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DTSTART;TZID=America/Los_Angeles:20240827T150000
DTEND;TZID=America/Los_Angeles:20240827T160000
DTSTAMP:20260530T114910
CREATED:20240718T211250Z
LAST-MODIFIED:20240718T211250Z
UID:10000494-1724770800-1724774400@datascience.ucsd.edu
SUMMARY:2024 HDSI Virtual Industry Open House
DESCRIPTION:The Halıcıoğlu Data Science Institute (HDSI) at UC San Diego is excited to welcome all employers\, partners\, campus colleagues\, and the broader community to our 2024 Virtual Industry Open House. Join us to learn about the latest developments at HDSI\, including our innovative programs\, engagement opportunities\, and how we are preparing the next generation of data science talent in this new era of AI. \nAgenda Highlights: \n\nFaculty overview of undergraduate and graduate programs\nInsights on how and why to recruit our talented students\nOpportunities for engagement with students\, alumni\, and career services\nIndustry Partnership Alliance\nQ&A session\n\nDate and Time: Tuesday\, August 27th\, from 3:00 PM to 4:00 PM \nRegistration Link: Click here\n\nWe look forward to your participation in shaping the future of data science together.
URL:https://datascience.ucsd.edu/event/2024-hdsi-virtual-industry-open-house/
LOCATION:Virtual
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2024/07/HDSI_Virtual_Industry_OH_Flyer.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240904T130000
DTEND;TZID=America/Los_Angeles:20240904T173000
DTSTAMP:20260530T114910
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240924T160000
DTEND;TZID=America/Los_Angeles:20240924T180000
DTSTAMP:20260530T114910
CREATED:20240911T180307Z
LAST-MODIFIED:20240911T180813Z
UID:10000498-1727193600-1727200800@datascience.ucsd.edu
SUMMARY:Special lecture with Karandeep Singh\, MD
DESCRIPTION:Join us for a special celebration in honor of Karandeep Singh\, MD\, the Joan and Irwin Jacobs Chancellor’s Endowed Chair in Digital Health Innovation. In addition to this prestigious role\, Dr. Singh serves as the inaugural Chief Health AI Officer at UC San Diego Health. \nTo commemorate this occasion\, the celebration will feature a special lecture from Dr. Singh\, where he will share his vision for the future of digital health innovation and the pivotal role AI will play in transforming health care. Dr. Singh’s lecture will be followed by a reception. \nWe will also honor Irwin and the late Joan Jacobs\, whose visionary generosity made these advances possible\, fueling the future of digital health and innovation at UC San Diego. \nTuesday\, September 24\n4 – 6 p.m. \nSanford Medical Education and Telemedicine Building\n3160 Biomedical Sciences Way  |  La Jolla\, California \nRSVP \nKindly RSVP by September 19. If you have any questions regarding this event\, please email kkiyan@ucsd.edu. \n\n  \n \nKarandeep Singh\, MD \nJoan and Irwin Jacobs Chancellor’s Endowed Chair in Digital Health Innovation\nChief Health AI Officer\, UC San Diego Health\nAssociate CMIO for Inpatient Care\, UC San Diego Health \nRead Dr.Singh’s bio \n  \n\nUC San Diego is committed to hosting inclusive\, accessible events that enable all individuals\, including those with disabilities\, to engage fully. If you need further assistance\, please contact us 72 hours prior to the date of the event. \nUC San Diego respects your privacy. You may opt out of receiving fundraising information for UC San Diego Health by visiting advancementoptout.ucsd.edu or calling us toll-free at (800) 588-2734. Your treatment or payment will not be affected by your choice to opt out of a fundraising communication.
URL:https://datascience.ucsd.edu/event/special-lecture-with-karandeep-singh-md/
LOCATION:Sanford Medical Education and Telemedicine Building\, 3160 Biomedical Sciences Way\, La Jolla\, California\, United States
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/09/JacobsHealth_banner.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241008T130000
DTEND;TZID=America/Los_Angeles:20241008T140000
DTSTAMP:20260530T114910
CREATED:20241008T003616Z
LAST-MODIFIED:20241008T201835Z
UID:10000501-1728392400-1728396000@datascience.ucsd.edu
SUMMARY:The Critical Role of Cyber Infrastructure in City Innovation and Beyond
DESCRIPTION:Talk Abstract \n\nCities\, humanity’s greatest inventions\, offer vast opportunities for innovation in science and technology. The increasing availability of big data paints a promising future for our cities. Over the past decade\, my work has focused on applying AI to address real-world city challenges. Recent collaborations with city practitioners have deepened my understanding of these complexities and refined my vision for achieving city intelligence. \nIn this talk\, I will present my work on advanced AI techniques for city transportation problems\, e.g.\, reinforcement learning for traffic signal control. I will then expand on this to discuss the resource-centric concept of city intelligence\, using real-world practices to showcase its practical applications. Finally\, I will emphasize the urgent need for new cyber infrastructure\, vital not only for city innovations but for all scientific disciplines driven by big data and intensive computing. \n\nSpeaker Bio\nDr. Zhenhui (Jessie) Li currently serves as the chief scientist at the Yunqi Academy of Engineering\, a non-profit institution situated in Hangzhou\, China. Prior to this role\, she held a tenured associate professor position at Pennsylvania State University. She earned her doctoral degree in Computer Science from the University of Illinois at Urbana-Champaign. Her research has been primarily devoted to advancing computing technologies to unlock the potential of data for cross-disciplinary research\, with a specific emphasis on city applications. For further information\, you can visit her website at (https://jessielzh.com/).
URL:https://datascience.ucsd.edu/event/the-critical-role-of-cyber-infrastructure-in-city-innovation-and-beyond/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1202
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/cropped-HDSI-UCSD-Image-e1712856546428.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241015T123000
DTEND;TZID=America/Los_Angeles:20241015T140000
DTSTAMP:20260530T114910
CREATED:20241008T002133Z
LAST-MODIFIED:20241008T003755Z
UID:10000500-1728995400-1729000800@datascience.ucsd.edu
SUMMARY:MathWorks Technical Seminar: AI & Machine Learning in Real-World Systems
DESCRIPTION:Beyond their use in traditional data analytics problems\, AI and Machine Learning techniques are changing the way real-world complex systems (like vehicles\, airplanes\, and even industrial production lines) are designed\, tested and fabricated. When building these systems\, engineers rely heavily on modeling and computer-assisted simulations. This seminar showcases how MATLAB and Simulink can help integrate AI models into the component and system level simulation stages of the design process\, and how they can help deploy the resulting control algorithms into the real-world systems. We will use an example where deep-learning and machine learning can be used to create a Virtual Sensor algorithm. Join us for a complementary seminar to see how this is done! Highlights include: \n\nDesigning and training machine learning components with Statistics and Machine Learning Toolbox\nDesigning and training deep learning components with Deep Learning Toolbox\nImporting trained TensorFlow models into MATLAB\nIntegrating machine learning and deep learning models into Simulink for system-level simulation\nGenerating library-free C code and performing PIL tests\nLSTM Model Compression using projection\n\nThis is the first in a 3 part seminar series in partnership with HDSI\, JSOE\, and SIO for this academic year. \nPlease note that this is open to all faculty\, staff\, and students (all grade & majors). \nRegister Here \n*Lunch is included in registration.
