n\nAbstract: \n Why do simple learning rules yield AI systems that generalize far beyond their training data? I argue that this reflects an abundance of learnable structure in nature\, and that this abundance motivates Nomic Liberalism\, a conception of laws developed from Minimal Primitivism (Chen and Goldstein\, 2022). On this view\, laws can be simple\, predictive\, representation-relative\, existing at many scales and in domains far beyond fundamental physics. Simplicity functions as a nomic razor: an epistemic guide to discovering laws\, rather than a general guide to truth (Chen\, 2024\, 2025).\n\nI show how puzzling phenomena in machine learning—such as double descent\, scaling laws\, and the emergence of AGI from simple objectives such as next-token prediction (Chen\, Belkin\, Bergen\, and Danks\, 2026)—can be understood as learning systems discovering such liberal laws\, often in domains traditionally thought to lack lawful structure\, and in coordinate systems quite unlike familiar human concepts. AI success thus provides concrete evidence for this expanded conception of lawhood. Yet abundance has limits. Recent results in quantum foundations establish in-principle constraints on learning: in high-dimensional quantum systems\, nearly all quantum states are observationally indistinguishable—a limit no learning algorithm can overcome (Chen and Tumulka\, 2025). A satisfactory theory of induction must therefore explain both why learning works so well and why it must sometimes fail.\n\n\n\n\n\nSpeaker Bio: ​”I am an associate professor of philosophy\, a faculty affiliate of the Halıcıoğlu Data Science Institute and the Chinese studies program at the University of California\, San Diego\, and ​a fellow of the John Bell Institute for the Foundations of Physics.\n\nMy primary research interests are foundations of AI\, philosophy of physics\, philosophy of science\, and metaphysics. I am also interested in philosophy of mind\, decision theory\, formal epistemology\, philosophy of mathematics\, philosophy of religion\, and Chinese philosophy. I have a side interest in using films to popularize philosophical ideas. Currently\, I’m co-writing a screenplay about a time-travel romance (inspired by a fascinating article in the SEP).”
URL:https://datascience.ucsd.edu/event/hdsi-seminar-eddy-keming-chen-ai-meets-philosophy-of-science-towards-a-foundation-of-ai/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260303T140000
DTEND;TZID=America/Los_Angeles:20260303T150000
DTSTAMP:20260528T155810
CREATED:20260206T211743Z
LAST-MODIFIED:20260226T195702Z
UID:10000542-1772546400-1772550000@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Jessilyn Dunn - The Digital Physiome: Wearables for Disease Detection and Monitoring
DESCRIPTION:Speaker: Jessilyn Dunn\nDate & Time: Monday March 2nd\, 2:00pm\nLocation: HDSI Multipurpose Room 123\nZoom Link: http://bit.ly/HDSI-Seminars\n\n\n\nTitle:  The Digital Physiome: Wearables for Disease Detection and Monitoring\n\n\nAbstract: Digital health is rapidly expanding due to surging healthcare costs\, deteriorating health outcomes\, and the growing prevalence and accessibility of mobile health and wearable technologies. Recent technological advancements make it possible to closely and continuously monitor individuals using multiple measurement modalities in real time. We are collecting and integrating such wearables data with clinical information to gain a more precise understanding of health and disease and develop actionable\, predictive health models for improving outcomes. We are simultaneously developing open source data science and machine learning tools for the digital health community\, including the Digital Biomarker Discovery Pipeline (DBDP)\, to facilitate the use of mobile device data in healthcare.\n\n\n\n\nSpeaker Bio: Jessilyn Dunn\, PhD\, is an Associate Professor of Biomedical Engineering and Biostatistics & Bioinformatics at Duke University. She directs the BIG IDEAs Lab\, which is focused on digital health innovation\, wearable sensors\, and the development and validation of AI-driven digital biomarkers. Dr. Dunn is the Principal Investigator of research initiatives funded by the NIH\, NSF\, and FDA which are developing digital biomarkers of conditions ranging from pre- and type 2 diabetes to influenza-like illness to Opioid Use Disorder. She sits on the Google Consumer Health Advisory Panel and is a recipient of the NSF CAREER Award and the IEEE EMBS Early Career Achievement Award for her leadership and innovation across engineering and medicine.
URL:https://datascience.ucsd.edu/event/jessilyn-dunn-seminar/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T140000
DTEND;TZID=America/Los_Angeles:20260302T150000
DTSTAMP:20260528T155810
CREATED:20260206T211446Z
LAST-MODIFIED:20260219T170241Z
UID:10000541-1772460000-1772463600@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Emily Aiken - Empirically understanding the relative value of prediction in allocation
DESCRIPTION:Speaker: Emily Aiken\nDate & Time: Monday March 2nd\, 2:00pm\nLocation: HDSI Multipurpose Room 123\n  \nTalk Title:\nEmpirically understanding the relative value of prediction in allocation \nAbstract:\nPublic institutions increasingly use prediction to allocate scarce resources. From a design perspective\, better predictions compete with other investments\, such as expanding capacity or improving treatment quality. Here\, the big question is not how to solve a specific allocation problem\, but rather which problem to solve. In this talk\, I will introduce an empirical toolkit\nto help planners form principled answers to this question and quantify the bottom-line welfare impact of investments in prediction versus other policy levers such as expanding capacity and improving treatment quality. I will then apply the framework to two real-world case studies on predictive methods for allocating humanitarian aid in Bangladesh and Ethiopia. Related papers: this and this. \nSpeaker bio: \nEmily is an assistant professor jointly appointed in HDSI and the School of Global Policy and Strategy. Her research interests are at the intersection of data science and development economics\, with a focus on analyzing large digital traces to inform the design of social protection and humanitarian aid programs. Her work centers on the implementation and evaluation of data-driven allocation systems for aid delivery in low-income countries. \nPrior to joining UC San Diego\, Emily was a postdoctoral scholar at Carnegie Mellon University Africa. She received her PhD from the UC Berkeley School of Information\, and holds an MS in computer science from UC Berkeley and a BA in computer science from Harvard University.
