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DTSTART;TZID=America/Los_Angeles:20251117T140000
DTEND;TZID=America/Los_Angeles:20251117T153000
DTSTAMP:20260531T034619
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:20260531T034619
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:20260531T034619
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:20260531T034619
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:20260531T034619
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:20260531T034619
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:20260531T034619
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:20260225T080000
DTEND;TZID=America/Los_Angeles:20260227T170000
DTSTAMP:20260531T034619
CREATED:20260204T211538Z
LAST-MODIFIED:20260423T160040Z
UID:10000539-1772006400-1772211600@datascience.ucsd.edu
SUMMARY:EnCORE Workshop
DESCRIPTION:The goal of this EnCORE workshop is to bring together researchers working on different aspects of interpretability in modern AI systems to enable Trustworthy AI. We aim to discuss recent advancements\, theoretical foundations\, and emerging directions across topics such as automated interpretability methods\, representation and concept-level analysis\, next-generation interpretable model architectures\, trustworthiness and limitations of explanations\, and new tools for understanding both deep vision models and large language models. \nMore broadly\, the workshop seeks to consider how interpretability supports reliability\, safety\, and effective human oversight in AI. It will also highlight the growing significance of interpretability for the theoretical computer science and data science communities\, where questions related to model structure\, guarantees\, abstraction\, and reasoning have become increasingly central. By bringing these perspectives together\, the workshop aims to foster deeper dialogue and shape future research directions in the study of transparent and understandable AI systems. \n\n\nDate: Feb 25 – 27\, 2026\n\nLocation: Atkinson Hall\, 4th Floor/EnCORE Space\, UC San Diego\nRegistration Deadline: February 13\, 2026\nWorkshop website: https://trustworthy-ai-workshop.github.io/encore-2026/\n\n\nRegistration Link: https://docs.google.com/forms/d/1uKMvmpYmAW4FMz0sI50PSth7G8NIC96ynu-aXIRQKsI\n\n\n\nPlease note: We have only limited seats available for in person attendance. The seats will be first come first served. Otherwise all registered participants can attend the workshop talks online. Registration is free but required. \nIndustry participation is limited and subject to organizer approval. Interested industry partners may fill out the registration form and contact the organizers:    Lily Weng (lweng@ucsd.edu)\, Sanjoy Dasgupta (sadasgupta@ucsd.edu)
URL:https://datascience.ucsd.edu/event/encore-workshop-on-interpretability-in-modern-ai/
LOCATION:Atkinson Hall\, Fourth Floor
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260225T140000
DTEND;TZID=America/Los_Angeles:20260225T150000
DTSTAMP:20260531T034619
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:20260531T034619
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:20260531T034620
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:20260531T034620
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:20260320T155900
DTEND;TZID=America/Los_Angeles:20260320T160000
DTSTAMP:20260531T034620
CREATED:20260309T220707Z
LAST-MODIFIED:20260309T220707Z
UID:10000551-1774022340-1774022400@datascience.ucsd.edu
SUMMARY:CVPR Call for Papers Deadline
DESCRIPTION:We are excited to announce the CVPR 2026 Workshop on Trustworthy\, Robust\, Uncertainty-Aware\, and Explainable Visual Intelligence and Beyond (TRUE-V)\, which will be held in Denver\, Colorado\, USA on June 3 or 4\, 2026. \nModern vision and vision-language systems are increasingly deployed in safety-critical and real-world settings\, yet they remain opaque\, brittle\, and difficult to calibrate. TRUE-V aims to advance principled foundations and practical methodologies for trustworthy visual intelligence\, spanning interpretability\, robustness\, uncertainty\, alignment\, and responsible deployment. \nWe welcome theoretical\, methodological\, and applied contributions that push forward the science and practice of trustworthy AI in vision and beyond. The topics of interest includes aspects that can help to advance Trustworthy Visual Intelligence\, including (but not limited to): \n• Interpretable and explainable computer