URL:https://datascience.ucsd.edu/event/mathworks-technical-seminar-ai-machine-learning-in-real-world-systems/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/10/10.2024-Mathworks-UCSD-Technical-Seminar-Series.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241021T130000
DTEND;TZID=America/Los_Angeles:20241021T140000
DTSTAMP:20260530T114910
CREATED:20241029T173423Z
LAST-MODIFIED:20241029T173423Z
UID:10000502-1729515600-1729519200@datascience.ucsd.edu
SUMMARY:HDSI Seminar -  Generative Social Choice | Ariel Procaccia
DESCRIPTION:Talk Information: \nWhen Monday Oct 21st 1:00pm\nWhere: HDSI MPR 123\nZoom Info: http://bit.ly/HDSI-Seminars \nTitle: Generative Social Choice \nAbstract: “The mathematical study of voting\, social choice theory\, has traditionally only been applicable to choices among a few predetermined alternatives\, but not to open-ended decisions such as collectively selecting a textual statement. This limitation is addressed by generative social choice\, a design methodology for open-ended democratic processes that combines the rigor of social choice theory with the capability of large language models to generate text and extrapolate preferences. I’ll introduce a framework that divides the design of AI-augmented democratic processes into two components: first\, proving that the process satisfies representation guarantees when given access to oracle queries; second\, empirically validating that these queries can be approximately implemented using a large language model. I’ll also discuss the application of this framework to the problem of summarizing free-form opinions into a proportionally representative slate of opinion statements. By providing rigorous guarantees\, generative social choice could alleviate concerns about AI-driven democratic innovation and help unlock its potential. \nBio: Ariel Procaccia is Gordon McKay Professor of Computer Science at Harvard University. He works on a broad and dynamic set of problems related to AI\, algorithms\, economics\, and society. He has helped create systems and platforms that are widely used to solve everyday fair division problems\, resettle refugees and select citizens’ assemblies. To make his research accessible to the public\, he regularly writes opinion and exposition pieces for publications such as the Washington Post\, Bloomberg\, Wired and Scientific American. He is a AAAI Fellow (2024) and a recipient of the ACM SIGecom Mid-Career Award (2024)\, Social Choice and Welfare Prize (2020)\, Guggenheim Fellowship (2018)\, IJCAI Computers and Thought Award (2015) and Sloan Research Fellowship (2015).
URL:https://datascience.ucsd.edu/event/hdsi-seminar-generative-social-choice-ariel-procaccia/
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:20241024T100000
DTEND;TZID=America/Los_Angeles:20241024T110000
DTSTAMP:20260530T114910
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:20241030T130000
DTEND;TZID=America/Los_Angeles:20241030T143000
DTSTAMP:20260530T114910
CREATED:20241029T173302Z
LAST-MODIFIED:20241029T173302Z
UID:10000504-1730293200-1730298600@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Maksim Kitsak -Modeling and Inference of Complementarity Mechanisms in Networks.
DESCRIPTION:Talk Information:\nWhen Wednesday Oct 30th 1:00pm\nWhere: HDSI MPR 123\nZoom Info: http://bit.ly/HDSI-Seminars \nTitle: Modeling and Inference of Complementarity Mechanisms in Networks. \nAbstract: “In many networks\, including networks of protein-protein interactions\, interdisciplinary collaboration networks\, and semantic networks\, connections are established between nodes with complementary rather than similar properties. What is complementarity?\nThe Oxford Dictionary asserts that “”two people or things that are complementary are different but together form a useful or attractive combination of skills\, qualities or physical features.”” Sadly\, our understanding of complementarity in networks does not\ngo far beyond definition. While complementarity is abundant in networks\, we lack mathematical intuition and quantitative methods to study complementarity mechanisms in these systems. Instead\, we routinely retreat to using available off-the-shelf methods developed in the first place for similarity-driven networks. \nIn my talk\, I will discuss my group’s recent achievements in the analysis of complementarity mechanisms in networks. I will first explain why existing similarity-based inference and learning methods are not readily applicable to systems where complementarity between interacting nodes plays a significant role. I will then deduce\, starting with the definition by the Oxford Dictionary\, a general complementarity framework for networks capable of describing any matching relations and containing both similarity and antitheses relations as special cases. Using the general framework\, I will formulate a minimal null model to learn complementarity embeddings of real networks via maximum-likelihood estimation. I will demonstrate how complementarity embeddings can be used to infer both complementary and similar nodes in a network\, enabling network inference tasks\, such as link prediction and community detection. I will conclude my talk with an outlook on the interplay of similarity and complementarity in the formation of networks\, arguing for a careful re-evaluation of existing similarity-inspired methods.” \nBio: “Maksim Kitsak is an Associate Professor of the Electrical Engineering\, Mathematics\, and Computer Science faculty of the Delft University of Technology\, the Netherlands. Prof. Kitsak has been working at the intersection of Network Theory\, Machine Learning\, and Statistical Physics. Prof. Kitsak is particularly interested in the fundamental principles behind non-Euclidean network embeddings and novel applications of network embeddings in communication and biological networks. His research is often published in prestigious journals\, such as Nature and Science Families. Prof. Kitsak gratefully acknowledges the financial support of the National Science Foundation (NSF\, USA)\, Army Research Office (ARO\, USA)\, and the Dutch Research Council (NWO\, NL).”
URL:https://datascience.ucsd.edu/event/hdsi-seminar-maksim-kitsak-modeling-and-inference-of-complementarity-mechanisms-in-networks/
LOCATION:Halıcıoğlu Data Science Institute\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA Room 123
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/cropped-HDSI-UCSD-Image-e1712856546428.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241106T140000
DTEND;TZID=America/Los_Angeles:20241106T150000
DTSTAMP:20260530T114910
CREATED:20241112T204134Z
LAST-MODIFIED:20241112T204134Z
UID:10000506-1730901600-1730905200@datascience.ucsd.edu
SUMMARY:Revisiting Scalarization in Multi-Task Learning | Prof. Han Zhao
DESCRIPTION:Title: Revisiting Scalarization in Multi-Task Learning \nAbstract: Linear scalarization\, i.e.\, combining all loss functions by a weighted sum\, has been the default choice in the literature of multi-task learning (MTL) since its inception. In recent years\, there has been a surge of interest in developing Specialized Multi-Task Optimizers (SMTOs) that treat MTL as a multi-objective optimization problem. However\, it remains open whether there is a fundamental advantage of SMTOs over scalarization. In fact\, heated debates exist in the community comparing these two types of algorithms\, mostly from an empirical perspective. In this talk\, I will revisit scalarization from a theoretical perspective. I will be focusing on linear MTL models and studying whether scalarization is capable of fully exploring the Pareto front. Our findings reveal that\, in contrast to recent works that claimed empirical advantages of scalarization\, scalarization is inherently incapable of full exploration\, especially for those Pareto optimal solutions that strike the balanced trade-offs between multiple tasks. More concretely\, when the model is under-parametrized\, we reveal a multi-surface structure of the feasible region and identify necessary and sufficient conditions for full exploration. This leads to the conclusion that scalarization is in general incapable of tracing out the Pareto front. Our theoretical results provide a more intuitive explanation of why scalarization fails beyond non-convexity. I will conclude the talk by briefly discussing the extension of our results to general nonlinear neural networks.\nBio: Dr. Han Zhao is an Assistant Professor of Computer Science and\, by courtesy\, of Electric and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC). He is also an Amazon Visiting Academic at Amazon AI. Dr. Zhao earned his Ph.D. degree in machine learning from Carnegie Mellon University. His research interest is centered around trustworthy machine learning\, with a focus on algorithmic fairness\, robust generalization under distribution shifts and model interpretability. He has been named a Kavli Fellow of the National Academy of Sciences and has been selected for the AAAI New Faculty Highlights program. His research has been recognized through a Google Research Scholar Award\, an Amazon Research Award\, and a Meta Research Award.