URL:https://datascience.ucsd.edu/event/emily-aiken-seminar/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260225T140000
DTEND;TZID=America/Los_Angeles:20260225T150000
DTSTAMP:20260528T155810
CREATED:20260219T165539Z
LAST-MODIFIED:20260219T165539Z
UID:10000549-1772028000-1772031600@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Zhijian Liu - Efficient AI in the Era of Large Models
DESCRIPTION:Speaker: Zhijian Liu\nDate & Time: Wednesday February 25th\, 2:00pm\nLocation: HDSI Multipurpose Room 123 \nTalk Title:\nEfficient AI in the Era of Large Models \nAbstract:\nLarge foundation models now deliver remarkable capabilities in understanding\, reasoning\, and acting across digital and physical domains. Yet their computational cost has become the primary bottleneck to scaling and real-world deployment. As models expand in size\, context length\, and modality\, efficiency is no longer optional. It is a first-order challenge in algorithms and systems design. \nIn this talk\, I present advances along three complementary directions: parallel decoding\, quantized reasoning\, and structured sparsity. I show how diffusion-based parallel drafting reduces autoregressive latency through speculative decoding; how 4-bit quantization remains near-lossless even under demanding reasoning workloads; and how structured sparsity accelerates long-context inference and long-form reasoning. I conclude with case studies demonstrating how these principles enable efficient multimodal and physical AI systems. Together\, these results show that advances in algorithms and systems design are essential to making large models faster\, more affordable\, and deployable at scale. \nSpeaker Bio: Zhijian Liu is an assistant professor at UCSD. Previously\, he received his Ph.D. and S.M. from MIT and his B.Eng. from Shanghai Jiao Tong University. His research focuses on efficient machine learning and systems. He was selected as the recipient of the Qualcomm Innovation Fellowship. He was also recognized as a Rising Star in ML and Systems by MLCommons and a Rising Star in Data Science by UChicago and UCSD.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-zhijian-liu-efficient-ai-in-the-era-of-large-models/
LOCATION:Halıcıoğlu Data Science Institute Room 123\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260211T110000
DTEND;TZID=America/Los_Angeles:20260211T123000
DTSTAMP:20260528T155810
CREATED:20251002T164405Z
LAST-MODIFIED:20251002T164405Z
UID:10000531-1770807600-1770813000@datascience.ucsd.edu
SUMMARY:HDSI/TILOS Seminar - Maarten de Hoop
DESCRIPTION:Talk Details TBA
URL:https://datascience.ucsd.edu/event/hdsi-tilos-seminar-maarten-de-hoop/
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T140000
DTEND;TZID=America/Los_Angeles:20260209T150000
DTSTAMP:20260528T155810
CREATED:20260116T194400Z
LAST-MODIFIED:20260123T223602Z
UID:10000536-1770645600-1770649200@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series - Xiuyuan Cheng - Wasserstein-regularized learning via neural transport maps: from gradient flow to minimax
DESCRIPTION:Speaker: Xiuyuan Cheng\nDate & Time: Monday February 9th\, 2:00pm\nLocation: HDSI Multipurpose Room 123\n\n\n\nTalk Title: Wasserstein-regularized learning via neural transport maps: from gradient flow to minimax \n\nAbstract: Wasserstein regularization has become a common tool in learning problems over distributions\, while its practical computation remains challenging\, especially in high dimensions. This talk presents a framework for Wasserstein-regularized learning based on directly parameterizing transport maps with neural networks. Rather than working with dual potentials or entropic relaxations\, we explicitly model the transport map\, which enables principled optimization in Wasserstein space as well as scalable out-of-sample evaluation. One example is the implementation of Wasserstein-2 proximal steps via flow networks that realize the Jordan–Kinderlehrer–Otto (JKO) scheme\, yielding a variational interpretation of flow-based generative models. The main focus of the talk is a different regime motivated by distributionally robust optimization (DRO)\, where transport is chosen adversarially through a minimax objective. In this setting\, we establish convergence guarantees for gradient descent–ascent (GDA) dynamics of the Wasserstein minimax problem\, and show how a neural transport map learned from a sample-based matching loss enables direct generation from the worst-case distribution. Together\, these examples highlight both the opportunities and challenges of incorporating Wasserstein regularization into generative modeling\, suggesting that the transport-map formulation offers a flexible and promising way to leverage Wasserstein geometry in learning problems over distributions.\n\nSpeaker Bio: Prof Xiuyuan Cheng currently is Professor of Mathematics at Duke University\, previously as Associate Professor of Mathematics at Trinity College of Arts & Sciences. Prof. Cheng works as an applied analyst\, developing theoretical and computational techniques to solve problems in high-dimensional statistics\, signal processing and machine learning. They are a past recipient of the National Science Foundation CAREER Award as well as the Sloan Research Fellowship-Mathematics.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-xiuyuan-cheng/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260205T150000
DTEND;TZID=America/Los_Angeles:20260205T160000
DTSTAMP:20260528T155810
CREATED:20260116T200658Z
LAST-MODIFIED:20260116T200658Z
UID:10000537-1770303600-1770307200@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series - Stephen Pizer - A geometric model for anatomic objects targeted for statistical analysis
DESCRIPTION:Title:\nA geometric model for anatomic objects targeted for statistical analysis\nSpeaker:\nStephen Pizer\, Kenan Professor of Computer Science\, University of North Carolina at Chapel Hill \nAbstract:\nA novel representation called the evolutionary s-rep represents anatomic objects by encompassing an unusually rich collection of geometric features\, image intensity based\nfeatures\, genetic features\, etc.  It can be used for shape-based classification of anatomic objects\, hypothesis testing on inter-class shape variations\, and object segmentation\, using web-\nresident software. The evolutionary s-rep and example applications will be discussed. This new representation provides better statistics than alternatives because its across-object positional correspondences are based on geometry not just on the object boundary but also in its interior\, on second-order and not just positional (0th order) features\, and on finding for each case a\ndiffeomorphism from a basic\, ellipsoidal object. \nSpeaker Bio\n“My PhD dissertation\, in 1967\, was the first in medical image computing\, and I have been teaching\, writing\, and doing research in that area continuously since my first summer job at Massachusetts General Hospital in 1962 and since my joining the UNC Computer Science faculty in 1967. I have had many collaborations across the UNC Medical School\, in some of whose departments I have had adjunct appointments. I led the committee that led to the formation of the Biomedical Research Imaging Center at UNC. 61 PhDs have been produced with me as principal advisor. \nMy early research emphases were on image quality restoration and on 2D and 3D display\, as well as related models of human vision. I helped advise Charles Metz’s dissertation in that area\, and we collaborated for some years\, including a paper that was an early component of the EM algorithm. However\, for the last 4.5 decades my focus has been on geometric models of anatomic objects\nespecially suited for statistics\, statistical analyses of these\, and the applications of that in diagnosis\, treatment planning\, and object segmentation and registration\, with clinical targets all over the body and with many image sources. Major medical targets were radiation oncology\, neuroscience\, and recently colonoscopy. The form of model I have developed is skeletal\, and in particular what we call the evolutionary s-rep\, with its advantages over object boundary based models of also locally capturing interior properties such as object curvature and cross-object width and of providing an object-relative coordinate system important in accessing image intensities as they are used for segmentation and registration. Towards diagnosis and other statistical objectives\, my statistics professor colleague JS Marron and I have made important contributions in methods of statistics of shape that recognize that object geometry cannot be directly analyzed by Euclidean methods because abstractly it resides on a curved manifold. Most especially\, the method called Principal Nested Spheres (CPNS)\, allowing statistical analysis of directional data\, was developed in our laboratory. Collaborations with Kitware\, Inc. in software development and tutorials related to shape analysis in the salt.slicer.org toolkit including s- reps uses\, especially by Jared Vicory\, have been and continue to be important. Successes of a variety of types for statistics on s-reps include the commercial success of the company\, Morphormics\, now part of\nAccuray\, that we spun off and whose main product at that time\, built upon statistics of skeletal models\, focused on segmentation of male pelvic organs from CT for radiation treatment planning. Other work showed registrations and segmentations of mobile structures across medical imaging modalities. Our work over the last decade or so shows that our latest form of s-reps that evolve from ellipsoids while according to rich geometric properties of the object interior and boundary yield notable improvements over boundary point distribution models and models based on smooth deformations of means for object representation in classification\, hypothesis testing\, and production of correspondence across a population of neuroanatomic objects.”
URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-stephen-pizer-a-geometric-model-for-anatomic-objects-targeted-for-statistical-analysis/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2026/01/steve-p.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251208T140000
DTEND;TZID=America/Los_Angeles:20251208T153000
DTSTAMP:20260528T155810
CREATED:20251002T164243Z
LAST-MODIFIED:20251208T220555Z
UID:10000530-1765202400-1765207800@datascience.ucsd.edu
SUMMARY:HDSI Distinguished Seminar - Ila Fiete
DESCRIPTION:Speaker: Ila Fiete\nDate & Time: Mon Dec 8th\, 2:00pm\nNEW LOCATION:  Jacobs Hall\, Qualcomm Conference Center B\, 1st Floor \nAbstract: Modular and hierarchical structures are ubiquitous in the brain\, and the decomposition of tasks into invariant subfactors of variation are arguably the basis for robust\, efficient learning\, compositional generalization\, and lack of forgetting. In this talk\, I will describe simple mechanisms for the emergent self-organization of structures for such computation\, arising from local competition and fault-tolerance constraints. I will show how these principles are implemented in brains\, leading to qualitative and topologically robust predictions about brain organization. I will discuss how these biological principles can be abstracted to drive modularity in artificial neural networks\, and show how modular organization can serve as an inductive bias for world structure learning\, including the discovery of vanishingly few modular solutions to modular problems in the space of all possible solutions. \nBio: Ila Fiete is a professor of brain and cognitive sciences\, associate member of the McGovern Institute\, and director of the K. Lisa Yang ICoN Center at MIT. Fiete earned a BS in mathematics and physics at the University of Michigan\, obtaining her PhD in physics at Harvard University in 2004. She conducted her postdoctoral work at the Kavli Institute for Theoretical Physics at the University of California\, Santa Barbara while she was also a visiting member of the Center for Theoretical Biophysics at the University of California\, San Diego. Fiete subsequently spent two years at Caltech as a Broad Fellow in brain circuitry\, then joined the faculty of the University of Texas at Austin before coming to MIT in 2019.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-ila-fiete/
LOCATION:Qualcomm Conference Room at JSOE\, Jacobs Hall\, 9736 Engineers Ln\, La Jolla\, San Diego\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251203T110000
DTEND;TZID=America/Los_Angeles:20251203T123000
DTSTAMP:20260528T155810
CREATED:20251002T164101Z
LAST-MODIFIED:20251002T164101Z
UID:10000529-1764759600-1764765000@datascience.ucsd.edu
SUMMARY:HDSI/TILOS Seminar - Jeremy Schwartz
DESCRIPTION:Talk Details TBA
URL:https://datascience.ucsd.edu/event/hdsi-tilos-seminar-jeremy-schwartz/
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251117T140000
DTEND;TZID=America/Los_Angeles:20251117T153000
DTSTAMP:20260528T155810
CREATED:20251002T163926Z
LAST-MODIFIED:20251029T222512Z
UID:10000528-1763388000-1763393400@datascience.ucsd.edu
SUMMARY:HDSI Distingushed Seminar - Yisong Yue
DESCRIPTION:TITLE Design\, Measure\, Interpret: Foundation Models in the Scientific Loop \nABSTRACT “As foundation models become powerful scientific priors\, a central question emerges: how can they drive the full cycle of discovery—from designing experiments to interpreting results? This talk presents a probabilistic framework that unites experiment design and inverse problems under a common foundation model. I will highlight recent progress in adaptive design algorithms and diffusion-based inversion methods\, and discuss how these ideas point toward an AI-driven ecosystem for science.” \nBIO Yisong Yue is a Professor of Computing and Mathematical Sciences at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that\, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong previously served as Senior Program Chair of ICLR 2024 and General Chair of ICLR 2025\, and currently serves on the ICLR board. Yisong’s research interests are centered around machine learning and artificial intelligence\, particularly in getting AI to work in high-stakes and high-expertise domains. To that end\, his research agenda spans both fundamental and applied pursuits\, from novel learning frameworks all the way to deployment in autonomous driving on public roads. His work has been recognized with multiple paper awards and nominations\, including in robotics\, computer vision\, sports analytics\, machine learning for health\, and information retrieval. During his time in industry\, Yisong worked on machine learning approaches to behavior modeling and motion planning for autonomous driving.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-yisong-yue/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251112T110000
DTEND;TZID=America/Los_Angeles:20251112T120000
DTSTAMP:20260528T155810
CREATED:20251106T170549Z
LAST-MODIFIED:20251106T170549Z
UID:10000534-1762945200-1762948800@datascience.ucsd.edu
SUMMARY:TILOS-HDSI Seminar: Adam Oberman - AI Safety Theory: The Missing Middle Ground
DESCRIPTION:The next TILOS-HDSI seminar will be Wednesday\, November 12 at 11am PST with Adam Oberman (McGill University). The title is AI Safety Theory: The Missing Middle Ground. \nTalk Information \nSpeaker: Adam Oberman (McGill University) \nDate & Time: Wednesday\, November 12 @ 11am PST \nVenue: HDSI 123 \nAbstract: Over the past few years\, the capabilities of generative artificial intelligence (AI) systems have advanced rapidly. Along with the benefits of AI\, there is also a risk of harm. In order to benefit from AI while mitigating the risks\, we need a grounded theoretical framework. \nThe current AI safety theory\, which predates generative AI\, is insufficient. Most theoretical AI safety results tend to reason absolutely: a system is a system is “aligned” or “mis-aligned”\, “honest” or “dishonest”. But in practice safety is probabilistic\, not absolute. The missing middle ground is a quantitative or relative theory of safety — a way to reason formally about degrees of safety. Such a theory is required for defining safety and harms\, and is essential for technical solutions as well as for making good policy decisions. \nIn this talk I will: \n\nReview current AI risks (from misuse\, from lack of reliability\, and systemic risks to the economy) as well as important future risks (lack of control).\nReview theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.\nExplain why technical and theoretical safety solutions are valuable\, even by contributors outside of the major labs.\nDiscuss some gaps in the theory and present some open problems which could address the gaps.\n\nBio: Adam Oberman is a Full Professor of Mathematics and Statistics at McGill University\, a Canada CIFAR AI Chair\, and an Associate Member of Mila. He is a research collaborator at LawZero\, Yoshua Bengio’s AI Safety Institute. He has been researching AI safety since 2024. His research spans generative models\, reinforcement learning\, optimization\, calibration\, and robustness. Earlier in his career\, he made significant contributions to optimal transport and nonlinear partial differential equations. He earned degrees from the University of Toronto and the University of Chicago\, and previously held faculty and postdoctoral positions at Simon Fraser University and the University of Texas at Austin.