vision\n• Robustness and reliability under distribution shift\n• Uncertainty estimation and trust calibration\n• Concept bottleneck & modular architectures\n• Alignment\, safety\, and ethical considerations in vision models\n• Human-in-the-loop evaluation and Evaluation benchmarks\n• Trustworthy deployment in high-stakes domains\n• Vision-language and multimodal reasoning under uncertainty \nSubmission are now open through OpenReview at: \nhttps://openreview.net/group?id=thecvf.com/CVPR/2026/Workshop/TRUE-V. \nWe welcome and encourage the authors to consult the submission guidelines and important dates listed on the workshop website: https://trustworthy-ai-workshop.github.io/cvpr2026-TRUE-V/ \nThe paper submission deadline is Mar 20\, 2026 at 23:59 AoE. Accepted papers will be presented as posters\, with a subset selected for spotlight talks. Please feel free to reach out to the organizers directly if you have any questions. \nWe welcome your submissions and look forward to your contributions! Please feel free to forward this CFP to your networks. \nThis workshop is organized by: \n– Lily Weng (UC San Diego\, lweng@ucsd.edu)\,\n– Nghia Hoang (Washington State University\, trongnghia.hoang@wsu.edu)\,\n– Tammy Riklin Raviv (Ben Gurion University\, rrtammy@bgu.ac.il)\n– Giuseppe Raffa (Intel Labs\, giuseppe.raffa@intel.com)\n– Arno Blaas (Apple\, ablaas@apple.com)\n– Eunji Kim (Amazon\, kce407@snu.ac.kr)\n– Bhavya Kailkhura (LLNL\, kailkhura1@llnl.gov)\n– Kowshik Thopalli (LLNL\, thopalli1@llnl.gov)
URL:https://datascience.ucsd.edu/event/cvpr-call-for-papers-deadline/
LOCATION:CA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260330T140000
DTEND;TZID=America/Los_Angeles:20260330T150000
DTSTAMP:20260531T034620
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260426T080000
DTEND;TZID=America/Los_Angeles:20260427T170000
DTSTAMP:20260531T034620
CREATED:20260121T214728Z
LAST-MODIFIED:20260423T155957Z
UID:10000538-1777190400-1777309200@datascience.ucsd.edu
SUMMARY:ICLR 2026 Workshop
DESCRIPTION:Principled Design for Trustworthy AI – Interpretability\, Robustness\, and Safety across Modalities\nICLR 2026 Workshop\n? International Conference on Learning Representations (ICLR 2026)\n? Date: Sunday April 26 or Monday April 27 · ? Location: Rio de Janeiro\, Brazil \n\nOverview\nModern AI systems\, particularly large language models\, vision-language models\, and deep vision networks\, are increasingly deployed in high-stakes settings such as healthcare\, autonomous driving\, and legal decisions. Yet\, their lack of transparency\, fragility to distributional shifts between train/test environments\, and representation misalignment in emerging tasks and data/feature modalities raise serious concerns about their trustworthiness. \nThis workshop focuses on developing trustworthy AI systems by principled design: models that are interpretable\, robust\, and aligned across the full lifecycle – from training and evaluation to inference-time behavior and deployment. We aim to unify efforts across modalities (language\, vision\, audio\, and time series) and across technical areas of trustworthiness spanning interpretability\, robustness\, uncertainty\, and safety. \n\nCall for Papers\nWe invite submissions on topics including (but not limited to): \n\nInterpretable and Intervenable Models\n\nconcept bottlenecks and modular architectures\, mechanistic interpretability and concept-based reasoning\, interpretability for control and real-time intervention;\n\n\nInference-Time Safety and Monitoring\n\nreasoning trace auditing in LLMs and VLMs\, inference-time safeguards and safety mechanisms\, chain-of-thought consistency and hallucination detection\, real-time monitoring and failure intervention mechanisms;\n\n\nMultimodal Trust Challenges\n\ngrounding failures and cross-modal misalignment\, safety in vision-language and deep vision systems\, cross-modal alignment and robust multimodal reasoning\, trust and uncertainty in video\, audio\, and time-series models\n\n\nRobustness and Threat Models\n\nadversarial attacks and defenses\, robustness to distributional\, conceptual\, and cascading shifts\, formal verification methods and safety guarantees\, robustness under streaming\, online\, or low-resource conditions;\n\n\nTrust Evaluation and Responsible Deployment\n\nhuman-AI trust calibration\, confidence estimation\, uncertainty quantification\, metrics for interpretability/alignment/robustness\, transparent and accountable deployment pipelines\, safety alignment;\n\n\nSafety and Trustworthiness in LLM Agents\n\nsafety and failures in planning and action execution\, emergent behaviors in multi-agent interactions\, intervention and control in agent loops\, alignment of long-horizon goals with user intent\, auditing and debugging LLM agents in real-world deployment.