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DTSTART;TZID=America/Los_Angeles:20250929T140000
DTEND;TZID=America/Los_Angeles:20250929T153000
DTSTAMP:20260531T165446
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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:20251007T140000
DTEND;TZID=America/Los_Angeles:20251007T150000
DTSTAMP:20260531T165446
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:20251110T140000
DTEND;TZID=America/Los_Angeles:20251110T143000
DTSTAMP:20260531T165446
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:20251112T110000
DTEND;TZID=America/Los_Angeles:20251112T120000
DTSTAMP:20260531T165446
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:20251117T140000
DTEND;TZID=America/Los_Angeles:20251117T153000
DTSTAMP:20260531T165446
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:20251203T110000
DTEND;TZID=America/Los_Angeles:20251203T123000
DTSTAMP:20260531T165446
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/
LOCATION:CA
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251208T140000
DTEND;TZID=America/Los_Angeles:20251208T153000
DTSTAMP:20260531T165446
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:20260113T140000
DTEND;TZID=America/Los_Angeles:20260113T150000
DTSTAMP:20260531T165446
CREATED:20260107T193320Z
LAST-MODIFIED:20260107T225249Z
UID:10000535-1768312800-1768316400@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series - Ann Kennedy - Latent state inference from neural dynamics  and behavior
DESCRIPTION:Talk Time: Mon Jan 12th\, 2026 | 2:00pm \nLocation: Data Science Building 1st Floor\, Room 123 \nTalk Abstract\nAs we interact with the world around us\, we experience a constant stream of sensory inputs\, and must generate a constant stream of behavioral actions. What makes brains more than simple input-output machines is their capacity to integrate sensory inputs with our internal motivations and drives to produce behavior that is flexible and adaptive. How can we uncover evidence of this integration from observed neural activity? In this talk\, I will present our lab’s recent work inferring the structure and dynamics of latent motivational states from the observed actions and neural activity of freely behaving mice. We draw on classical normative models of animal behavior from ethology\, showing how these models can help us analyze and interpret experimental data to understand how the circuit architecture of the brain gives rise to the algorithms of survival. \nSpeaker Biography\nAnn Kennedy is a theoretical neuroscientist interested in understanding how the structure of the nervous system gives rise to its function. Originally from Virginia\, they studied Biomedical Engineering at Johns Hopkins and Neuroscience at Columbia University\, where they earned my PhD in the lab of Larry Abbott in the Center for Theoretical Neuroscience. Dr Kennedy pursued postdoctoral training in the lab of David Anderson at Caltech\, and opened their lab at Northwestern University in 2020\, moving to Scripps Research in 2024.\nThe Kennedy Lab studies the underlying neuroscience and brain structures that give rise to fundamental behaviors related to fear\, survival and social interactions. By better understanding the neural activities that guide our decision-making and behavior\, Dr. Kennedy’s work aims to reveal insights about the guiding principles of behavior\, as well as what happens in cases of dysfunctions—for example\, social dysfunction or excessive fear and anxiety.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-ann-kennedy/
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:20260531T165446
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:20260209T140000
DTEND;TZID=America/Los_Angeles:20260209T150000
DTSTAMP:20260531T165446
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:20260211T110000
DTEND;TZID=America/Los_Angeles:20260211T123000
DTSTAMP:20260531T165446
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/
LOCATION:CA
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260225T140000
DTEND;TZID=America/Los_Angeles:20260225T150000
DTSTAMP:20260531T165446
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:20260302T140000
DTEND;TZID=America/Los_Angeles:20260302T150000
DTSTAMP:20260531T165446
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:20260303T140000
DTEND;TZID=America/Los_Angeles:20260303T150000
DTSTAMP:20260531T165446
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:20260304T140000
DTEND;TZID=America/Los_Angeles:20260304T150000
DTSTAMP:20260531T165446
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:20260330T140000
DTEND;TZID=America/Los_Angeles:20260330T150000
DTSTAMP:20260531T165446
CREATED:20260206T212001Z
LAST-MODIFIED:20260312T171805Z
UID:10000543-1774879200-1774882800@datascience.ucsd.edu
SUMMARY:Pietro Perona Distinguished Lecture
DESCRIPTION:Talk Information\nSpeaker: Pietro Perona\nDate & Time: Monday March 30th\, 2pm\nLocation: HDSI Multipurpose Room 123\n\n\nTITLE Responsible AI – A case for causal reasoning\n\nABSTRACT “As Artificial Intelligence  (AI) finds increasing applications in industry and society\, responsible deployment demands that we measure and correct algorithmic biases vis-a-vis protected attributes such as sex\, age and ethnicity. State of the art methods for measuring algorithmic bias rely on test sets that are collected in the wild and are then annotated for the protected attributes. Such methods are therefore observational and yield correlational observations. I will argue that\, in order to obtain useful information to discover and correct biases we need causal information which is only available if we use experimental methods. I will show that modern generative models offer a promising starting point to develop experimental testing methods. I will review our recent work in face synthesis and demonstrate its application to the study of algorithmic bias in gender classification\, face recognition\, and social judgment of faces.”\n\nBIO Pietro Perona is the Allan E. Puckett Professor of Electrical Engineering at the California Institute of Technology. He is known for his research in computer vision and is the director of the Caltech Computational Vision Group. Professor Perona’s research focuses on vision: how do we see and how can we build machines that see. Professor Perona is currently interested in visual recognition\, more specifically visual categorization. He is studying how machines can learn to recognize frogs\, cars\, faces and trees with minimal human supervision\, and how machines can learn from human experts. His project `Visipedia’ has produced two smart device apps (iNaturalist and Merlin Bird ID) that anyone can use to recognize the species of plants and animals from a photograph. In collaboration with Professors Anderson and Dickinson\, professor Perona is building vision systems and statistical techniques for measuring actions and activities in fruit flies and mice. This enables geneticists and neuroethologists to investigate the relationship between genes\, brains and behavior. Professor Perona is also interested in studying how humans perform visual tasks\, such as searching and recognizing image content. One of his recent projects studies how to harness the visual ability of thousands of people on the web.
URL:https://datascience.ucsd.edu/event/pietro-perona-distinguished-lecture/
LOCATION:CA
CATEGORIES:Seminar
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END:VCALENDAR