URL:https://datascience.ucsd.edu/event/tilos-hdsi-seminar-adam-oberman-ai-safety-theory-the-missing-middle-ground/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:HDSI Event,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251110T140000
DTEND;TZID=America/Los_Angeles:20251110T143000
DTSTAMP:20260528T155810
CREATED:20251002T163809Z
LAST-MODIFIED:20251104T223610Z
UID:10000527-1762783200-1762785000@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Tianhao Wang
DESCRIPTION:Speaker: Tianhao Wang\n\nDate & Time: Monday Nov 10th\, 2pm\nLocation: HDSI Multipurpose Room 123\, 1st floor \n\n\nTitle: Adaptive Optimizers: From Structured Preconditioners to Adaptive Geometry\n\nAbstract: Adaptive optimizers such as Adam and Shampoo are workhorses of modern machine learning\, enabling efficient training of large-scale models across architectures and domains. In this talk\, we will present a unified framework for adaptive optimizers with structured preconditioners\, encompassing a variety of existing methods and introducing new ones. Our analysis reveals the fundamental interplay between preconditioner structures and loss geometries\, highlighting in particular that more adaptivity is not always helpful. Furthermore\, the dominance of adaptive methods has recently been challenged by the surprising effectiveness of simpler normalized steepest descent (NSD)–type methods such as Muon\, while a consensus has emerged that both families of methods succeed by exploiting the non-Euclidean geometry of the loss landscape. Building on the proposed framework\, we show that the convergence of adaptive optimizers is governed by a notion of adaptive smoothness\, which contrasts with the standard smoothness assumption leveraged by NSD. In addition\, although adaptive smoothness is a stronger condition\, it enables acceleration via Nesterov momentum\, which cannot be achieved under the standard smoothness assumption in non-Euclidean settings. Finally\, we develop a notion of adaptive gradient variance that parallels adaptive smoothness and yields qualitatively improved guarantees compared to those based on standard gradient variance.\n\n\nSpeaker Bio: Tianhao Wang is an Assistant Professor at the Halıcıoğlu Data Science Institute\, University of California\, San Diego. Prior to UCSD\, he was a Research Assistant Professor at Toyota Technological Institute at Chicago. He received his PhD from the Department of Statistics and Data Science at Yale University in 2024. His research focuses on theoretical foundations at the intersection of deep learning\, optimization\, and statistics.\nMore info is available on Professor Wang’s website: https://tiiao.github.io/
URL:https://datascience.ucsd.edu/event/hdsi-seminar-tianhao-wang/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251017T100000
DTEND;TZID=America/Los_Angeles:20251017T113000
DTSTAMP:20260528T155810
CREATED:20251002T162628Z
LAST-MODIFIED:20251002T162628Z
UID:10000525-1760695200-1760700600@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Anupam Datta - What is your Agent’s GPA? Toward Trustworthy Data Agents 
DESCRIPTION:Speaker: Anupam Datta\nDate & Time: Friday Oct 17th\, 10am\nLocation: HDSI Multipurpose Room 123\, 1st floor \nTalk title: What is your Agent’s GPA?\nSubtitle: Toward Trustworthy Data Agents \nAbstract: We introduce the Agent GPA (Goal-Plan-Action) framework: an evaluationparadig m based on an agent’s operational loop of setting goals\, devising plans\, and executing actions. The framework includes five evaluation metrics: Goal Fulfillment\, Logical Consistency\, Execution Efficiency\, Plan Quality\, and Plan Adherence. Logical Consistency checks that an agent’s actions are consistent with its prior actions. Execution Efficiency checks whether the agent executes in the most efficient way to achieve its goal. Plan Quality checks whether an agent’s plans are aligned with its goals; Plan Adherence checks if an agent’s actions are aligned with its plan; and Goal Fulfillment checks that agent’s final outcomes match the stated goals. Our experimental results on two benchmark datasets – the public GAIA dataset and an internal dataset for a production-grade data agent – show that this framework (a) provides a systematic way to cover a broad range of agent failures\, including all agent errors on the GAIA benchmark dataset; (b) exhibits strong agreement between human and LLM judges\, ranging from 80% to over 95%; and (c) localizes errors with 86% agreement with human annotations to enable targeted improvement of agent performance. \nBio: Anupam Datta is a Principal Research Scientist and Snowflake AI Research Lead at Snowflake. He joined Snowflake as part of the acquisition of TruEra where he served as Co-Founder\, President\, and Chief Scientist from 2019-2024. Datta was on the faculty at Carnegie Mellon University from 2007-2022\, most recently as a tenured Professor of Electrical & Computer Engineering and Computer Science. Datta’s current research focuses on Trustworthy AI\, spanning evaluation\, explainability\, fairness\, and adversarial robustness of ML models and GenAI applications. Specific results include early work on Shapley Values & gradient-based explanations\, fairness assessments\, robustness of classical machine learning and deep learning models for natural language processing and computer vision\, and the TruLens open source project for evaluation and experiment tracking of GenAI apps. These research results have had a significant impact on products at TruEra and Snowflake. Datta has published over 100 research papers\, served as Chair of the National Academies Workshop on Assessing and Improving AI Trustworthiness\, on the Steering Committee of of the ACM Conference on Fairness\, Accountability\, and Transparency\, and the IEEE Computer Security Foundations Symposium\, and as an Editor-in-Chief of Foundations and Trends in Privacy and Security. He received the 2018 David P. Casasent Outstanding Research Award from the CMU College of Engineering\, a 2020 Young Alumni Achiever Award from IIT Kharagpur\, a 2021 Google Faculty Research Award\, and several awards for top papers at conferences. Datta obtained a B.Tech. from IIT Kharagpur\, and Ph.D. and M.S. degrees from Stanford University in Computer Science\, where he currently teaches a course on Trustworthy AI.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-anupam-datta-what-is-your-agents-gpa-toward-trustworthy-data-agents/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Special Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251007T140000
DTEND;TZID=America/Los_Angeles:20251007T150000
DTSTAMP:20260528T155810
CREATED:20250929T215834Z
LAST-MODIFIED:20250929T215856Z
UID:10000524-1759845600-1759849200@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Dragan Radulović - A New Paradigm for the Analysis of Large Text Files