\n\n\n\nReviews are double-blind and the accepted papers are non-archival. Accepted papers will be presented as posters and/or short talks. \nSubmission Instruction\n\nFormat: (1) Short paper track: max 4 pages\, excluding references; (2) Long paper track: max 9 pages\, excluding references. Please use the LaTeX style files (ICLR conference style) provided here.\nSubmission: Openreview link\nSubmission deadline: Feb 2\, 2026 (AoE)\nGuideline: The content of submission needs to be original and not accepted in other archival venues by the time of our submission deadline. Violation of this policy will be desk-rejected.\n\nNote that for Openreivew submission\, new profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically. \n\nImportant Dates\n\n\n\nEvent\nDate\n\n\n\n\nSubmission deadline\nFeb 2\, 2026\n\n\nNotification to authors\nFeb 28\, 2026\n\n\nCamera-ready deadline\nMar 6\, 2026\n\n\nWorkshop date\nApril 26 or 27\, 2026\n\n\n\n(All deadlines are AoE.) \n  \nContact\n? Lily Weng (lweng@ucsd.edu)\, Nghia Hoang (trongnghia.hoang@wsu.edu) \n  \nWebsite\nFor more information please visit the workshop webpage
URL:https://datascience.ucsd.edu/event/iclr-2026-workshop-principled-design-for-trustworthy-ai-interpretability-robustness-and-safety-across-modalities/
LOCATION:CA
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260603
DTEND;VALUE=DATE:20260605
DTSTAMP:20260531T034620
CREATED:20260309T221545Z
LAST-MODIFIED:20260423T155836Z
UID:10000552-1780444800-1780617599@datascience.ucsd.edu
SUMMARY:CVPR Workshop
DESCRIPTION:We are excited to announce the CVPR 2026 Workshop on Trustworthy\, Robust\, Uncertainty-Aware\, and Explainable Visual Intelligence and Beyond (TRUE-V)\, which will be held in Denver\, Colorado\, USA on June 3 or 4\, 2026. \nModern vision and vision-language systems are increasingly deployed in safety-critical and real-world settings\, yet they remain opaque\, brittle\, and difficult to calibrate. TRUE-V aims to advance principled foundations and practical methodologies for trustworthy visual intelligence\, spanning interpretability\, robustness\, uncertainty\, alignment\, and responsible deployment. \nWe welcome theoretical\, methodological\, and applied contributions that push forward the science and practice of trustworthy AI in vision and beyond. The topics of interest includes aspects that can help to advance Trustworthy Visual Intelligence\, including (but not limited to): \n• Interpretable and explainable computer vision\n• Robustness and reliability under distribution shift\n• Uncertainty estimation and trust calibration\n• Concept bottleneck & modular architectures\n• Alignment\, safety\, and ethical considerations in vision models\n• Human-in-the-loop evaluation and Evaluation benchmarks\n• Trustworthy deployment in high-stakes domains\n• Vision-language and multimodal reasoning under uncertainty \nSubmission are now open through OpenReview at: \nhttps://openreview.net/group?id=thecvf.com/CVPR/2026/Workshop/TRUE-V. \nWe welcome and encourage the authors to consult the submission guidelines and important dates listed on the workshop website: https://trustworthy-ai-workshop.github.io/cvpr2026-TRUE-V/ \nThe paper submission deadline is Mar 20\, 2026 at 23:59 AoE. Accepted papers will be presented as posters\, with a subset selected for spotlight talks. Please feel free to reach out to the organizers directly if you have any questions. \nWe welcome your submissions and look forward to your contributions! Please feel free to forward this CFP to your networks. \nThis workshop is organized by: \n– Lily Weng (UC San Diego\, lweng@ucsd.edu)\,\n– Nghia Hoang (Washington State University\, trongnghia.hoang@wsu.edu)\,\n– Tammy Riklin Raviv (Ben Gurion University\, rrtammy@bgu.ac.il)\n– Giuseppe Raffa (Intel Labs\, giuseppe.raffa@intel.com)\n– Arno Blaas (Apple\, ablaas@apple.com)\n– Eunji Kim (Amazon\, kce407@snu.ac.kr)\n– Bhavya Kailkhura (LLNL\, kailkhura1@llnl.gov)\n– Kowshik Thopalli (LLNL\, thopalli1@llnl.gov)
URL:https://datascience.ucsd.edu/event/cvpr-workshop-trustworthy-robust-uncertainty-aware-and-explainable-visual-intelligence-and-beyond/
LOCATION:CA
END:VEVENT
END:VCALENDAR