URL:https://datascience.ucsd.edu/event/revisiting-scalarization-in-multi-task-learning-prof-han-zhao/
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:20241108T110000
DTEND;TZID=America/Los_Angeles:20241108T120000
DTSTAMP:20260530T114910
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:20241118T160000
DTEND;TZID=America/Los_Angeles:20241118T173000
DTSTAMP:20260530T114910
CREATED:20241113T193525Z
LAST-MODIFIED:20241113T193637Z
UID:10000507-1731945600-1731951000@datascience.ucsd.edu
SUMMARY:AI in the Enterprise
DESCRIPTION:Are you interested in the exciting world of AI startups and industry? Are you interested in helping strengthen bridges between industry and academia?\n\n\n\n\n\n\n\n\nUCSD HDSI and RapidFire AI are delighted to announce a virtual panel discussion event titled “AI in the Enterprise.” The goal is to bridge industry and academia at the cutting edge of practical AI by discussing the recent rapid evolution of AI\, its implications for enterprises\, how startups and tech companies can empower enterprises to succeed with AI\, and how AI-related academic curricula should evolve in this new era.\n\n\n\n\n\n\n\nThe panel will be moderated by professor Arun Kumar of CSE and HDSI\, also the CTO and cofounder of RapidFire AI. The panelists span UCSD faculty/alumni and AI industry leaders in the San Diego area:\n\nAli Arsanjani\, Google\nRohan Paul\, Illumina\nHao Zhang\, Snowflake and UCSD\n\n\nPlease RSVP by EOD Friday\, November 15 on this Google Form: https://forms.gle/o3L6Tb6ih5u12SiY7 \, The Zoom link will be sent to the registrants soon afterward.
URL:https://datascience.ucsd.edu/event/ai-in-the-enterprise/
LOCATION:Virtual
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/11/AIintheEnterprisePanelFlyer.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250205T140000
DTEND;TZID=America/Los_Angeles:20250205T153000
DTSTAMP:20260530T114910
CREATED:20250130T190217Z
LAST-MODIFIED:20250130T190217Z
UID:10000508-1738764000-1738769400@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Hongzhe Li
DESCRIPTION:When Wednesday Feb 5th 2:00pm\nWhere: Computer Science & Engineering (CSE) 1st floor\, Seminar Room 1242 \nTitle: Fréchet Regression of Random Objects on Vector Covariates and Its applications for Single Cell RNA-seq Data Analysis \nAbstract: \nPopulation-level single-cell RNA-seq data captures gene expression profiles across thousands of cells from each individual in a sizable cohort. This data facilitates the construction of cell-type- and individual-specific gene co-expression networks by estimating covariance matrices. Investigating how these co-expression networks relate to individual-level covariates provides critical insights into the interplay between molecular processes and biological or clinical traits. This talk introduces Fréchet regression\, modeling covariance matrices as outcomes and vector covariates as predictors\, using the Wasserstein distance between covariance matrices as a metric instead of the Euclidean distance. A test statistic is proposed based on the Fréchet mean and covariate-weighted Fréchet mean\, with its asymptotic null distribution derived. Analysis of large-scale single-cell RNA-seq data reveals an association between the co-expression network of genes in the nutrient-sensing pathway and age\, highlighting perturbations in gene co-expression networks with aging. \nAdditionally\, a robust local Fréchet regression approach\, leveraging neural unbalanced optimal transport\, is briefly discussed to explore how cells are temporally organized during the differentiation of human embryonic stem cells into embryoid bodies. \nBio: Bio: Hongzhe Li (Lee) is Perelman Professor of Biostatistics\, Epidemiology and Informatics and Vice Chair of Research Integration at the Perelman School of Medicine at the University of Pennsylvania (Penn). He is also Director of Center for Statistics in Biomedical Big Data and a faculty member in the graduate groups of Genomics and Computational Biology and Computational and Applied Mathematics at Penn. Dr Li also has a secondary appointment in the Department of Statistics at the Wharton School. His research has been focused on developing powerful statistical and computational methods for analysis of large-scale genetic\, genomics and metagenomics data.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-hongzhe-li/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250221T140000
DTEND;TZID=America/Los_Angeles:20250221T153000
DTSTAMP:20260530T114910
CREATED:20250219T201049Z
LAST-MODIFIED:20250219T201049Z
UID:10000509-1740146400-1740151800@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Victor Minces - The Sound of Data
DESCRIPTION:When Friday\, February 21st\nWhere: HDSI MPR 123\n\n\n\n\n\n\n\n\nTitle: The Sound of Data\n\nSpeaker: Victor Minces\n\nAbstract: In this talk\, Dr. Minces will give an overview of his career and how it led to the development of Listening to Waves\, a program that creates playful activities and web applications that connect music with science through data visualization and sonification. He will demonstrate how to use the applications created by his team to create surprising sounds and how the applications can help people understand the science of waves\, signal processing\, and music. He will discuss the impact of his program on children’s attitudes toward science and the education system. Further\, he will demonstrate new projects for sonifying data\, such as ‘the talking hand\,’ an application transforming hand movements into phonemes.\n\nBio: Dr. Minces is a neuroscientist of music\, sound artist\, performer\, and developer of educational programs centered on the STEM of music. He studied fine arts and physics at the University of Buenos Aires and obtained his Ph.D. in Computational Neurobiology at the University of California\, San Diego\, in Andrea Chiba’s laboratory. He is now a research scientist in the Department of Cognitive Science. He has studied how large neural networks in the brain encode sensory information and how the brain processes musical rhythm. He has created Listening to Waves\, a widely adopted program that develops web applications and activities for people to learn about the science of sound through playful exploration.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-victor-minces-the-sound-of-data/
LOCATION:Halıcıoğlu Data Science Institute Room 123\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Guest Lecture,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250314T110000
DTEND;TZID=America/Los_Angeles:20250314T143000
DTSTAMP:20260530T114910
CREATED:20250313T162027Z