DESCRIPTION:Speaker:Dragan Radulović\nDate & Time: Tuesday Oct 7th\, 2pm\nLocation: HDSI Multipurpose Room 123\, 1st floor\n\n \nTitle:  A New Paradigm for the Analysis of Large Text Files\n \nTalk Abstract: The problem is as follows: a large text file containing information on thousands of individuals serves as the input. The output is a simple yes-or-no prediction. For example\, the algorithm might receive a new patient’s file and must provide a prognosis—yes or no—for a given disease (or treatment\, or test\, etc.). I have developed a rather unusual (and quite peculiar) method for doing this. The algorithm has been successfully used for several years by an (undisclosed) American professional sports team. It has never been published\, and until recently\, I was not even allowed to share it with anyone. Now that the confidentiality clause in my agreement has expired\, I am free to share it with the world.\n \nSpeaker Bio: Dragan Radulović is a mathematician specializing in probability on Banach spaces\, empirical processes\, and copula functions. Parallel to this more theoretical career\, Dragan also explores applications—particularly in data analysis. He was the principal mathematician at the successful startup Quantiva (Princeton\, 1999–2003)\, where he designed a suite of algorithms tailored to detecting anomalies in internet traffic. From 2002 to 2011\, he worked on problems in molecular biology. In this area\, he was the first author of several high-profile papers (Nature Genetics\, PLOS Biology\, Cancer Informatics). His key innovation was a novel algorithm that analyzes mass spectrometry data to provide protein quantification—something that was not possible at the time.\n\n\n\n\nMore recently\, he worked as a contractor for the Chicago Blackhawks\, a professional hockey team. There he designed a suite of algorithms that processed large numerical and textual datasets collected by scouts and hockey professionals. The output of these algorithms was predictive modeling of players’ future performances. \nDragan Radulović is also an author. His first book\, On the Road Again (2018)\, recounts his road trip through Iran and Afghanistan. His second book\, Why Does Math Work? (Cambridge\, 2023)\, received praise in the Notices of the American Mathematical Society: \n“If you have wondered about the philosophical underpinnings of mathematics\, this book is for you. It contains insightful queries for a mathematician to ponder and could definitely be the start of some enlightening conversations\, perhaps in a departmental book club or seminar course. I found myself enjoying the many tangents (pun intended!) and digressions in this wonderfully unique and well-articulated book.” —Emily J. Olson\, Notices of the American Mathematical Society \nDragan has had stints at Princeton University and Yale University. He later moved to South Florida\, where he surfs\, writes\, and does mathematics.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-dragan-radulovic-a-new-paradigm-for-the-analysis-of-large-text-files/
LOCATION:Halıcıoğlu Data Science Institute Room 123\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250929T140000
DTEND;TZID=America/Los_Angeles:20250929T153000
DTSTAMP:20260528T155810
CREATED:20250918T200519Z
LAST-MODIFIED:20250918T205524Z
UID:10000523-1759154400-1759159800@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Regina Liu - Fusion Learning: Fusing Inferences from Diverse Data Sources
DESCRIPTION:HDSI will be hosting its first Seminar Series speaker of the academic year at the end of this month. Regina Liu (Rutgers University) will be giving a talk Monday Sept 29th at 2pm in the HDSI Multipurpose room\, 1st floor Room 123. \n\nSpeaker: Regina Liu\nDate & Time: Monday Sept 29th\, 2pm\nLocation: HDSI Multipurpose Room 123\, 1st floor \n\nTalk Title: Fusion Learning: Fusing Inferences from Diverse Data Sources\n\nAbstract: \nAdvanced data acquisition technology has greatly increased the accessibility of complex inferences\, based on summary statistics or sample data\, from diverse data sources. Fusion learning refers to combining complex inferences from multiple sources to yield a more effective overall. We focus on the tasks: 1) Whether/When to combine inferences? 2) How to combine inferences efficiently? 3) How to combine inferences to enhance an individual study\, thus named i-Fusion?\n\nWe present a general framework for nonparametric and efficient fusion learning. The main tool underlying this framework is the new notion of depth confidence distribution (depth-CD)\, developed by combining data depth\, bootstrap and confidence distributions. We show that a depth-CD is an omnibus form of confidence regions\, whose contours of level sets shrink toward the true parameter value\, and thus an all-encompassing inferential tool. The approach is efficient\, general and robust\, and readily applies to heterogeneous studies covering a broad range of complex settings. The approach is demonstrated with an aviation safety analysis application in tracking aircraft landing performance and a zero-event studies in clinical trials with non- estimable parameters. \n\nKey words: confidence distribution\, data depth\, fusion learning\, heterogeneous studies\n\nSpeaker Bio:\nRegina Liu is Distinguished Professor\, Rutgers University. Her research areas include data depth\, resampling\, nonparametric statistics\, confidence distribution\, and fusion learning. Aside from theoretical and methodological research\, she has long collaborated with the FAA on aviation safety research projects on process control\, text mining and risk management. \nShe is an elected fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). She is the recipient of 2021 Noether Distinguished Scholar Award (ASA)\, 2024 Elizabeth Scott Award (Committee of Presidents of Statistical Societies (COPSS))\, and the IMS 2025 Neyman Award & Lecture. She has served as Co-Editor for the Journal of the American Statistical Association and as Associate Editor for several journals. She was elected President of the Institute of Mathematical Statistics (IMS)\, 2020-2021.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-reginia-liu-fusion-learning-fusing-inferences-from-diverse-data-sources/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250528T130000
DTEND;TZID=America/Los_Angeles:20250528T143000
DTSTAMP:20260528T155810
CREATED:20250519T164145Z
LAST-MODIFIED:20250519T164145Z
UID:10000521-1748437200-1748442600@datascience.ucsd.edu
SUMMARY:Shuang Hao - Empowering and Strengthening Security in the AI Era
DESCRIPTION:When Wednesday May 28th\, 1pm\nWhere: HDSI 1st Floor Multipurpose Room 123 \n\nTitle: Empowering and Strengthening Security in the AI Era\n \nAbstract: Revolutionary advances in artificial intelligence (AI) techniques have led to promising applications and widespread deployment accessible to users. However\, AI techniques are increasingly being abused by cybercriminals\, such as creating synthetic content for scams or injecting malicious instances into services. It is imperative to cultivate systematic analysis and defenses against security threats in the era of AI.\nIn this talk\, I will describe my research on developing empirical-theoretical approaches to address AI abuses and attacks. First\, I will introduce the approaches of leveraging user intelligence to characterize and detect AI-generated face images\, enabling human-AI collaboration to strengthen security of generative AI. Second\, I will describe the analysis of attacks exploiting machine unlearning in the AI ecosystem\, and quantify model degradation and risks in unlearning scenarios. My research builds systematic approaches and principled solutions to advance AI security. \n \nBio: Shuang Hao is an Associate Professor of Computer Science at the University of Texas at Dallas. He obtained his Ph.D. from the Georgia Institute of Technology\, and he was a postdoctoral scholar at the University of California\, Santa Barbara before joining UT Dallas. His research interests are in security and its intersection with AI\, data science\, and user behavior analysis. His current research focuses on designing data-driven approaches to advance security in the AI ecosystem. He has published extensively in top-tier security conferences including S&P\, USENIX Security\, CCS\, and NDSS. He has received multiple awards and recognitions\, including an NSF CAREER Award\, an IETF Applied Networking Research Prize\, a DSN Best Paper Award\, an IMC Best Paper Award Runner-up\, two-time CSAW Best Security Paper Award Finalist\, and a Yahoo! Key Scientific Challenges Program Award. His work has been featured in media outlets such as MIT Technology Review\, Slashdot\, Fortune\, CNN\, and The Wall Street Journal. More about his research can be found at https://www.utdallas.edu/~shao/
