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X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
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DTSTART;TZID=America/Los_Angeles:20260211T110000
DTEND;TZID=America/Los_Angeles:20260211T123000
DTSTAMP:20260531T144029
CREATED:20251002T164405Z
LAST-MODIFIED:20251002T164405Z
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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:20260225T140000
DTEND;TZID=America/Los_Angeles:20260225T150000
DTSTAMP:20260531T144029
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:20260531T144029
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:20260531T144029
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:20260531T144029
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:20260531T144029
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/
CATEGORIES:Seminar
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