BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Halıcıoğlu Data Science Institute - UC San Diego - ECPv6.16.2//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Halıcıoğlu Data Science Institute - UC San Diego X-ORIGINAL-URL:https://datascience.ucsd.edu X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20230312T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20231105T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20240310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20241103T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20250309T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20251102T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20260308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20261101T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20250317T110000 DTEND;TZID=America/Los_Angeles:20250317T120000 DTSTAMP:20260530T140143 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:20250314T110000 DTEND;TZID=America/Los_Angeles:20250314T143000 DTSTAMP:20260530T140143 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:20250221T140000 DTEND;TZID=America/Los_Angeles:20250221T153000 DTSTAMP:20260530T140143 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:20250205T140000 DTEND;TZID=America/Los_Angeles:20250205T153000 DTSTAMP:20260530T140143 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:20241118T160000 DTEND;TZID=America/Los_Angeles:20241118T173000 DTSTAMP:20260530T140143 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:20241108T110000 DTEND;TZID=America/Los_Angeles:20241108T120000 DTSTAMP:20260530T140143 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:20241106T140000 DTEND;TZID=America/Los_Angeles:20241106T150000 DTSTAMP:20260530T140143 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:20241030T130000 DTEND;TZID=America/Los_Angeles:20241030T143000 DTSTAMP:20260530T140143 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:20241024T100000 DTEND;TZID=America/Los_Angeles:20241024T110000 DTSTAMP:20260530T140143 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:20241021T130000 DTEND;TZID=America/Los_Angeles:20241021T140000 DTSTAMP:20260530T140143 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:20241015T123000 DTEND;TZID=America/Los_Angeles:20241015T140000 DTSTAMP:20260530T140143 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:20241008T130000 DTEND;TZID=America/Los_Angeles:20241008T140000 DTSTAMP:20260530T140143 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:20240924T160000 DTEND;TZID=America/Los_Angeles:20240924T180000 DTSTAMP:20260530T140143 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:20240904T130000 DTEND;TZID=America/Los_Angeles:20240904T173000 DTSTAMP:20260530T140143 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:20240827T150000 DTEND;TZID=America/Los_Angeles:20240827T160000 DTSTAMP:20260530T140143 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240724T100000 DTEND;TZID=America/Los_Angeles:20240724T110000 DTSTAMP:20260530T140143 CREATED:20240717T171141Z LAST-MODIFIED:20240717T171141Z UID:10000489-1721815200-1721818800@datascience.ucsd.edu SUMMARY:HDSI/TILOS Seminar | Rob Nowak | What Kinds of Functions do Neural Networks Learn? Theory and Practical Applications DESCRIPTION:When Wednesday July 24th 10:00am *Updated\nWhere: HDSI 123 * Updated\nZoom Info: https://ucsd.zoom.us/j/99334315002 *Updated \nTitle: What Kinds of Functions do Neural Networks Learn?  Theory and Practical Applications \nAbstract:  This talk presents a theory characterizing the types of functions neural networks learn from data. Specifically\, the function space generated by deep ReLU networks consists of compositions of functions from the Banach space of second-order bounded variation in the Radon transform domain. This Banach space includes functions with smooth projections in most directions. A representer theorem associated with this space demonstrates that finite-width neural networks suffice for fitting finite datasets. The theory has several practical applications. First\, it provides a simple and theoretically grounded method for network compression. Second\, it shows that multi-task training can yield significantly different solutions compared to single-task training\, and that multi-task solutions can be related to kernel ridge regressions. Third\, the theory has implications for improving implicit neural representations\, where multi-layer neural networks are used to represent continuous signals\, images\, or 3D scenes. This exploration bridges theoretical insights with practical advancements\, offering a new perspective on neural network capabilities and future research directions.