URL:https://datascience.ucsd.edu/event/shuang-hao-empowering-and-strengthening-security-in-the-ai-era/
LOCATION:Halıcıoğlu Data Science Institute Room 123\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250423T130000
DTEND;TZID=America/Los_Angeles:20250423T143000
DTSTAMP:20260528T155810
CREATED:20250404T162241Z
LAST-MODIFIED:20250404T162241Z
UID:10000517-1745413200-1745418600@datascience.ucsd.edu
SUMMARY:LIVED EXPERIENCE RESEARCH SUMMIT
DESCRIPTION:Location: HDSI MPR \nTo watch via zoom please contact: hdsiassistant@ucsd.edu \nFormerly Incarcerated professor speaking on his lived experience in research. Researchers from\nSmarr Lab and MOSAIC lab. \nNoel Vest\, PhD\, is an Assistant Professor at the Boston University School of Public Health. His research interests include mental health\, substance use disorders\, and addiction recovery. As a formerly incarcerated scholar and a person in long-term recovery\, Dr. Vest is an advocate for social justice issues and public policy concerning substance use disorder recovery and prison reentry. He completed his PhD in Experimental Psychology from Washington State University and did his postdoctoral fellowship at Stanford University. \n 
URL:https://datascience.ucsd.edu/event/lived-experience-research-summit/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2025/04/TUS_HS_HDSI_Collaboration_V4_Flyer-scaled.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250411T140000
DTEND;TZID=America/Los_Angeles:20250411T150000
DTSTAMP:20260528T155810
CREATED:20250408T201744Z
LAST-MODIFIED:20250408T201744Z
UID:10000518-1744380000-1744383600@datascience.ucsd.edu
SUMMARY:
DESCRIPTION:Seminar Information\n\nSeminar Date\nApril 11\, 2025 – 2:00 PM\n\n\n\nLocation\nThe FUNG Auditorium – PFBH\n\n  \n\n\n\n\n\n\n\n\n\n  \n\nAbstract\n\nPersonal and population health applications built on top of large-scale mobile sensor data and computing platforms have a great potential to impact the way we diagnose diseases\, track\, and manage our health. However\, the existing sensing mechanisms often fail to accurately capture and infer syndromic signatures that are indicative of anomalies in internal physiological and behavioral processes at an earlier stage. A mobile sensing system that can harness early syndromic signals at an individual or a community level can pave the way to effective just-in-moment intervention\, early screening\, and prevention. \nIn this talk\, I will present our recent and ongoing research to demonstrate how physiological time series data harnessed from on-body wearable systems can be used for modeling opioid use/administration\, affective states including craving\, pain\, stress and euphoria\, and opioid misuse.  I will talk about different approaches (attention based approach and Large Language Model based approach) to fuse multimodal physiological biomarker data\, behavioral data\, clinical health record data\, demographic data as well as symptom data. I will highlight how integration of pharmacological knowledge such as Pharmacokinetics of a specific substance can help neural networks to better generalize and learn opioid related physiological events better than a purely data driven approach. \nChronic opioid use induces neuroplastic changes in brain circuits\, causing predictable changes in different states including heightened stress\, increased pain\, and intense cravings. The fluctuations or changes in stress\, pain and craving states carries telltale signal for opioid misuse risk. In the last part of the talk\, I will present how the heart rate variability data from a wearable wristband can be used to predict momentary pain\, stress and craving state trajectories with a personalized hierarchical deep learning model\, obviating the need for obtrusive ecological momentary assessments throughout the day. We adopt a nonlinear dynamical systems approach with different features including persistence entropy to extract subtle trends from the moment-by-moment fluctuations or changes in pain\, stress and craving states. Our analysis reveals a hidden counter-intuitive association between high entropy or lack of predictability (i.e.\, chaos) in the momentary pain\, stress and craving states with the decrease in opioid misuse risk. Leveraging Chaos Theory\, the entropy-based nonlinear dynamical features can be used to train a deep learning based approach for accurate opioid misuse risk assessment. \n\n\n\nSpeaker Bio\n\nTauhidur Rahman is an Assistant Professor in the Halıcıoğlu Data Science Institute and Computer Science and Engineering at the University of California San Diego where he directs the Mobile Sensing and Ubiquitous Computing Laboratory (MOSAIC Lab). His current research focuses on building novel ubiquitous and mobile health sensing technologies that capture observable low-level physical signals in the form of an acoustic and electromagnetic wave from our bodies and surrounding environments and map them to relevant biological and behavioral measurements. Some of his notable accomplishments include a Google Research Scholar Award 2023\, a Google Ph.D. fellowship in 2016 in mobile computing\, a finalist position in Qualcomm innovation fellowship in 2015\, Outstanding Teaching Award 2015 from Cornell University\, one best paper award in ACM Digital Health 2016\, one best paper honorable mention award in ACM Ubicomp 2015 and a distinguished paper award from ACM IMWUT in 2021. Tauhidur received his B.S. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology\, his M.S. in Electrical Engineering from the University of Texas at Dallas and PhD in Information Science from Cornell University. He has a long track-record working with large-scale multi-modal and multi-rate sensor data\, especially in the application areas of digital epidemiology\, substance use disorder\, mental health and sleep. His work has been featured in several US-based and International media outlets including Wall Street Journal\, MIT Technology Review\, NewScientist\, Public Television for Western New England\, Daily Mail (UK) and Hindustan Times (India). His laboratory has been funded by NSF\, NIH\, DARPA and industry grants. \n 
URL:https://datascience.ucsd.edu/event/34697/
LOCATION:Powell-Focht Bioengineering Hall (PFBH)\, FUNG Auditorium
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250407T130000
DTEND;TZID=America/Los_Angeles:20250407T150000
DTSTAMP:20260528T155810
CREATED:20250401T200812Z
LAST-MODIFIED:20250401T200812Z
UID:10000516-1744030800-1744038000@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Xiaofei Shi-  Continuous-time Reinforcement Learning with Forward-Backward Stochastic Differential Equations
