BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Halıcıoğlu Data Science Institute - UC San Diego - ECPv6.17.1//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;VALUE=DATE:20251111 DTEND;VALUE=DATE:20251113 DTSTAMP:20250616T155433Z 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:20251007T140000 DTEND;TZID=America/Los_Angeles:20251007T150000 DTSTAMP:20250929T215856Z 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:20250528T130000 DTEND;TZID=America/Los_Angeles:20250528T143000 DTSTAMP:20250519T164145Z 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:20250521T150000 DTEND;TZID=America/Los_Angeles:20250521T170000 DTSTAMP:20250422T202557Z 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:20250423T130000 DTEND;TZID=America/Los_Angeles:20250423T143000 DTSTAMP:20250404T162241Z 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:20250411T140000 DTEND;TZID=America/Los_Angeles:20250411T150000 DTSTAMP:20250408T201744Z 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:20250407T130000 DTEND;TZID=America/Los_Angeles:20250407T150000 DTSTAMP:20250401T200812Z 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:20250331T110000 DTEND;TZID=America/Los_Angeles:20250331T120000 DTSTAMP:20250318T195914Z 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:20250317T110000 DTEND;TZID=America/Los_Angeles:20250317T120000 DTSTAMP:20250313T162327Z 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:20250221T140000 DTEND;TZID=America/Los_Angeles:20250221T153000 DTSTAMP:20250219T201049Z 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:20250130T190217Z 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:20241106T140000 DTEND;TZID=America/Los_Angeles:20241106T150000 DTSTAMP:20241112T204134Z 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:20241029T173302Z 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:20240724T100000 DTEND;TZID=America/Los_Angeles:20240724T110000 DTSTAMP:20240717T171141Z 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:20240717T171423Z 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:20240606T110000 DTEND;TZID=America/Los_Angeles:20240606T170000 DTSTAMP:20240606T183803Z 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:20240603T153000 DTEND;TZID=America/Los_Angeles:20240603T170000 DTSTAMP:20240612T154318Z 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:20240525T004904Z 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:20240423T110000 DTEND;TZID=America/Los_Angeles:20240423T120000 DTSTAMP:20240501T164107Z 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:20240410T140000 DTEND;TZID=America/Los_Angeles:20240410T153000 DTSTAMP:20240409T190531Z CREATED:20240409T185628Z LAST-MODIFIED:20240409T190531Z UID:10000470-1712757600-1712763000@datascience.ucsd.edu SUMMARY:Making the Most of Your Camera | James Tompkin DESCRIPTION:Abstract: Images are everywhere\, especially images of the real world\, and visual computing is important both for reconstructing useful models from these images and for providing us humans with interactive tools for visualization and analysis. These methods aid real world sensing and measurement\, scientific and medical imaging\, and media and the arts. The past three years in visual computing have been populated by methods in neural fields – a flexible way to solve the inverse problems required to reconstruct visual scene models. To make the most of the many images from our cameras\, we must be able to scalably reconstruct large scenes from thousands of images\, and I will discuss how to achieve this with hybrid neural fields. Further\, to make the most of active illumination sensors in our cameras\, we must be able to integrate their different signals\, and I will discuss a physically-based neural field to achieve this\, including for dynamic scenes. Finally\, I will contextualize these tools within ongoing discussions around data and 3D learning. \nBio: James Tompkin (jamestompkin.com) is the John E. Savage Assistant Professor of Computer Science at Brown University. His research at the intersection of computer vision\, computer graphics\, and human-computer interaction helps develop new visual computing tools. His doctoral work at University College London studied large-scale video processing and exploration techniques\, and postdoctoral work at Max-Planck-Institute for Informatics and Harvard University helped create new methods to edit content within images and videos. Recent research has developed new techniques for low-level scene reconstruction\, view synthesis for VR\, and content editing and generation. URL:https://datascience.ucsd.edu/event/making-the-most-of-your-camera-james-tompkin/ 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:20240405T123000 DTEND;TZID=America/Los_Angeles:20240405T140000 DTSTAMP:20240329T001950Z CREATED:20240329T001842Z LAST-MODIFIED:20240329T001950Z UID:10000468-1712320200-1712325600@datascience.ucsd.edu SUMMARY:"Advancing NLP for Timely and Actionable Feedback in Healthcare Conversations" | Veronica Perez-Rosas DESCRIPTION:Abstract: “Effective communication is crucial in healthcare for ensuring successful clinical interactions\, as it affects how patients respond\, the decisions being made by both patients and clinicians\, and the outcomes of treatments. Recent developments in Natural Language Processing (NLP) aim to improve and support these interactions within clinical settings. In this talk\, I will discuss my research on offering timely and actionable evaluative feedback for mental healthcare interactions\, addressing a crucial bottleneck in effective mental healthcare delivery. I will specifically focus on computational approaches for building conversational systems to aid in psychotherapy training\, and present two NLP tasks to generate language-based feedback: (1) generating counselor responses following established counseling strategies\, and (2) offering alternative rewrites to counseling trainees’ responses to refine their counseling skills. I will conclude the talk by outlining future directions towards my long-term agenda of building computational approaches that understand\, model\, and predict health behaviors while also being human-centric and scalable” \nBio: “Veronica Perez-Rosas is an Assistant Research Scientist at the University of Michigan. She received her Ph.D. in Computer Science and Engineering from the University of North Texas in 2014\, and was a postdoctoral fellow at the University of Michigan until 2016. Her research interests include Natural Language Processing\, Machine Learning\, Affect Recognition\, and Multimodal Processing of Human Behavior. Her research focuses on developing computational methods to analyze\, recognize\, and predict human behaviors during social interactions. She has authored papers in leading conferences and journals in Natural Language Processing and Multimodal Processing\, has mentored numerous students in these research areas\, and has served as workshop chair or area chair for multiple international conferences in the field.” URL:https://datascience.ucsd.edu/event/advancing-nlp-for-timely-and-actionable-feedback-in-healthcare-conversations-veronica-perez-rosas/ 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:20240403T140000 DTEND;TZID=America/Los_Angeles:20240403T153000 DTSTAMP:20240329T001345Z CREATED:20240326T221709Z LAST-MODIFIED:20240329T001345Z UID:10000462-1712152800-1712158200@datascience.ucsd.edu SUMMARY:"Contextualized learning for adaptive yet persistent AI in biomedicine" | Ben Lengerich DESCRIPTION:Abstract: “In biomedical data analysis\, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution\, context-specific insights by integrating information across datasets\, but it is difficult to design systems which both share information and dynamically adapt to context. Toward this aim\, this presentation will examine “contextualized learning”\, a meta-learning paradigm which learns relationships between dataset context and statistical parameters. Using contextualized network inference as an illustrative example\, I will show how we can estimate context-specific graphical models\, offering insights such as personalized gene expression analysis for SOTA cancer subtyping. The talk will also discuss trends towards “contextualized understanding”\, bridging statistical and foundation models to standardize interpretability. The primary aim is to illustrate how contextualized learning and understanding contribute to creating learning systems that are both adaptive and persistent\, facilitating cross-context information sharing and detailed analysis.” \nBio: “Ben Lengerich is a Postdoctoral Associate and Alana Fellow at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Broad Institute of MIT and Harvard\, where he is advised by Manolis Kellis. His research in machine learning and computational biology emphasizes the use of context-adaptive models to understand complex diseases and advance precision medicine. Through his work\, Ben aims to bridge the gap between data-driven insights and actionable medical interventions. He holds a PhD in Computer Science and MS in Machine Learning from Carnegie Mellon University\, where he was advised by Eric Xing. His work has been recognized with spotlight presentations at conferences including NeurIPS\, ISMB\, AMIA\, and SMFM\, financial support from the Alana Foundation\, selection as a “”Rising Star in Data Science” by the University of Chicago and UC San Diego\, and “”Next Generation in Biomedicine”” by the Broad Institute.” URL:https://datascience.ucsd.edu/event/special-seminar-ben-lengerich/ LOCATION:Computer Science & Engineering Building (CSE)\, Room 1202 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:20240403T120000 DTEND;TZID=America/Los_Angeles:20240403T133000 DTSTAMP:20240401T225533Z CREATED:20240327T215044Z LAST-MODIFIED:20240401T225533Z UID:10000466-1712145600-1712151000@datascience.ucsd.edu SUMMARY:MathWorks & HDSI AI Seminar | Esperanza Linares DESCRIPTION:HDSI! Come and join MathWorks Engineers for a technical seminar on AI (and lunch!) on Wednesday\, April 3! Come learn why data scientists should learn MATLAB – we will highlight the tools that will be serve your role as data scientists and data science students. You can also learn about our engineer’s journey\, roles available at MathWorks\, and the use of our tools in industry! \nMathworks UCSD Technical Seminar Series \nLow-Code AI in MATLAB \nLearn how you can apply AI in your field without extensive knowledge in programming. This hands-on session includes a quick recap on the fundamentals of AI and three exercises where you will learn how to