\nBio: Robert Nowak is the Grace Wahba Professor of Data Science and Keith and Jane Nosbusch Professor in Electrical and Computer Engineering at the University of Wisconsin-Madison. His research focuses on machine learning\, optimization\, and signal processing. He serves on the editorial boards of the SIAM Journal on the Mathematics of Data Science and the IEEE Journal on Selected Areas in Information Theory.meeting with him. If… URL:https://datascience.ucsd.edu/event/hdsi-tilos-seminar-rob-nowak-what-kinds-of-functions-do-neural-networks-learn-theory-and-practical-applications/ 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/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240722T140000 DTEND;TZID=America/Los_Angeles:20240722T150000 DTSTAMP:20260530T140143 CREATED:20240717T171423Z LAST-MODIFIED:20240717T171423Z UID:10000488-1721656800-1721660400@datascience.ucsd.edu SUMMARY:Mayank Garg | Tackling Acute Respiratory Distress Syndrome (ARDS) : Integrated and Holistic Approaches DESCRIPTION:When Monday July 22nd 2:00pm\nWhere: HDSI MPR 123\nZoom Info: http://bit.ly/HDSI-Seminars \nTitle: “Tackling Acute Respiratory Distress Syndrome (ARDS) : Integrated and Holistic Approaches” \nAbstract: ARDS is a complex heterogenous disorder which forms a significant health care burden. Pathophysiologically\, ARDS is caused by multiple aetiologies which can lead to the diversity in clinical presentation seen. In our work with preclinical rodent models\, we investigate the role of host mitochondrial factors in skewing the inflammation resolution pathways leading to an aggravated and exaggerated state of inflammation and possibly increased mortality. This work elicits another instance of how endotype identification is essential in ARDS (and other diseases)\, to improve translation of basic research. A potential solution for this could be integration of data driven approaches to derive biological insights which would serve as essential context for preclinical disease models. \nMayank Garg Bio: Mayank is a physician scientist who completed his medical graduation and clinical training from IPGME&R and SSKM Hospital\, Kolkata\, India. He gained brief experience in intensive care before switching to experimental research at CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB)\, Delhi\, India. He is affiliated with Ashoka University as a Simons Ashoka Early Career fellow. \nMayank is currently pursuing quantitative health research to explore the heterogeneity of ICU disorders like Sepsis and ARDS. Realising the importance of context in biomedical research\, he aims to derive mechanisms to leverage data science in a clinically relevant manner\, and to validate them using contextual application of experimental models. \nHe also believes in the potential of digital transformation in personal empowerment\, for improving health-care\, sick-care\, and clinical research. He is collaborating on a project to develop a digital framework to assist data collection and aid analysis for personalized lifestyle medicine. He plans to leverage the power of LLMs integrated with such digital frameworks for healthcare and healthcare research. He strongly advocates for collaborative growth and strict ethical standards as the foundation for advancing science. URL:https://datascience.ucsd.edu/event/mayank-garg-tackling-acute-respiratory-distress-syndrome-ards-integrated-and-holistic-approaches/ 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/2024/01/cropped-HDSI-UCSD-Image-e1712856546428.