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X-WR-CALNAME:Halıcıoğlu Data Science Institute - UC San Diego
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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:20260225T080000
DTEND;TZID=America/Los_Angeles:20260227T170000
DTSTAMP:20260423T160040Z
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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:20241108T110000
DTEND;TZID=America/Los_Angeles:20241108T120000
DTSTAMP:20241104T224916Z
CREATED:20241104T224916Z
LAST-MODIFIED:20241104T224916Z
UID:10000505-1731063600-1731067200@datascience.ucsd.edu
SUMMARY:EnCORE Public Lecture -  Jon Kleinberg
DESCRIPTION:ucsd_encore-zoom is inviting you to a scheduled Zoom meeting.\nJoin Zoom Meeting\nhttps://ucsd.zoom.us/j/98016992761\nMeeting ID: 980 1699 2761\nOne tap mobile\n+16699006833\,\,98016992761# US (San Jose)\n+12133388477\,\,98016992761# US (Los Angeles)\nDial by your location\n+1 669 900 6833 US (San Jose)\n+1 213 338 8477 US (Los Angeles)\n+1 669 219 2599 US (San Jose)\nMeeting ID: 980 1699 2761\nFind your local number: https://ucsd.zoom.us/u/ab2INvbFzw
URL:https://datascience.ucsd.edu/event/encore-public-lecture-jon-kleinberg/
LOCATION:https://ucsd.zoom.us/j/98016992761
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241024T100000
DTEND;TZID=America/Los_Angeles:20241024T110000
DTSTAMP:20241023T182044Z
CREATED:20241023T181207Z
LAST-MODIFIED:20241023T182044Z
UID:10000503-1729764000-1729767600@datascience.ucsd.edu
SUMMARY:Deep Learning: a Non-parametric Statistical Viewpoint
DESCRIPTION:ABSTRACT \nThe advent of deep learning has completely revolutionized how we perceive data to obtain superhuman performance across all fields of modern science. However\, despite the remarkable empirical successes of deep learners\, the theoretical guarantees for their statistical accuracy remain rather pessimistic. In particular\, the data distributions on which deep learners are generally applied\, such as natural images\, are often hypothesized to have an intrinsic low-dimensional structure in a typically high-dimensional feature space. However\, this is often not reflected in the derived rates in the state-of-the-art analyses. This talk aims to bridge the gap between the theory and practice of deep learning from a statistical perspective. We demonstrate that deep learners exhibit a convergence rate determined solely by the intrinsic dimensionality of the data\, rather than its nominal high-dimensional feature representation. Our work not only provides practical guidelines for selecting suitable network architectures but also connects the theoretical analyses of these models to established convergence rates in optimal transport and non-parametric statistics literature. In particular\, we derive the sharpest convergence rates for various learning scenarios\, including Generative Adversarial Networks (GANs)\, Wasserstein Autoencoders (WAEs)\, federated learning\, Bi-directional GANs\, and general deep supervised learners. Furthermore\, we introduce a novel measure\, called the entropic dimension\, to characterize the intrinsic dimension of probability measures and achieve the sharpest known approximation results for neural networks employing Rectified Linear Unit (ReLU) activation\, improving upon classical benchmarks. \nBIOGRAPHY \nSaptarshi Chakraborty is a fifth-year Ph.D. student in Statistics at the University of California\, Berkeley\, advised by Prof. Peter Bartlett. Prior to joining Berkeley\, he earned his M.Stat and B. Stat (Hons.) degrees in Statistics from the Indian Statistical Institute (ISI)\, Kolkata\, India. He is primarily interested in the theoretical and methodological foundations of machine learning\, especially\, deep learning theory\, unsupervised learning\, dimensionality reduction\, optimal transport\, and optimization. \nZOOM LINK: https://ucsd.zoom.us/j/93363424503
URL:https://datascience.ucsd.edu/event/deep-learning-a-non-parametric-statistical-viewpoint/
LOCATION:Atkinson Hall\, Fourth Floor
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2024/10/Saptarshi-Chakraborty-EnCORE-Flyer.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240904T130000
DTEND;TZID=America/Los_Angeles:20240904T173000
DTSTAMP:20240821T161832Z
CREATED:20240821T160959Z
LAST-MODIFIED:20240821T161832Z
UID:10000496-1725454800-1725471000@datascience.ucsd.edu
SUMMARY:Encore Industry Day
DESCRIPTION:The UC San Diego EnCORE is excited to host Industry Day on Wednesday\, September 4th\, 2024\, at the UCSD campus. This all-day event will showcase exciting research in foundations of data science\, ML systems and AI\, with contributions from leading experts in industry and academia. Attendees will have the opportunity to network with entrepreneurs\, industry researchers\, faculty and peers from leading institutions. The event introduces a dynamic exploration of cutting-edge advancements and collaborative opportunities centered around machine learning and AI. Register Now!  \n \nTime: Noon – 1:00 pm (PST)\, and 4:30 pm- 5:30 pm PST\n\nLocation: Atkinson Hall\, 4th Floor/EnCORE Space\, UC San Diego \n  \n\n\nWho should apply? CSE PhD Students and Postdocs.  We will consider Masters students and undergraduate students as well if accompanied by an advisor letter on the quality of the work. The space is limited. Apply soon to be considered. – Register Now!  \n  \n\nPoster Requirements\n\n\n\nA pdf of your poster must be submitted by August 29th at 9 AM (PST) to Zaray Aguilar\, EnCORE Executive Assistant\, zaaguilar@ucsd.edu or uploaded to this form\nIf you do not submit your poster file in time\, you MUST PRINT & BRING your poster to the event.