LAST-MODIFIED:20250313T162027Z
UID:10000511-1741950000-1741962600@datascience.ucsd.edu
SUMMARY:HDSI 2025 Senior Capstone Showcase
DESCRIPTION:Poster session for the undergraduate data science major’s senior capstone program\n  \nRegistration is now open for the Halıcıoğlu Data Science Institute’s 2025 Senior Capstone Showcase! We invite you to join our senior class in an interactive presentation of the projects they have worked on for the past two quarters.\nRegister here: https://dsc-capstone.org/showcase-25/
URL:https://datascience.ucsd.edu/event/hdsi-2025-senior-capstone-showcase/
LOCATION:Price Center East Ballroom\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:HDSI Event,Showcase
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250317T110000
DTEND;TZID=America/Los_Angeles:20250317T120000
DTSTAMP:20260530T114910
CREATED:20250313T162327Z
LAST-MODIFIED:20250313T162327Z
UID:10000512-1742209200-1742212800@datascience.ucsd.edu
SUMMARY:Seminar: Deep Learning Theory in the Age of Generative AI - Sadhika Malladi
DESCRIPTION:Monday\, March 17\n11:00 AM – 12:00 PM (PST) \nCSE 1242  \nTitle: Deep Learning Theory in the Age of Generative AI \nAbstract:\nModern deep learning has achieved remarkable results\, but the design of training methodologies largely relies on guess-and-check approaches. Thorough empirical studies of recent massive language models (LMs) is prohibitively expensive\, underscoring the need for theoretical insights\, but classical ML theory struggles to describe modern training paradigms. I present a novel approach to developing prescriptive theoretical results that can directly translate to improved training methodologies for LMs. My research has yielded actionable improvements in model training across the LM development pipeline — for example\, my theory motivates the design of MeZO\, a fine-tuning algorithm that reduces memory usage by up to 12x and halves the number of GPU-hours required. Throughout the talk\, to underscore the prescriptiveness of my theoretical insights\, I will demonstrate the success of these theory-motivated algorithms on novel empirical settings published after the theory. \nBiosketch:\n\nSadhika Malladi is a final-year PhD student in Computer Science at Princeton University advised by Sanjeev Arora. Her research advances deep learning theory to capture modern-day training settings\, yielding practical training improvements and meaningful insights into model behavior. She has co-organized multiple workshops\, including Mathematical and Empirical Understanding of Foundation Models at ICLR 2024 and Mathematics for Modern Machine Learning (M3L) at NeurIPS 2024. She was named a 2025 Siebel Scholar.
URL:https://datascience.ucsd.edu/event/seminar-deep-learning-theory-in-the-age-of-generative-ai-sadhika-malladi/
LOCATION:CSE 1242
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250327T140000
DTEND;TZID=America/Los_Angeles:20250327T150000
DTSTAMP:20260530T114910
CREATED:20250325T192334Z
LAST-MODIFIED:20250325T192334Z
UID:10000514-1743084000-1743087600@datascience.ucsd.edu
SUMMARY:Special TILOS Seminar: Claire Boyer | Single location regression and attention-based models
DESCRIPTION:Talk Information \nSpeaker: Claire Boyer (Université Paris-Saclay) \nDate & Time: Thursday\, March 27 @ 2pm PDT \nVenue: HDSI 123 \nTitle: Single location regression and attention-based models \nAbstract: Attention-based models\, such as Transformer\, excel across various tasks but lack a comprehensive theoretical understanding\, especially regarding token-wise sparsity and internal linear representations. To address this gap\, we introduce the single-location regression task\, where only one token in a sequence determines the output\, and its position is a latent random variable\, retrievable via a linear projection of the input. To solve this task\, we propose a dedicated predictor\, which turns out to be a simplified version of a non-linear self-attention layer. We study its theoretical properties\, by showing its asymptotic Bayes optimality and analyzing its training dynamics. In particular\, despite the non-convex nature of the problem\, the predictor effectively learns the underlying structure. This work highlights the capacity of attention mechanisms to handle sparse token information and internal linear structures.
URL:https://datascience.ucsd.edu/event/special-tilos-seminar-claire-boyer-single-location-regression-and-attention-based-models/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250331T110000
DTEND;TZID=America/Los_Angeles:20250331T120000
DTSTAMP:20260530T114910
CREATED:20250318T195914Z
LAST-MODIFIED:20250318T195914Z
UID:10000513-1743418800-1743422400@datascience.ucsd.edu
SUMMARY:Seminar - Jeremy Bernstein - Metrized Deep Learning
DESCRIPTION:Jeremy Bernstein\n\nMIT CSAIL\n \n\n\nMonday\, March 31\n11:00 AM – 12:00 PM (PST) \nCSE 1242\n\nTitle: Metrized Deep Learning\n\n\nAbstract:\nWe build neural networks in a modular and programmatic way using software libraries like PyTorch and JAX. But optimization theory has not caught up to the flexibility of this paradigm\, and practical advances in neural net optimization are largely driven by heuristics. In this talk\, I will argue that to treat deep learning rigorously\, we must build our optimization theory programmatically and in lockstep with the neural network itself. To instantiate this idea we propose the “modular norm”\, which is a norm on the weight space of general neural architectures. The modular norm is constructed by stitching together norms on individual tensor spaces as the architecture is constructed. The modular norm has several applications: automatic Lipschitz certificates for general architectures in both weights and inputs; automatic learning rate transfer across scale; and most recently we built the duality theory for the modular norm\, leading to fast optimizers like “Muon”\, which set speed records for training transformers. We are building the theory of the modular norm into a software library called Modula to ease the development and deployment of metrized deep learning algorithms—you can find out more at https://modula.systems/.\n\n\n\nBiosketch:\n\nJeremy Bernstein is a postdoc in CSAIL at MIT advised by Phillip Isola. His goal is to uncover the computational and statistical laws of natural and artificial intelligence\, and thereby design learning systems that are more efficient\, more automatic and more useful in practice. He has a PhD in Computation & Neural Systems from Caltech and Bachelor’s and Master’s degrees in Physics from the University of Cambridge. He was a recipient of the NVIDIA graduate fellowship.