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240618T080000 DTEND;TZID=America/Los_Angeles:20240618T193000 DTSTAMP:20260530T140143 CREATED:20240612T152605Z LAST-MODIFIED:20240612T152605Z UID:10000485-1718697600-1718739000@datascience.ucsd.edu SUMMARY:TILOS Industry Day DESCRIPTION:EVENT WEBSITE \n\n\n\n8:00 – 8:45am\nBreakfast\n\n\n8:45 – 9:00am\nWelcome Remarks and Introduction to TILOS\nDirector Yusu Wang (UCSD) and AD Translation Vijay Kumar (UPenn)\n\n\n9:00 – 10:30am\nSESSION 1\nIndustry Keynote: Nicholas Roy (Zoox and MIT)\nTILOS Faculty Highlights:\nFarinaz Koushanfar (UCSD)\nNisheeth Vishnoi (Yale U)\n\n\n10:30 – 10:45am\nBreak (15 minutes)\n\n\n10:45am – 12:15pm\nSESSION 2\nIndustry Keynote: Nageen Himayat (Intel Labs)\nTILOS Faculty Highlights:\nAlejandro Ribeiro (UPenn)\nSean Gao (UCSD)\n\n\n12:15 – 2:00pm\nTILOS Trainee Poster Lightning Preview Session + Lunch\n\n\n2:00 – 3:00pm\nPanel on Academic-Industry Relations / Collaborations\nInvited Panelists:\nNing Bi (Qualcomm VP Engineering)\nVitaly Feldman (Apple ML Research)\nKatherine Heller (Google Responsible AI)\nTara Javidi (UCSD)\nSomdeb Majumdar (Intel AI/ML Lab)\nModerator: Vijay Kumar (TILOS AD Translation\, UPenn)\n\n\n3:00 – 3:30pm\nBreak (30 minutes)\n\n\n3:30 – 5:00pm\nSESSION 3\nIndustry Keynote: Carolina Parada (Google DeepMind)\nTILOS Faculty Highlights:\nNikolay Atanasov (UCSD)\nMisha Belkin (UCSD)\n\n\n5:00 – 7:30pm\nBuffet Dinner and Trainee Poster Session URL:https://tilos.ai/events/tilos-industry-day-2024/#new_tab LOCATION:Halıcıoğlu Data Science Institute\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA CATEGORIES:Symposium ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240606T110000 DTEND;TZID=America/Los_Angeles:20240606T170000 DTSTAMP:20260530T140143 CREATED:20240311T193901Z LAST-MODIFIED:20240606T183803Z UID:10000455-1717671600-1717693200@datascience.ucsd.edu SUMMARY:HDSI Anniversary Symposium DESCRIPTION:[vc_row][vc_column][vc_custom_heading text=”JOIN US FOR THE HALICIOGLU DATA SCIENCE INSTITUTE ANNIVESARY SYMPOSIUM!” font_container=”tag:h4|text_align:left|color:%2300629b” css=”” custom_css=”font-family: ‘Refrigerator Deluxe Extrabold’ !important;”][vc_column_text css=””]Date: June 6\nTime: 11:00 AM – 5:00 PM\nLocation: Halıcıoğlu Data Science Institute\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093 \nJoin us to celebrate the advancements and future of the Halıcıoğlu Data Science Institute. The event will feature: \n\nKeynote Speaker: Thomas Andriola\, Vice Chancellor and Chief Digital Officer\, UC Irvine\nFaculty Talks: Tiffany Amariuta\, Haojian Jin\, Sooyhun Liao\nPanel Discussions\nPhD Poster Session\n\nRSVP: Register Below[/vc_column_text][vc_separator color=”custom” css=”” accent_color=”#d462ad”][vc_custom_heading text=”AGENDA” font_container=”tag:h4|text_align:left|color:%2300629b” css=”” custom_css=”font-family: ‘Refrigerator Deluxe Extrabold’ !important;”][vc_column_text css=””]Time HDSI 6-Year Symposium 11:00 amPoster Session & Check In11:30 amLunch12:30 pmWelcome Remarks12:45 pmHDSI Data Planet \n\n \n Arun Kumar \n \n \n Associate Professor\, CSE and HDSI \n \n \n \n\n \n Jingbo Shang \n \n \n Assistant Professor\, CSE and HDSI \n \n \n 1:30 pmModerator Introduction \n\n \n Benjamin Smarr \n \n \n Moderator | Assistant Professor\, HDSI and Bioengineering \n \n \n 1:35 pmKeynote | Data Science: Where do we go from here? \n\n \n Thomas Andriola \n \n \n Vice Chancellor\, Information Technology and Data; Chief Digital Officer\, UC Irvine \n \n \n 2:25 pmQ&A2:35 pmBreak2:45 pmEquity in Healthcare \n\n \n Tiffany Amariuta \n \n \n Assistant Professor\, HDSI and Biomedical Informatics \n \n \n 3:05 pmSecurity & Privacy \n\n \n Haojian Jin \n \n \n Assistant Professor\, HDSI \n \n \n 3:25 pmScalable education using student data \n\n \n Sooyhun Liao \n \n \n Assistant Teaching Professor\, HDSI \n \n \n 3:45 pmPanel Discussion – Impact of Data Science \n\n \n Sooyhun Liao \n \n \n Assistant Teaching Professor\, HDSI \n \n \n \n\n \n Haojian Jin \n \n \n Assistant Professor\, HDSI \n \n \n \n\n \n Tiffany Amariuta \n \n \n Assistant Professor\, HDSI and Biomedical Informatics \n \n \n \n\n \n Benjamin Smarr \n \n \n Moderator | Assistant Professor\, HDSI and Bioengineering \n \n \n 4:10 pmPanel Discussion – Future of Data Science at UCSD \n\n \n Frank Wuerthwein \n \n \n Director\, San Diego Supercomputer Center \n \n \n \n\n \n Rajesh Gupta \n \n \n Founding Director\, Halıcıoğlu Data Science Institute \n \n \n \n\n \n Sorin Lerner \n \n \n Chair\, Computer Science & Engineering \n \n \n \n\n \n Bill Lin \n \n \n Chair\, Electrical and Computer Engineering \n \n \n \n\n \n Shankar Subramaniam \n \n \n Moderator | Distinguished Professor\, Bioengineering\, Computer Science & Engineering\, Cellular & Molecular Medicine\, and Nanoengineering \n \n \n \n\n \n Michael Holst \n \n \n Chair\, Mathematics \n \n \n 4:50 pmClosing Remarks[/vc_column_text][vc_separator color=”custom” css=”” accent_color=”#d462ad”][/vc_column][/vc_row] URL:https://datascience.ucsd.edu/event/hdsi-6-year-symposium/ LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States CATEGORIES:Symposium ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/03/HDSI_6th_Anniversary_V2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240605T123000 DTEND;TZID=America/Los_Angeles:20240605T140000 DTSTAMP:20260530T140143 CREATED:20240528T231005Z LAST-MODIFIED:20240528T233634Z UID:10000482-1717590600-1717596000@datascience.ucsd.edu SUMMARY:Inaugural Graduate Commencement Awards Luncheon DESCRIPTION:The 2024  Inaugural Graduate Commencement Awards Luncheon will take place on Wednesday June 5 at HDSI’s Multi-Purpose Room. Join us in celebrating the successes of our graduating MS and PhD. Space is limited and we have to confirm catering numbers\, so please RSVP no later than Friday 5/31!!\nWhen: Wed June 5\, 2024\nTime: 12:30pm – 2:00pm\nWhere: HDSI Ground Floor\, Multi-purpose Room\nDress: Business Casual URL:https://datascience.ucsd.edu/event/inaugural-graduate-commencement-awards-luncheon/ LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States CATEGORIES:Student Event 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:20240603T153000 DTEND;TZID=America/Los_Angeles:20240603T170000 DTSTAMP:20260530T140143 CREATED:20240612T154318Z LAST-MODIFIED:20240612T154318Z UID:10000486-1717428600-1717434000@datascience.ucsd.edu SUMMARY:Bhanu Teja Gullapalli’s Dissertation Defense - June 3\, at 3:30 pm. PST. DESCRIPTION:Please join the HDSI PhD Program for Bhanu Teja Gullapalli’s presentation of his dissertation on June 3\, at 3:30 pm. PST. \nThis defense will take place in-person\, (HDSI Conference Room 138 ) but there will also be a Zoom link provided for remote attendees. \nPlease note that the Zoom meeting will be recorded and the committee will meet in closed session after the presentation. \n\nTitle: Harnessing Digital Biomarkers of Substance Use and Addiction with Large-Scale Mobile Sensor Data \nAbstract: Mobile sensors are often used in health to track and monitor health\, ranging from daily activities to diagnosing life-threatening conditions; however\, they are underutilized for substance use and its disorders. Our work is focused on developing digital biomarkers from the physiological data captured from wearable devices for substance use. Specifically\, we build models that combine the multimodal sensor data from wearable devices to detect drug administrations\, predict drug-induced mental states such as drug craving and euphoria. We further show that integrating drug pharmacokinetics information into these data-driven models enhances the accuracy of drug monitoring\, thereby increasing the generalizability and trust. A consistent pattern observed among these models was bias based on drug-usage history; therefore\, we develop a model that screens users and distinguishes opioid misusers from prescription users\, which would allow for more accurate prescription of opioids\, minimizing the risk of