\nPosters must be 36″ x 24″ vertical orientation\nEnCORE Students must download and use this poster template\n\n  \n\n\nLightning Talk Requirements\n\n\n\nA .pdf of your 1 slide must be submitted by August 29th at 9 AM (PST) to Zaray Aguilar\, EnCORE Executive Assistant\, zaaguilar@ucsd.edu or uploaded to this form. \nWe will not accept any other file types or more than 1 slide.\nYou will have 1 minute to present what your poster is about. If you go over 1 minute\, you will be asked to stop.\n\nEnCORE Students must download and use this slide template\n\n  \n\n\n\n\nWe will welcome posters on the topics on:\n\n\n\n\n\nTheoretical Computer Science\nAI foundations and applications \nResponsible Machine Learning\nInformation Theory\nOptimization\nComputational Games\nFoundations of Neuroscience\n\n\n \n\nThere will be several PRIZES for best posters! \nGeneral Registration ( For all ) \n\n\n\nPost Doc & Grad Registration ( For Post Doc & Grad students only )\n\n 
URL:https://datascience.ucsd.edu/event/encore-industry-day/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Industry
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/08/Encore_Industry_Day_Flyer.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240205T140000
DTEND;TZID=America/Los_Angeles:20240205T150000
DTSTAMP:20240205T170333Z
CREATED:20240205T170247Z
LAST-MODIFIED:20240205T170333Z
UID:10000435-1707141600-1707145200@datascience.ucsd.edu
SUMMARY:Scaling Data-Constrained Language Model
DESCRIPTION:Extrapolating scaling trends suggest that training dataset size for LLMs may soon be limited by the amount of text data available on the internet. In this talk we investigate scaling language models in data-constrained regimes. Specifically\, we run a set of empirical experiments varying the extent of data repetition and compute budget. From these experiments we propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally\, we discuss and experiment with approaches for mitigating data scarcity.\n \nBio: Alexander “Sasha” Rush is an Associate Professor at Cornell Tech and a researcher at Hugging Face. His research interest is in the study of language models with applications in controllable text generation\, efficient inference\, and applications in summarization and information extraction. In addition to research\, he has written several popular open-source software projects supporting NLP research\, programming for deep learning\, and virtual academic conferences. His projects have received paper and demo awards at major NLP\, visualization\, and hardware conferences\, an NSF Career Award and Sloan Fellowship. He tweets at @srush_nlp.\n\n\n \n \n 
URL:https://datascience.ucsd.edu/event/scaling-data-constrained-language-model/
LOCATION:Virtual
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/Encore-logo_HDSI-Website.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240201
DTEND;VALUE=DATE:20240203
DTSTAMP:20240201T173318Z
CREATED:20240201T173318Z
LAST-MODIFIED:20240201T173318Z
UID:10000433-1706745600-1706918399@datascience.ucsd.edu
SUMMARY:ENCORE: NSF TRIPODS Workshop
DESCRIPTION:
URL:https://encore.ucsd.edu/nsf-tripods-workshop/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Workshops
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/Encore-logo_HDSI-Website.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231030T130000
DTEND;TZID=America/Los_Angeles:20231030T140000
DTSTAMP:20231030T172523Z
CREATED:20231002T164053Z
LAST-MODIFIED:20231030T172523Z
UID:10000401-1698670800-1698674400@datascience.ucsd.edu
SUMMARY:The Uneasy Relation Between Deep Learning and Statistics
DESCRIPTION:Abstract: Deep learning uses the language and tools of statistics and classical machine learning\, including empirical and population losses and optimizing a hypothesis on a training set. But it uses these tools in regimes where they should not be applicable: the optimization task is non-convex\, models are often large enough to overfit\, and the training and deployment tasks can radically differ. In this talk I will survey the relation between deep learning and statistics. In particular we will discuss recent works supporting the emerging intuition that deep learning is closer in some aspects to human learning than to classical statistics. Rather than estimating quantities from samples\, deep neural nets develop broadly applicable representations and skills through their training.\n\nThe talk will not assume background knowledge in artificial intelligence or deep learning.\n\n\n \nBio: Boaz Barak is the Gordon McKay professor of Computer Science at Harvard University’s John A. Paulson School of Engineering and Applied Sciences. Barak’s research interests include all areas of theoretical computer science and in particular cryptography\, computational complexity\, and the foundations of machine learning. Previously\, he was a principal researcher at Microsoft Research New England\, and before that an associate professor (with tenure) at Princeton University’s computer science department. Barak has won the ACM dissertation award\, the Packard and Sloan fellowships\, and was also selected for Foreign Policy magazine’s list of 100 leading global thinkers for 2014. He was also chosen as a Simons investigator and a Fellow of the ACM. Barak is a member of the scientific advisory boards for Quanta Magazine and the Simons Institute for the Theory of Computing. He is also a board member of AddisCoder\, a non-profit organization for teaching algorithms and coding to high-school students in Ethiopia and Jamaica. Barak wrote with Sanjeev Arora the textbook “Computational Complexity: A Modern Approach”.
URL:https://datascience.ucsd.edu/event/the-uneasy-relation-between-deep-learning-and-statistics/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/Encore-logo_HDSI-Website.png
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