URL:https://datascience.ucsd.edu/event/seminar-jeremy-bernstein-metrized-deep-learning/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250402T110000
DTEND;TZID=America/Los_Angeles:20250402T120000
DTSTAMP:20260530T114910
CREATED:20250325T211323Z
LAST-MODIFIED:20250325T211323Z
UID:10000515-1743591600-1743595200@datascience.ucsd.edu
SUMMARY:TILOS seminar speaker Michael Mahoney (UC Berkeley)  - Foundational Methods for Foundation Models for Scientific Machine Learning
DESCRIPTION:TITLE  Foundational Methods for Foundation Models for Scientific Machine Learning| \nABSTRACT  The remarkable successes of ChatGPT in natural language processing (NLP) and related developments in computer vision (CV) motivate the question of what foundation models would look like and what new advances they would enable\, when built on the rich\, diverse\, multimodal data that are available from large-scale experimental and simulational data in scientific computing (SC)\, broadly defined. Such models could provide a robust and principled foundation for scientific machine learning (SciML)\, going well beyond simply using ML tools developed for internet and social media applications to help solve future scientific problems. I will describe recent work demonstrating the potential of the “pre-train and fine-tune” paradigm\, widely-used in CV and NLP\, for SciML problems\, demonstrating a clear path towards building SciML foundation models; as well as recent work highlighting multiple “failure modes” that arise when trying to interface data-driven ML methodologies with domain-driven SC methodologies\, demonstrating clear obstacles to traversing that path successfully. I will also describe initial work on developing novel methods to address several of these challenges\, as well as their implementations at scale\, a general solution to which will be needed to build robust and reliable SciML models consisting of millions or billions or trillions of parameters. \nBIO Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as head of the Machine Learning and Analytics Group at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning\, including randomized matrix algorithms and randomized numerical linear algebra\, scientific machine learning\, scalable stochastic optimization\, geometric network analysis tools for structure extraction in large informatics graphs\, scalable implicit regularization methods\, computational methods for neural network analysis\, physics informed machine learning\, and applications in genetics\, astronomy\, medical imaging\, social network analysis\, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics\, and he has worked and taught at Yale University in the mathematics department\, at Yahoo Research\, and at Stanford University in the mathematics department. Among other things\, he was on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI)\, he was on the National Research Council’s Committee on the Analysis of Massive Data\, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science\, he ran the Park City Mathematics Institute’s 2016 PCMI Summer Session on The Mathematics of Data\, he ran the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets\, and he was the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/. \nWhen: April 2nd 11am \nLocation: HDSI MPR 123
URL:https://datascience.ucsd.edu/event/tilos-seminar-speaker-michael-mahoney-uc-berkeley-foundational-methods-for-foundation-models-for-scientific-machine-learning/
LOCATION:HDSI 123
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250407T130000
DTEND;TZID=America/Los_Angeles:20250407T150000
DTSTAMP:20260530T114910
CREATED:20250401T200812Z
LAST-MODIFIED:20250401T200812Z
UID:10000516-1744030800-1744038000@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Xiaofei Shi-  Continuous-time Reinforcement Learning with Forward-Backward Stochastic Differential Equations
DESCRIPTION:When Monday April 7th 1:00pm\nWhere: HDSI 1st Floor Multipurpose Room 123\nTitle: Continuous-time Reinforcement Learning with Forward-Backward Stochastic Differential Equations \nAbstract:\nIn this talk we introduce a mathematical formulation of reinforcement learning problem with a system of forward-backward stochastic differential equations (FBSDEs). With the Deep FBSDE Solver proposed by Han\, Jentzen\, and E (2018)\, deep architecture for FBSDE systems shows great success in continuous-time stochastic control problems. In our work\, we show how to further leverage the FBSDE formulation to solve traditionally intractable equilibrium problems in finance. We present a general computational framework for solving continuous-time financial market equilibria under minimal modeling assumptions while incorporating realistic financial frictions\, such as trading costs\, and supporting multiple interacting agents. Inspired by generative adversarial networks (GANs)\, our approach employs a novel generative deep reinforcement learning framework with a decoupling feedback system embedded in the adversarial training loop\, which we term as the reinforcement link. This architecture stabilizes the generator by integrating the information from the discriminator. Our theoretically guided feedback mechanism enables the decoupling of the equilibrium system\, overcoming challenges that hinder conventional numerical algorithms. \nBio: Professor Xiaofei Shi is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto. Before joining U of T\, they worked as a Term Assistant Professor at Columbia University. Professor Shi obtained their PhD in Mathematical Finance at Carnegie Mellon University\, under the supervision of Prof. Johannes Muhle-Karbe. They are mainly interested in stochastic optimization and stochastic differential equations with applications to mathematical finance and have also worked on various topics in data science\, including crowdsourcing\, dimensionality reduction\, and sparse recovery.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-xiaofei-shi-continuous-time-reinforcement-learning-with-forward-backward-stochastic-differential-equations/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250411T140000
DTEND;TZID=America/Los_Angeles:20250411T150000
DTSTAMP:20260530T114910
CREATED:20250408T201744Z
LAST-MODIFIED:20250408T201744Z
UID:10000518-1744380000-1744383600@datascience.ucsd.edu
SUMMARY:
DESCRIPTION:Seminar Information\n\nSeminar Date\nApril 11\, 2025 – 2:00 PM\n\n\n\nLocation\nThe FUNG Auditorium – PFBH\n\n  \n\n\n\n\n\n\n\n\n\n  \n\nAbstract\n\nPersonal and population health applications built on top of large-scale mobile sensor data and computing platforms have a great potential to impact the way we diagnose diseases\, track\, and manage our health. However\, the existing sensing mechanisms often fail to accurately capture and infer syndromic signatures that are indicative of anomalies in internal physiological and behavioral processes at an earlier stage. A mobile sensing system that can harness early syndromic signals at an individual or a community level can pave the way to effective just-in-moment intervention\, early screening\, and prevention. \nIn this talk\, I will present our recent and ongoing research to demonstrate how physiological time series data harnessed from on-body wearable systems can be used for modeling opioid use/administration\, affective states including craving\, pain\, stress and euphoria\, and opioid misuse.  