addiction. URL:https://datascience.ucsd.edu/event/bhanu-teja-gullapallis-dissertation-defense-june-3-at-330-pm-pst/ LOCATION:https://ucsd.zoom.us/j/92070614513 CATEGORIES:HDSI Event END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240529T140000 DTEND;TZID=America/Los_Angeles:20240529T153000 DTSTAMP:20260530T140143 CREATED:20240525T004222Z LAST-MODIFIED:20240525T004904Z UID:10000479-1716991200-1716996600@datascience.ucsd.edu SUMMARY:Forecasting the Antibody Response against the Influenza Virus | Tal Einav DESCRIPTION:Abstract: Although influenza is one of the best-studied viruses\, vaccine effectiveness remains around 20-50%. A sizable fraction of people exhibit a weak or short-lived antibody response following vaccination\, yet we cannot identify these individuals a priori nor ascertain whether a different vaccine would have served them better. In this talk\, we demonstrate how machine learning can leverage the wealth of prior studies to forecast each person’s vaccine response. We will discuss when these predictions are accurate\, when they fall short\, and some of the exciting possibilities for the future of this field. \n\nBio: Tal Einav’s career path has included a year-long sabbatical as a software developer\, teaching courses at the Marine Biology Laboratory (100 hours per week!)\, and training at Caltech and the Fred Hutch Cancer Center. He runs the Computational Immunology Lab at LJI\, and his work blends questions and techniques from computer science and biology to predict how biological systems will behave. In today’s talk\, the biological system is everyone in this room\, and the question is what will happen when you receive your next influenza vaccine. URL:https://datascience.ucsd.edu/event/forecasting-the-antibody-response-against-the-influenza-virus-tal-einav/ 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/2024/01/HDSI-UCSD-Image_Dark-blue-e1710178042629.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240522T140000 DTEND;TZID=America/Los_Angeles:20240522T150000 DTSTAMP:20260530T140143 CREATED:20240520T222549Z LAST-MODIFIED:20240525T004748Z UID:10000481-1716386400-1716390000@datascience.ucsd.edu SUMMARY:Paths to AI Accountability | Sarah H. Cen DESCRIPTION:Speaker: Sarah H. Cen\n\nAbstract: We have begun grappling with difficult questions related to the rise of AI\, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with AI accountability: determining who owes responsibility and to whom in the age of AI. In this talk\, I will discuss the two main components of AI accountability\, then illustrate them through a case study on social media. Within the context of social media\, I will focus on how social media platforms filter (or curate) the content that users see. I will review several methods for auditing social media\, drawing from concepts and tools in hypothesis testing\, causal inference\, and LLMs.\n\nBio: Sarah is a final-year PhD student at MIT in the Electrical Engineering and Computer Science Department advised by Professor Aleksander Mądry and Professor Devavrat Shah. Sarah utilizes methods from machine learning\, statistical inference\, causal inference\, and game theory to study responsible computing and AI policy. Previously\, she has written about social media\, trustworthy algorithms\, algorithmic fairness\, and more. She is currently interested in AI auditing\, AI supply chains\, and IP Law x Gen AI. URL:https://datascience.ucsd.edu/event/paths-to-ai-accountability/ 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/2024/01/HDSI-UCSD-Image_Dark-blue-e1710178042629.