DESCRIPTION:When Monday April 7th 1:00pm\nWhere: HDSI 1st Floor Multipurpose Room 123\nTitle: Continuous-time Reinforcement Learning with Forward-Backward Stochastic Differential Equations \nAbstract:\nIn this talk we introduce a mathematical formulation of reinforcement learning problem with a system of forward-backward stochastic differential equations (FBSDEs). With the Deep FBSDE Solver proposed by Han\, Jentzen\, and E (2018)\, deep architecture for FBSDE systems shows great success in continuous-time stochastic control problems. In our work\, we show how to further leverage the FBSDE formulation to solve traditionally intractable equilibrium problems in finance. We present a general computational framework for solving continuous-time financial market equilibria under minimal modeling assumptions while incorporating realistic financial frictions\, such as trading costs\, and supporting multiple interacting agents. Inspired by generative adversarial networks (GANs)\, our approach employs a novel generative deep reinforcement learning framework with a decoupling feedback system embedded in the adversarial training loop\, which we term as the reinforcement link. This architecture stabilizes the generator by integrating the information from the discriminator. Our theoretically guided feedback mechanism enables the decoupling of the equilibrium system\, overcoming challenges that hinder conventional numerical algorithms. \nBio: Professor Xiaofei Shi is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto. Before joining U of T\, they worked as a Term Assistant Professor at Columbia University. Professor Shi obtained their PhD in Mathematical Finance at Carnegie Mellon University\, under the supervision of Prof. Johannes Muhle-Karbe. They are mainly interested in stochastic optimization and stochastic differential equations with applications to mathematical finance and have also worked on various topics in data science\, including crowdsourcing\, dimensionality reduction\, and sparse recovery.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-xiaofei-shi-continuous-time-reinforcement-learning-with-forward-backward-stochastic-differential-equations/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250402T110000
DTEND;TZID=America/Los_Angeles:20250402T120000
DTSTAMP:20260528T155810
CREATED:20250325T211323Z
LAST-MODIFIED:20250325T211323Z
UID:10000515-1743591600-1743595200@datascience.ucsd.edu
SUMMARY:TILOS seminar speaker Michael Mahoney (UC Berkeley)  - Foundational Methods for Foundation Models for Scientific Machine Learning
DESCRIPTION:TITLE  Foundational Methods for Foundation Models for Scientific Machine Learning| \nABSTRACT  The remarkable successes of ChatGPT in natural language processing (NLP) and related developments in computer vision (CV) motivate the question of what foundation models would look like and what new advances they would enable\, when built on the rich\, diverse\, multimodal data that are available from large-scale experimental and simulational data in scientific computing (SC)\, broadly defined. Such models could provide a robust and principled foundation for scientific machine learning (SciML)\, going well beyond simply using ML tools developed for internet and social media applications to help solve future scientific problems. I will describe recent work demonstrating the potential of the “pre-train and fine-tune” paradigm\, widely-used in CV and NLP\, for SciML problems\, demonstrating a clear path towards building SciML foundation models; as well as recent work highlighting multiple “failure modes” that arise when trying to interface data-driven ML methodologies with domain-driven SC methodologies\, demonstrating clear obstacles to traversing that path successfully. I will also describe initial work on developing novel methods to address several of these challenges\, as well as their implementations at scale\, a general solution to which will be needed to build robust and reliable SciML models consisting of millions or billions or trillions of parameters. \nBIO Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as head of the Machine Learning and Analytics Group at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning\, including randomized matrix algorithms and randomized numerical linear algebra\, scientific machine learning\, scalable stochastic optimization\, geometric network analysis tools for structure extraction in large informatics graphs\, scalable implicit regularization methods\, computational methods for neural network analysis\, physics informed machine learning\, and applications in genetics\, astronomy\, medical imaging\, social network analysis\, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics\, and he has worked and taught at Yale University in the mathematics department\, at Yahoo Research\, and at Stanford University in the mathematics department. Among other things\, he was on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI)\, he was on the National Research Council’s Committee on the Analysis of Massive Data\, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science\, he ran the Park City Mathematics Institute’s 2016 PCMI Summer Session on The Mathematics of Data\, he ran the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets\, and he was the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/. \nWhen: April 2nd 11am \nLocation: HDSI MPR 123
URL:https://datascience.ucsd.edu/event/tilos-seminar-speaker-michael-mahoney-uc-berkeley-foundational-methods-for-foundation-models-for-scientific-machine-learning/
LOCATION:HDSI 123
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250331T110000
DTEND;TZID=America/Los_Angeles:20250331T120000
DTSTAMP:20260528T155810
CREATED:20250318T195914Z
LAST-MODIFIED:20250318T195914Z
UID:10000513-1743418800-1743422400@datascience.ucsd.edu
SUMMARY:Seminar - Jeremy Bernstein - Metrized Deep Learning
DESCRIPTION:Jeremy Bernstein\n\nMIT CSAIL\n \n\n\nMonday\, March 31\n11:00 AM – 12:00 PM (PST) \nCSE 1242\n\nTitle: Metrized Deep Learning\n\n\nAbstract:\nWe build neural networks in a modular and programmatic way using software libraries like PyTorch and JAX. But optimization theory has not caught up to the flexibility of this paradigm\, and practical advances in neural net optimization are largely driven by heuristics. In this talk\, I will argue that to treat deep learning rigorously\, we must build our optimization theory programmatically and in lockstep with the neural network itself. To instantiate this idea we propose the “modular norm”\, which is a norm on the weight space of general neural architectures. The modular norm is constructed by stitching together norms on individual tensor spaces as the architecture is constructed. The modular norm has several applications: automatic Lipschitz certificates for general architectures in both weights and inputs; automatic learning rate transfer across scale; and most recently we built the duality theory for the modular norm\, leading to fast optimizers like “Muon”\, which set speed records for training transformers. We are building the theory of the modular norm into a software library called Modula to ease the development and deployment of metrized deep learning algorithms—you can find out more at https://modula.systems/.\n\n\n\nBiosketch:\n\nJeremy Bernstein is a postdoc in CSAIL at MIT advised by Phillip Isola. His goal is to uncover the computational and statistical laws of natural and artificial intelligence\, and thereby design learning systems that are more efficient\, more automatic and more useful in practice. He has a PhD in Computation & Neural Systems from Caltech and Bachelor’s and Master’s degrees in Physics from the University of Cambridge. He was a recipient of the NVIDIA graduate fellowship.