I will talk about different approaches (attention based approach and Large Language Model based approach) to fuse multimodal physiological biomarker data\, behavioral data\, clinical health record data\, demographic data as well as symptom data. I will highlight how integration of pharmacological knowledge such as Pharmacokinetics of a specific substance can help neural networks to better generalize and learn opioid related physiological events better than a purely data driven approach. \nChronic opioid use induces neuroplastic changes in brain circuits\, causing predictable changes in different states including heightened stress\, increased pain\, and intense cravings. The fluctuations or changes in stress\, pain and craving states carries telltale signal for opioid misuse risk. In the last part of the talk\, I will present how the heart rate variability data from a wearable wristband can be used to predict momentary pain\, stress and craving state trajectories with a personalized hierarchical deep learning model\, obviating the need for obtrusive ecological momentary assessments throughout the day. We adopt a nonlinear dynamical systems approach with different features including persistence entropy to extract subtle trends from the moment-by-moment fluctuations or changes in pain\, stress and craving states. Our analysis reveals a hidden counter-intuitive association between high entropy or lack of predictability (i.e.\, chaos) in the momentary pain\, stress and craving states with the decrease in opioid misuse risk. Leveraging Chaos Theory\, the entropy-based nonlinear dynamical features can be used to train a deep learning based approach for accurate opioid misuse risk assessment. \n\n\n\nSpeaker Bio\n\nTauhidur Rahman is an Assistant Professor in the Halıcıoğlu Data Science Institute and Computer Science and Engineering at the University of California San Diego where he directs the Mobile Sensing and Ubiquitous Computing Laboratory (MOSAIC Lab). His current research focuses on building novel ubiquitous and mobile health sensing technologies that capture observable low-level physical signals in the form of an acoustic and electromagnetic wave from our bodies and surrounding environments and map them to relevant biological and behavioral measurements. Some of his notable accomplishments include a Google Research Scholar Award 2023\, a Google Ph.D. fellowship in 2016 in mobile computing\, a finalist position in Qualcomm innovation fellowship in 2015\, Outstanding Teaching Award 2015 from Cornell University\, one best paper award in ACM Digital Health 2016\, one best paper honorable mention award in ACM Ubicomp 2015 and a distinguished paper award from ACM IMWUT in 2021. Tauhidur received his B.S. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology\, his M.S. in Electrical Engineering from the University of Texas at Dallas and PhD in Information Science from Cornell University. He has a long track-record working with large-scale multi-modal and multi-rate sensor data\, especially in the application areas of digital epidemiology\, substance use disorder\, mental health and sleep. His work has been featured in several US-based and International media outlets including Wall Street Journal\, MIT Technology Review\, NewScientist\, Public Television for Western New England\, Daily Mail (UK) and Hindustan Times (India). His laboratory has been funded by NSF\, NIH\, DARPA and industry grants. \n 
URL:https://datascience.ucsd.edu/event/34697/
LOCATION:Powell-Focht Bioengineering Hall (PFBH)\, FUNG Auditorium
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250423T130000
DTEND;TZID=America/Los_Angeles:20250423T143000
DTSTAMP:20260530T114910
CREATED:20250404T162241Z
LAST-MODIFIED:20250404T162241Z
UID:10000517-1745413200-1745418600@datascience.ucsd.edu
SUMMARY:LIVED EXPERIENCE RESEARCH SUMMIT
DESCRIPTION:Location: HDSI MPR \nTo watch via zoom please contact: hdsiassistant@ucsd.edu \nFormerly Incarcerated professor speaking on his lived experience in research. Researchers from\nSmarr Lab and MOSAIC lab. \nNoel Vest\, PhD\, is an Assistant Professor at the Boston University School of Public Health. His research interests include mental health\, substance use disorders\, and addiction recovery. As a formerly incarcerated scholar and a person in long-term recovery\, Dr. Vest is an advocate for social justice issues and public policy concerning substance use disorder recovery and prison reentry. He completed his PhD in Experimental Psychology from Washington State University and did his postdoctoral fellowship at Stanford University. \n 
URL:https://datascience.ucsd.edu/event/lived-experience-research-summit/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2025/04/TUS_HS_HDSI_Collaboration_V4_Flyer-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250521T150000
DTEND;TZID=America/Los_Angeles:20250521T170000
DTSTAMP:20260530T114910
CREATED:20250422T202557Z
LAST-MODIFIED:20250422T202557Z
UID:10000519-1747839600-1747846800@datascience.ucsd.edu
SUMMARY:HDSI UG Scholarship Showcase
DESCRIPTION:HDSI UG Scholarship Showcase Registration\n\nThe HDSI UG Scholarship at UC San Diego supports multidisciplinary student-led projects. Students choose their own research topics and lead the research process with guidance from a faculty or industry mentor. These opportunities allow students to deepen analytical skills\, develop data science portfolios\, and foster novel data-driven approaches to problem solving. \n\nThis showcase will highlight the projects of the 2024-2025 HDSI UG Scholarship recipients in an interactive poster presentation session\, open to HDSI and the public. The event will take place on Wednesday\, May 21 2025\, from 3:00 pm – 5:00 pm. Please RSVP to confirm your attendance below. \n\n\n  \nClick here for RSVP Link
URL:https://datascience.ucsd.edu/event/ugshowcase25/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Showcase,Special Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2025/04/HDSI-Undergrad-Scholarship-Showcase.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250528T130000
DTEND;TZID=America/Los_Angeles:20250528T143000
DTSTAMP:20260530T114910
CREATED:20250519T164145Z
LAST-MODIFIED:20250519T164145Z
UID:10000521-1748437200-1748442600@datascience.ucsd.edu
SUMMARY:Shuang Hao - Empowering and Strengthening Security in the AI Era
DESCRIPTION:When Wednesday May 28th\, 1pm\nWhere: HDSI 1st Floor Multipurpose Room 123 \n\nTitle: Empowering and Strengthening Security in the AI Era\n \nAbstract: Revolutionary advances in artificial intelligence (AI) techniques have led to promising applications and widespread deployment accessible to users. However\, AI techniques are increasingly being abused by cybercriminals\, such as creating synthetic content for scams or injecting malicious instances into services. It is imperative to cultivate systematic analysis and defenses against security threats in the era of AI.\nIn this talk\, I will describe my research on developing empirical-theoretical approaches to address AI abuses and attacks. First\, I will introduce the approaches of leveraging user intelligence to characterize and detect AI-generated face images\, enabling human-AI collaboration to strengthen security of generative AI. Second\, I will describe the analysis of attacks exploiting machine unlearning in the AI ecosystem\, and quantify model degradation and risks in unlearning scenarios. My research builds systematic approaches and principled solutions to advance AI security. \n \nBio: Shuang Hao is an Associate Professor of Computer Science at the University of Texas at Dallas. He obtained his Ph.D. from the Georgia Institute of Technology\, and he was a postdoctoral scholar at the University of California\, Santa Barbara before joining UT Dallas. His research interests are in security and its intersection with AI\, data science\, and user behavior analysis. His current research focuses on designing data-driven approaches to advance security in the AI ecosystem. He has published extensively in top-tier security conferences including S&P\, USENIX Security\, CCS\, and NDSS. He has received multiple awards and recognitions\, including an NSF CAREER Award\, an IETF Applied Networking Research Prize\, a DSN Best Paper Award\, an IMC Best Paper Award Runner-up\, two-time CSAW Best Security Paper Award Finalist\, and a Yahoo! Key Scientific Challenges Program Award. His work has been featured in media outlets such as MIT Technology Review\, Slashdot\, Fortune\, CNN\, and The Wall Street Journal. More about his research can be found at https://www.utdallas.edu/~shao/