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240522T100000 DTEND;TZID=America/Los_Angeles:20240522T110000 DTSTAMP:20260530T140143 CREATED:20240521T224521Z LAST-MODIFIED:20240521T224521Z UID:10000480-1716372000-1716375600@datascience.ucsd.edu SUMMARY:TILOS Seminar: Large Datasets and Models for Robots in the Real World DESCRIPTION:Large Datasets and Models for Robots in the Real World\nNicklas Hansen\, UC San Diego\nHDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: Recent progress in AI can be attributed to the emergence of large models trained on large datasets. However\, teaching AI agents to reliably interact with our physical world has proven challenging\, which is in part due to a lack of large and sufficiently diverse robot datasets. In this talk\, I will cover ongoing efforts of the Open X-Embodiment project–a collaboration between 279 researchers across 20+ institutions–to build a large\, open dataset for real-world robotics\, and discuss how this new paradigm is rapidly changing the field. Concretely\, I will discuss why we need large datasets in robotics\, what such datasets may look like\, and how large models can be trained and evaluated effectively in a cross-embodiment cross-environment setting. Finally\, I will conclude the talk by sharing my perspective on the limitations of current embodied AI agents\, as well as how to move forward as a community. \n\nNicklas Hansen is a Ph.D. student at University of California San Diego advised by Prof. Xiaolong Wang and Prof. Hao Su. His research focuses on developing generalist AI agents that learn from interaction with the physical and digital world. He has spent time at Meta AI (FAIR) and University of California Berkeley (BAIR)\, and received his B.S. and M.S. degrees from Technical University of Denmark. He is a recipient of the 2024 NVIDIA Graduate Fellowship\, and his work has been featured at top venues in machine learning and robotics. Webpage: www.nicklashansen.com URL:https://datascience.ucsd.edu/event/tilos-seminar-large-datasets-and-models-for-robots-in-the-real-world/ 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/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240508T070000 DTEND;TZID=America/Los_Angeles:20240508T200000 DTSTAMP:20260530T140143 CREATED:20240501T163014Z LAST-MODIFIED:20240501T163014Z UID:10000478-1715151600-1715198400@datascience.ucsd.edu SUMMARY:Compassionate constructive laziness | Brad Voytek DESCRIPTION:The Triton Neurotech and TNT Academy team is excited to announce their first event in their Professor Talk series! Join them for a talk put on by Dr. Bradley Voytek on Wednesday\, May 8th from 7-8pm in Henry Booker Room\, Jacobs Hall 2512.\n\nDr. Voytek is a Professor in the Department of Cognitive Science\, the Halıcıoğlu Data Science Institute\, and the Neurosciences Graduate Program at UC San Diego. After his PhD at UC Berkeley\, he joined Uber as their first data scientist—when it was a 10-person startup—where he helped build their data science strategy and team. His research lab combines large-scale data science and machine learning to study how brain regions communicate with one another\, and how that communication changes with aging and disease. \nHe is an advocate for promoting science to the public and speaks extensively with students at all grade levels about the joys of scientific research and discovery. To that end he is going to speak about the following: \nTitle: Compassionate constructive laziness \nDescription: Work hard. Follow your passion. Head down\, nose to the grindstone hustle. Who do we do these things for and why? Yes\, hard work is good if you have a clear set of goals and a vision to make your future life better and easier. Yes\, passion is wonderful\, but it can be blinding. And yes\, grinding hassle can get you far\, but at the cost of making many interpersonal interactions primarily transactional. In this discussion\, Dr. Voytek will talk about his experience with the principle of constructive laziness – at first an accidental discovery\, but later a more mindful ethos. URL:https://datascience.ucsd.edu/event/compassionate-constructive-laziness-brad-voytek/ LOCATION:Henry Booker Room\, Jacobs Hall 2512 CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/05/TNT-Academy-and-Triton-Neurotech-BVoytek-e1714580971303.