URL:https://datascience.ucsd.edu/event/seminar-jeremy-bernstein-metrized-deep-learning/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250327T140000
DTEND;TZID=America/Los_Angeles:20250327T150000
DTSTAMP:20260528T155810
CREATED:20250325T192334Z
LAST-MODIFIED:20250325T192334Z
UID:10000514-1743084000-1743087600@datascience.ucsd.edu
SUMMARY:Special TILOS Seminar: Claire Boyer | Single location regression and attention-based models
DESCRIPTION:Talk Information \nSpeaker: Claire Boyer (Université Paris-Saclay) \nDate & Time: Thursday\, March 27 @ 2pm PDT \nVenue: HDSI 123 \nTitle: Single location regression and attention-based models \nAbstract: Attention-based models\, such as Transformer\, excel across various tasks but lack a comprehensive theoretical understanding\, especially regarding token-wise sparsity and internal linear representations. To address this gap\, we introduce the single-location regression task\, where only one token in a sequence determines the output\, and its position is a latent random variable\, retrievable via a linear projection of the input. To solve this task\, we propose a dedicated predictor\, which turns out to be a simplified version of a non-linear self-attention layer. We study its theoretical properties\, by showing its asymptotic Bayes optimality and analyzing its training dynamics. In particular\, despite the non-convex nature of the problem\, the predictor effectively learns the underlying structure. This work highlights the capacity of attention mechanisms to handle sparse token information and internal linear structures.
URL:https://datascience.ucsd.edu/event/special-tilos-seminar-claire-boyer-single-location-regression-and-attention-based-models/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250317T110000
DTEND;TZID=America/Los_Angeles:20250317T120000
DTSTAMP:20260528T155810
CREATED:20250313T162327Z
LAST-MODIFIED:20250313T162327Z
UID:10000512-1742209200-1742212800@datascience.ucsd.edu
SUMMARY:Seminar: Deep Learning Theory in the Age of Generative AI - Sadhika Malladi
DESCRIPTION:Monday\, March 17\n11:00 AM – 12:00 PM (PST) \nCSE 1242  \nTitle: Deep Learning Theory in the Age of Generative AI \nAbstract:\nModern deep learning has achieved remarkable results\, but the design of training methodologies largely relies on guess-and-check approaches. Thorough empirical studies of recent massive language models (LMs) is prohibitively expensive\, underscoring the need for theoretical insights\, but classical ML theory struggles to describe modern training paradigms. I present a novel approach to developing prescriptive theoretical results that can directly translate to improved training methodologies for LMs. My research has yielded actionable improvements in model training across the LM development pipeline — for example\, my theory motivates the design of MeZO\, a fine-tuning algorithm that reduces memory usage by up to 12x and halves the number of GPU-hours required. Throughout the talk\, to underscore the prescriptiveness of my theoretical insights\, I will demonstrate the success of these theory-motivated algorithms on novel empirical settings published after the theory. \nBiosketch:\n\nSadhika Malladi is a final-year PhD student in Computer Science at Princeton University advised by Sanjeev Arora. Her research advances deep learning theory to capture modern-day training settings\, yielding practical training improvements and meaningful insights into model behavior. She has co-organized multiple workshops\, including Mathematical and Empirical Understanding of Foundation Models at ICLR 2024 and Mathematics for Modern Machine Learning (M3L) at NeurIPS 2024. She was named a 2025 Siebel Scholar.
URL:https://datascience.ucsd.edu/event/seminar-deep-learning-theory-in-the-age-of-generative-ai-sadhika-malladi/
LOCATION:CSE 1242
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250221T140000
DTEND;TZID=America/Los_Angeles:20250221T153000
DTSTAMP:20260528T155810
CREATED:20250219T201049Z
LAST-MODIFIED:20250219T201049Z
UID:10000509-1740146400-1740151800@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Victor Minces - The Sound of Data
DESCRIPTION:When Friday\, February 21st\nWhere: HDSI MPR 123\n\n\n\n\n\n\n\n\nTitle: The Sound of Data\n\nSpeaker: Victor Minces\n\nAbstract: In this talk\, Dr. Minces will give an overview of his career and how it led to the development of Listening to Waves\, a program that creates playful activities and web applications that connect music with science through data visualization and sonification. He will demonstrate how to use the applications created by his team to create surprising sounds and how the applications can help people understand the science of waves\, signal processing\, and music. He will discuss the impact of his program on children’s attitudes toward science and the education system. Further\, he will demonstrate new projects for sonifying data\, such as ‘the talking hand\,’ an application transforming hand movements into phonemes.\n\nBio: Dr. Minces is a neuroscientist of music\, sound artist\, performer\, and developer of educational programs centered on the STEM of music. He studied fine arts and physics at the University of Buenos Aires and obtained his Ph.D. in Computational Neurobiology at the University of California\, San Diego\, in Andrea Chiba’s laboratory. He is now a research scientist in the Department of Cognitive Science. He has studied how large neural networks in the brain encode sensory information and how the brain processes musical rhythm. He has created Listening to Waves\, a widely adopted program that develops web applications and activities for people to learn about the science of sound through playful exploration.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-victor-minces-the-sound-of-data/
LOCATION:Halıcıoğlu Data Science Institute Room 123\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Guest Lecture,Seminar
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DTSTART;TZID=America/Los_Angeles:20250205T140000
DTEND;TZID=America/Los_Angeles:20250205T153000
DTSTAMP:20260528T155810
CREATED:20250130T190217Z
LAST-MODIFIED:20250130T190217Z
UID:10000508-1738764000-1738769400@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Hongzhe Li
DESCRIPTION:When Wednesday Feb 5th 2:00pm\nWhere: Computer Science & Engineering (CSE) 1st floor\, Seminar Room 1242 \nTitle: Fréchet Regression of Random Objects on Vector Covariates and Its applications for Single Cell RNA-seq Data Analysis \nAbstract: \nPopulation-level single-cell RNA-seq data captures gene expression profiles across thousands of cells from each individual in a sizable cohort. This data facilitates the construction of cell-type- and individual-specific gene co-expression networks by estimating covariance matrices. Investigating how these co-expression networks relate to individual-level covariates provides critical insights into the interplay between molecular processes and biological or clinical traits. This talk introduces Fréchet regression\, modeling covariance matrices as outcomes and vector covariates as predictors\, using the Wasserstein distance between covariance matrices as a metric instead of the Euclidean distance. A test statistic is proposed based on the Fréchet mean and covariate-weighted Fréchet mean\, with its asymptotic null distribution derived. Analysis of large-scale single-cell RNA-seq data reveals an association between the co-expression network of genes in the nutrient-sensing pathway and age\, highlighting perturbations in gene co-expression networks with aging. \nAdditionally\, a robust local Fréchet regression approach\, leveraging neural unbalanced optimal transport\, is briefly discussed to explore how cells are temporally organized during the differentiation of human embryonic stem cells into embryoid bodies. \nBio: Bio: Hongzhe Li (Lee) is Perelman Professor of Biostatistics\, Epidemiology and Informatics and Vice Chair of Research Integration at the Perelman School of Medicine at the University of Pennsylvania (Penn). He is also Director of Center for Statistics in Biomedical Big Data and a faculty member in the graduate groups of Genomics and Computational Biology and Computational and Applied Mathematics at Penn. Dr Li also has a secondary appointment in the Department of Statistics at the Wharton School. His research has been focused on developing powerful statistical and computational methods for analysis of large-scale genetic\, genomics and metagenomics data.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-hongzhe-li/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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