URL:https://datascience.ucsd.edu/event/shuang-hao-empowering-and-strengthening-security-in-the-ai-era/
LOCATION:Halıcıoğlu Data Science Institute Room 123\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250602T080000
DTEND;TZID=America/Los_Angeles:20250602T190000
DTSTAMP:20260530T114910
CREATED:20250515T153502Z
LAST-MODIFIED:20250515T153502Z
UID:10000520-1748851200-1748890800@datascience.ucsd.edu
SUMMARY:TILOS Industry Day 2025
DESCRIPTION:Our 4th Annual Industry Day will be June 2\, 2025\, at the Halıcıoğlu Data Science Institute at UC San Diego\, the campus hub for data science. This year TILOS Industry Day will feature: \n\nTalks from invited industry speakers sharing their perspectives on challenges in AI + Optimization + Use Domains (chips\, robotics\, networking)\nResearch highlights from TILOS team members\nPanel discussions on AI Challenges for Academia—An Industry Perspective and Building Deep Tech Companies\nPoster session featuring the work of TILOS trainees (students and postdoctoral scholars)\n\nMore information can be found at the event site: https://tilos.ai/tilos-industry-day-2025/ \nDate & Time\nMonday\, June 2\, 2025\n8:00am – 7:00pm \nRegistration\nRegistration is complementary but required as space is limited. Register HERE by Wednesday\, May 28\, 2025. \nVenue\nHalıcıoğlu Data Science Institute Room 123\nUniversity of California\, San Diego\n3234 Matthews Lane\nLa Jolla\, CA 92093\n[ MAP ] \nParking\nGilman Parking Structure (252 Russell Ln\, La Jolla\, CA 92093; 5 minute walk to venue). \nHopkins Parking Structure (9800 Hopkins Dr\, La Jolla\, CA 92093; 10 minute walk to venue). \nParking fees are payable at pay stations or pay-by-phone. Note that many visitor spots are limited to two hours. Even though the app allows you to pay for longer periods\, you will get a ticket after that time if parked in a 2-hour space. \nSchedule (subject to change)\n\n\n\n\n\n\n\n\n8:00 – 8:45am\nRegistration & Breakfast\n\n\n8:45 – 9:00am\nOpening Remarks\nYusu Wang\, TILOS Director\nVijay Kumar\, TILOS Associate Director of Translation\n\n\n9:00 – 10:05am\nSESSION 1 Chair: David Pan\, TILOS & UT Austin\n\n\n9:00 – 9:35am\nIndustry Keynote\nMark Ren\, Director of Design Automation Research\, NVIDIA\n\n\n9:35 – 9:50am\nTILOS Faculty Talk\nTajana Rosing\, UC San Diego\n\n\n9:50 – 10:05am\nTILOS Faculty Talk\nHamed Hassani\, University of Pennsylvania\n\n\n10:05 – 10:15am\nShort Break\n\n\n10:15 – 11:20am\nSESSION 2 Chair: Yusu Wang\, TILOS & UC San Diego\n\n\n10:15 – 10:50am\nIndustry Keynote\nVijay Shirsathe\, VP of Engineering\, Qualcomm\n\n\n10:50 – 11:05am\nTILOS Faculty Talk\nAlejandro Ribeiro\, University of Pennsylvania\n\n\n11:05am – 11:20am\nTILOS Faculty Talk\nFarinaz Koushanfar\, UC San Diego\n\n\n11:20 – 11:30am\nShort Break\n\n\n11:30am – 12:15pm\nPanel Discussion: AI Challenges for Academia—An Industry Perspective\nNageen Himayat\, Senior Principal Engineer\, Intel\nSoonho Kang\, Principal Applied Scientist\, Amazon Web Services\nSubarna Tripathi\, Research Scientist\, Intel Labs\nModerator: TBA\n\n\n12:15 – 1:15pm\nLunch\n\n\n1:15 – 2:30pm\nSESSION 3 Chair: TBA\n\n\n1:15 – 2:00pm\nTILOS Research by Industry Partners\nDavid Gonzalez Aguirre\, Research Scientist\, Intel Labs\nMojan Javaheripi\, Senior Researcher\, Microsoft\nTBA\n\n\n2:00 – 2:30pm\nSpotlight Talks for Poster Session\n\n\n2:30 – 3:15pm\nTILOS Trainee Poster Session + Coffee\n\n\n3:15 – 4:05pm\nSESSION 4 Chair: Vijay Kumar\, TILOS & University of Pennsylvania\n\n\n3:15 – 3:50pm\nIndustry Keynote\nJonathan Hurst\, Co-Founder & Chief Robot Officer\, Agility Robotics\n\n\n3:50 – 4:05pm\nTILOS Faculty Talk\nCamillo J. Taylor\, University of Pennsylvania\n\n\n4:05 – 4:15pm\nShort Break\n\n\n4:15 – 5:00pm\nPanel Discussion: Building Deep Tech Companies\nJohn Black\, SVP of Strategy\, Brain Corp\nHenrik Christensen\, TILOS & UC San Diego\nKatie Vasquez\, Calibrate Ventures\nModerator: Vijay Kumar\, TILOS & University of Pennsylvania\n\n\n5:00 – 7:00pm\nDinner
URL:https://datascience.ucsd.edu/event/tilos-industry-day-2025/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Conference,HDSI Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250929T140000
DTEND;TZID=America/Los_Angeles:20250929T153000
DTSTAMP:20260530T114910
CREATED:20250918T200519Z
LAST-MODIFIED:20250918T205524Z
UID:10000523-1759154400-1759159800@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Regina Liu - Fusion Learning: Fusing Inferences from Diverse Data Sources
DESCRIPTION:HDSI will be hosting its first Seminar Series speaker of the academic year at the end of this month. Regina Liu (Rutgers University) will be giving a talk Monday Sept 29th at 2pm in the HDSI Multipurpose room\, 1st floor Room 123. \n\nSpeaker: Regina Liu\nDate & Time: Monday Sept 29th\, 2pm\nLocation: HDSI Multipurpose Room 123\, 1st floor \n\nTalk Title: Fusion Learning: Fusing Inferences from Diverse Data Sources\n\nAbstract: \nAdvanced data acquisition technology has greatly increased the accessibility of complex inferences\, based on summary statistics or sample data\, from diverse data sources. Fusion learning refers to combining complex inferences from multiple sources to yield a more effective overall. We focus on the tasks: 1) Whether/When to combine inferences? 2) How to combine inferences efficiently? 3) How to combine inferences to enhance an individual study\, thus named i-Fusion?\n\nWe present a general framework for nonparametric and efficient fusion learning. The main tool underlying this framework is the new notion of depth confidence distribution (depth-CD)\, developed by combining data depth\, bootstrap and confidence distributions. We show that a depth-CD is an omnibus form of confidence regions\, whose contours of level sets shrink toward the true parameter value\, and thus an all-encompassing inferential tool. The approach is efficient\, general and robust\, and readily applies to heterogeneous studies covering a broad range of complex settings. The approach is demonstrated with an aviation safety analysis application in tracking aircraft landing performance and a zero-event studies in clinical trials with non- estimable parameters. \n\nKey words: confidence distribution\, data depth\, fusion learning\, heterogeneous studies\n\nSpeaker Bio:\nRegina Liu is Distinguished Professor\, Rutgers University. Her research areas include data depth\, resampling\, nonparametric statistics\, confidence distribution\, and fusion learning. Aside from theoretical and methodological research\, she has long collaborated with the FAA on aviation safety research projects on process control\, text mining and risk management. \nShe is an elected fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). She is the recipient of 2021 Noether Distinguished Scholar Award (ASA)\, 2024 Elizabeth Scott Award (Committee of Presidents of Statistical Societies (COPSS))\, and the IMS 2025 Neyman Award & Lecture. She has served as Co-Editor for the Journal of the American Statistical Association and as Associate Editor for several journals. She was elected President of the Institute of Mathematical Statistics (IMS)\, 2020-2021.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-reginia-liu-fusion-learning-fusing-inferences-from-diverse-data-sources/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251007T140000
DTEND;TZID=America/Los_Angeles:20251007T150000
DTSTAMP:20260530T114910
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:20251017T100000
DTEND;TZID=America/Los_Angeles:20251017T113000
DTSTAMP:20260530T114910
CREATED:20251002T162628Z
LAST-MODIFIED:20251002T162628Z
UID:10000525-1760695200-1760700600@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Anupam Datta - What is your Agent’s GPA? Toward Trustworthy Data Agents 