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240423T110000 DTEND;TZID=America/Los_Angeles:20240423T120000 DTSTAMP:20260530T140143 CREATED:20240501T163343Z LAST-MODIFIED:20240501T164107Z UID:10000474-1713870000-1713873600@datascience.ucsd.edu SUMMARY:Building and Deploying Large Language Model Applications Efficiently and Verifiably | Ying Sheng DESCRIPTION:Abstract:  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe applications of large language models (LLMs) are increasingly complex and diverse\, necessitating efficient and reliable frameworks for building and deploying them. In this talk\, I will begin with algorithms and systems for serving LLMs for everyone (FlexGen\, S-LoRA\, VTC)\, highlighting the growing trend of personalized LLM services. My work addresses the need to run LLMs locally for isolated individual needs. It also tackles the problem of efficiency and service fairness when resource sharing among many users is required. Once we have efficient deployment\, a primary concern is the reliability of generation. The second part of this talk aims to address this issue by exploring verifiable code generation. To achieve this\, I adopt tools in formal verification to facilitate LLMs in generating correctness certificates alongside other artifacts (Clover). Finally\, I will touch on future research avenues\, such as integrating formal methods with LLMs and developing programming systems for generative AI. \nBio:  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYing Sheng is a Ph.D. candidate in Computer Science at Stanford University\, advised by Clark Barrett. Her research focuses on building and deploying large language model applications\, emphasizing accessibility\, efficiency\, programmability\, and verifiability. Ying has authored numerous papers in top-tier AI\, system\, and automated reasoning conferences and journals\, such as NeurIPS\, ICML\, ICLR\, OSDI\, SOSP\, IJCAR\, and JAR. Her work has received a Best Paper award (as first author) at IJCAR and a Best Tool Paper award at TACAS. As a core member of the LMSYS Org\, she has developed influential open models\, datasets\, systems\, and evaluation tools\, such as Vicuna\, Chatbot Arena\, and SGLang. Ying is a recipient of the Machine Learning and Systems Rising Stars Award (2023) and the a16z Open Source AI Grant (2023). More information about her can be found at https://sites.google.com/view/yingsheng. URL:https://datascience.ucsd.edu/event/building-and-deploying-large-language-model-applications-efficiently-and-verifiably-ying-sheng/ 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:20240417T100000 DTEND;TZID=America/Los_Angeles:20240417T110000 DTSTAMP:20260530T140143 CREATED:20240409T185225Z LAST-MODIFIED:20240409T185225Z UID:10000469-1713348000-1713351600@datascience.ucsd.edu SUMMARY:TILOS Seminar: Transformers learn in-context by (functional) gradient descent DESCRIPTION:Transformers learn in-context by (functional) gradient descent\nXiang Cheng\, TILOS Postdoctoral Scholar at MIT\nHDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: Motivated by the in-context learning phenomenon\, we investigate how the Transformer neural network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent\, which enables it to learn linear functions in-context. More generally\, we show that a non-linear Transformer can implement functional gradient descent with respect to some RKHS metric\, which allows it to learn a broad class of functions in-context. Additionally\, we show that the RKHS metric is determined by the choice of attention activation\, and that the optimal choice of attention activation depends in a natural way on the class of functions that need to be learned. I will end by discussing some implications of our results for the choice and design of Transformer architectures. URL:https://datascience.ucsd.edu/event/tilos-seminar-transformers-learn-in-context-by-functional-gradient-descent/ LOCATION:Virtual CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240416T140000 DTEND;TZID=America/Los_Angeles:20240416T150000 DTSTAMP:20260530T140143 CREATED:20240410T170408Z LAST-MODIFIED:20240410T170614Z UID:10000472-1713276000-1713279600@datascience.ucsd.edu SUMMARY:Internships: The Student Perspective DESCRIPTION: URL:https://datascience-ucsd.12twenty.com/Login LOCATION:CA CATEGORIES:Workshops END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240416T110000 DTEND;TZID=America/Los_Angeles:20240416T120000 DTSTAMP:20260530T140143 CREATED:20240501T164203Z LAST-MODIFIED:20240501T164547Z UID:10000473-1713265200-1713268800@datascience.ucsd.edu SUMMARY:Unlocking musical creativity with generative AI | Chris Donahue DESCRIPTION:Abstract:  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn this talk\, I will present my work on developing and responsibly deploying generative AI systems that unlock and augment human creative potential in music. While we all possess a remarkably sophisticated intuition for and appreciation of music\, conventional tools for creative musical expression (e.g.\, instruments\, music notation) are inaccessible to those of us without formal training. To lower the barrier to entry\, I develop generative AI systems with intuitive forms of control (e.g.\, singing) that allow users to easily translate their ideas into music. My research also aims to augment the creative potential of experts. To this end\, I develop generative AI methods that can support realistic co-creation workflows for musicians\, analogous to tools like Copilot for programmers. \nMethodologically\, my work often centers around language models (LMs)\, and involves building new LM methods to confront the unique challenges posed by the domain of music\, such as modeling long sequences and understanding multimodal relationships. Another challenge of working in creative domains is evaluation—to confront this\, my work involves deploying systems to real-world users\, so that we may better understand how systems help users accomplish real creative goals. More broadly\, we are in the midst of a pivotal moment in music AI research\, where technological developments are suddenly translating into real-world impact. Accordingly\, I will discuss how I approach my current and future research goals in a responsible fashion\, commensurate with the broad economic\, cultural\, and social importance of music. \nBio:  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChris Donahue is an Assistant Professor in the Computer Science Department at CMU\, and a part-time research scientist at Google DeepMind working on the Magenta project. His research goal is to develop and responsibly deploy generative AI for music and creativity\, thereby unlocking and augmenting human creative potential. In practice\, this involves improving machine learning methods for controllable generative modeling of music and audio\, and deploying real-world interactive systems that allow anyone to harness generative music AI to accomplish their creative goals through intuitive forms of control. Chris’s research has been featured in live performances by professional musicians like The Flaming Lips\, and also empowers hundreds of daily users to convert their favorite music into interactive content through his website Beat Sage. His work has also received coverage from MIT Tech Review\, The Verge\, Business Insider\, and Pitchfork. Before CMU\, Chris was a postdoctoral scholar in the CS department at Stanford advised by Percy Liang. Chris holds a PhD from UC San Diego where he was jointly advised by Miller Puckette (music) and Julian McAuley (CS). URL:https://datascience.ucsd.edu/event/unlocking-musical-creativity-with-generative-ai-chris-donahue/ LOCATION:Computer Science and Engineering Building\, 3235 Voigt Dr\, La Jolla\, CA 92093\, USA\, Room 1242 CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240411T160000 DTEND;TZID=America/Los_Angeles:20240411T170000 DTSTAMP:20260530T140143 CREATED:20240409T185440Z LAST-MODIFIED:20240409T185440Z UID:10000471-1712851200-1712854800@datascience.ucsd.edu SUMMARY:Virtual Alumni Spotlight DESCRIPTION: URL:https://datascience.ucsd.edu/event/virtual-alumni-spotlight/ LOCATION:https://ucsd.zoom.us/j/93048333550 CATEGORIES:Seminar ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2024/03/HDSI_Alumni_Spotlight_Flyer_V1-scaled-e1712855056543.jpg END:VEVENT END:VCALENDAR