DESCRIPTION:Speaker: Anupam Datta\nDate & Time: Friday Oct 17th\, 10am\nLocation: HDSI Multipurpose Room 123\, 1st floor \nTalk title: What is your Agent’s GPA?\nSubtitle: Toward Trustworthy Data Agents \nAbstract: We introduce the Agent GPA (Goal-Plan-Action) framework: an evaluationparadig m based on an agent’s operational loop of setting goals\, devising plans\, and executing actions. The framework includes five evaluation metrics: Goal Fulfillment\, Logical Consistency\, Execution Efficiency\, Plan Quality\, and Plan Adherence. Logical Consistency checks that an agent’s actions are consistent with its prior actions. Execution Efficiency checks whether the agent executes in the most efficient way to achieve its goal. Plan Quality checks whether an agent’s plans are aligned with its goals; Plan Adherence checks if an agent’s actions are aligned with its plan; and Goal Fulfillment checks that agent’s final outcomes match the stated goals. Our experimental results on two benchmark datasets – the public GAIA dataset and an internal dataset for a production-grade data agent – show that this framework (a) provides a systematic way to cover a broad range of agent failures\, including all agent errors on the GAIA benchmark dataset; (b) exhibits strong agreement between human and LLM judges\, ranging from 80% to over 95%; and (c) localizes errors with 86% agreement with human annotations to enable targeted improvement of agent performance. \nBio: Anupam Datta is a Principal Research Scientist and Snowflake AI Research Lead at Snowflake. He joined Snowflake as part of the acquisition of TruEra where he served as Co-Founder\, President\, and Chief Scientist from 2019-2024. Datta was on the faculty at Carnegie Mellon University from 2007-2022\, most recently as a tenured Professor of Electrical & Computer Engineering and Computer Science. Datta’s current research focuses on Trustworthy AI\, spanning evaluation\, explainability\, fairness\, and adversarial robustness of ML models and GenAI applications. Specific results include early work on Shapley Values & gradient-based explanations\, fairness assessments\, robustness of classical machine learning and deep learning models for natural language processing and computer vision\, and the TruLens open source project for evaluation and experiment tracking of GenAI apps. These research results have had a significant impact on products at TruEra and Snowflake. Datta has published over 100 research papers\, served as Chair of the National Academies Workshop on Assessing and Improving AI Trustworthiness\, on the Steering Committee of of the ACM Conference on Fairness\, Accountability\, and Transparency\, and the IEEE Computer Security Foundations Symposium\, and as an Editor-in-Chief of Foundations and Trends in Privacy and Security. He received the 2018 David P. Casasent Outstanding Research Award from the CMU College of Engineering\, a 2020 Young Alumni Achiever Award from IIT Kharagpur\, a 2021 Google Faculty Research Award\, and several awards for top papers at conferences. Datta obtained a B.Tech. from IIT Kharagpur\, and Ph.D. and M.S. degrees from Stanford University in Computer Science\, where he currently teaches a course on Trustworthy AI.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-anupam-datta-what-is-your-agents-gpa-toward-trustworthy-data-agents/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251110T140000
DTEND;TZID=America/Los_Angeles:20251110T143000
DTSTAMP:20260530T114910
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;VALUE=DATE:20251111
DTEND;VALUE=DATE:20251113
DTSTAMP:20260530T114910
CREATED:20250616T155433Z
LAST-MODIFIED:20250616T155433Z
UID:10000522-1762819200-1762991999@datascience.ucsd.edu
SUMMARY:Rising Stars in Data Science Workshop
DESCRIPTION:Please note this event is only open to postdocs and graduate students and will be held in Stanford CA.\nFor more information\, including how to apply\, and other registration links please visit: https://datascience.stanford.edu/events/workshop/rising-stars-data-science \n\n\n\n\n\n\n\nWorkshop Overview\nThe Rising Stars in Data Science workshop\, hosted November 11-12 by Stanford University in collaboration with the University of California\, San Diego\, and the University of Chicago\, focuses on celebrating and fast tracking the careers of exceptional data scientists at a critical inflection point in their career: the transition to postdoctoral scholar\, research scientist\, industry research position\, or tenure track position. Over the past four years\, the Rising Stars workshop has hosted over 130 Rising Stars from nearly 40 institutions. \nThis fall\, the sixth annual Rising Stars workshop will showcase the exciting\, innovative data science initiatives at Stanford University\, UC San Diego\, and UChicago. This event will provide PhD students and postdocs the opportunity to plug into these networks\, platforms\, and opportunities. The workshop also aims to broaden access to data science by providing a platform and a supportive mentoring network to navigate academic careers in data science.  All graduate students and postdocs\, including those from a wide variety of lived experiences and communities\, are encouraged to apply.  Applications are encouraged from all people of all racial\, ethnic\, geographic\, and socioeconomic backgrounds\, sexual orientations\, genders\, and persons with disabilities. \nThe two-day workshop will feature career and research panels\, networking and mentoring opportunities\, and research talks from the Rising Stars. Participants will gain insights from faculty panels on career development questions such as: how to start your academic career in data science; how to strategically sustain your career through research collaborations\, publications\, and skill development; and how to form meaningful interdisciplinary collaborations in data science with industry and government partners. Participants will also hear inspiring keynote talks from established\, cutting-edge leaders in data science. Accepted participants will be reimbursed up to $1000 for qualified travel expenses. \nSchedule Outline \nEligibility & Guidelines\nIf you have any questions about your eligibility\, please send an email to datascience@stanford.edu.  \n\nApplicants must be full-time graduate students within 1 year of obtaining a PhD\, or a current postdoctoral scholar\, fellow\, or researcher.\nWe welcome applicants from a wide variety of fields and backgrounds: any eligible PhD or postdoc who is engaging in rigorous\, data-driven inquiry is encouraged to apply.\nApplicants from all institutions\, including but not limited to Stanford University\, the University of California\, San Diego\, and the University of Chicago\, are encouraged to apply.\nApplicants may only submit one application.\nApplicants may have nominations from a maximum of 2 faculty members or advisors.\n\nApplications are now open! The deadline to apply is August 1\, 2025. Applicants will be notified of their application status no later than September 9. \nWorkshop Format \n\nRising Star research talks\nPanels (career development\, data science research)\nKeynote address\n1:1 meetings with faculty members\nNetworking within the Stanford University\, UC San Diego\, and UChicago data science ecosystems\n\nVirtual Info Session \nJoin us on July 17\, 9:00 – 10:00 am PDT for an informational session on the 2025 Rising Stars in Data Science workshop. In this session\, attendees will learn more about the program\, hear from the Universities\, and ask questions of past program participants.
URL:https://datascience.ucsd.edu/event/rising-stars-in-data-science-workshop/
LOCATION:Simonyi Conference Center\, 389 Jane Stanford Way\, Stanford\, 94305\, United States
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251112T110000
DTEND;TZID=America/Los_Angeles:20251112T120000
DTSTAMP:20260530T114910
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
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END:VCALENDAR