classify human activities using MATLAB® interactive tools and apps: \n1. Accessing and preprocessing data acquired from a mobile device\n2. Applying clustering to the unlabeled data using the Cluster Data Live Editor Task\n3. Classifying the labeled data using two apps: Classification Learner app and the Deep Network Designer app \nAt the end of the seminar\, you will be able to design and train different machine learning and deep learning models without extensive programming knowledge. You will also learn how to automatically generate code from the interactive workflow. This will not only help you to reuse the models without manually going through all the steps but also to learn programming or advance your coding skills. \nAbout the Speaker: \nEsperanza Linares is a Senior Customer Success Engineer at MathWorks. She is part of a global team that partners with academic and research institutions worldwide\, focusing on student and research success. Before joining MathWorks\, she did her postdoctoral work in the pharmaceutical industry\, where she developed a discrete element method model to simulate the compaction of granular materials. She holds a BS in Mechanical Engineering from UNAM (Mexico) and a Ph.D. in Mechanical Engineering from Caltech. \nRegistration Link: https://forms.office.com/Pages/ResponsePage.aspx?id=ETrdmUhDaESb3eUHKx3B5tTIy0i-nn1KjKWuEYZzK09UNVNXNFM4NTA3Q045REVJWUNHNjcxUkZSTi4u \n*Lunch will be provided URL:https://datascience.ucsd.edu/event/mathworks-hdsi-ai-seminar-esperanza-linares/ 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/2023/03/mathworks_logo.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240402T140000 DTEND;TZID=America/Los_Angeles:20240402T153000 DTSTAMP:20240313T191528Z CREATED:20240313T191528Z LAST-MODIFIED:20240313T191528Z UID:10000459-1712066400-1712071800@datascience.ucsd.edu SUMMARY:Special Seminar | Xuhai Xu DESCRIPTION: URL:https://datascience.ucsd.edu/event/special-seminar-xuhai-xu/ 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:20240401T140000 DTEND;TZID=America/Los_Angeles:20240401T153000 DTSTAMP:20240329T000153Z CREATED:20240304T172031Z LAST-MODIFIED:20240329T000153Z UID:10000453-1711980000-1711985400@datascience.ucsd.edu SUMMARY:"Instance-Optimization: Rethinking Database Design for the Next 1000X" | Jialin Ding DESCRIPTION:Abstract: “Modern database systems aim to support a large class of different use cases while simultaneously achieving high performance. However\, as a result of their generality\, databases often achieve adequate performance for the average use case but do not achieve the best performance for any individual use case. In this talk\, I will describe my work on designing databases that use machine learning and optimization techniques to automatically achieve performance much closer to the optimal for each individual use case. In particular\, I will present my work on instance-optimized database storage layouts\, in which the co-design of data structures and optimization policies improves query performance in analytic databases by orders of magnitude. I will highlight how these instance-optimized data layouts address various challenges posed by real-world database workloads and how I implemented and deployed them in production within Amazon Redshift\, a widely-used commercial database system.” \nBio: “Jialin Ding is an Applied Scientist at AWS. Before that\, he received his PhD in computer science from MIT\, advised by Tim Kraska. He works broadly on applying machine learning and optimization techniques to improve data management systems\, with a focus on building databases that automatically self-optimize to achieve high performance for any specific application. His work has appeared in top conferences such as SIGMOD\, VLDB\, and CIDR\, and has been recognized by a Meta Research PhD Fellowship. To learn more about Jialin’s work\, please visit https://jialinding.github.io/.” URL:https://datascience.ucsd.edu/event/special-seminar-jialin-ding/ 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:20240401T110000 DTEND;TZID=America/Los_Angeles:20240401T123000 DTSTAMP:20240328T234737Z CREATED:20240328T234406Z LAST-MODIFIED:20240328T234737Z UID:10000467-1711969200-1711974600@datascience.ucsd.edu SUMMARY:How Do We Get There?: Toward Intelligent Behavior Intervention | Xuhai Xu DESCRIPTION:Abstract: As the intelligence of everyday smart devices continues to evolve\, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent behavior change intervention pipeline for health — combining behavior modeling & interaction design — seems to be within reach. How do we get there? \nIn this talk\, I will introduce a comprehensive intervention pipeline that bridges behavior science theory-driven designs and generalizable behavior models. I will also introduce my efforts on passive sensing datasets\, human-centered algorithms\, and a benchmark platform that drives the community toward more robust and deployable intervention systems for health and well-being. \nBio: Xuhai “Orson” Xu is a postdoc at MIT EECS. He received his PhD at the University of Washington. Specializing in human-computer interaction\, applied machine learning\, and health\, Xu develops intelligent behavior intervention systems to promote human health and well-being. His research covers two aspects — 1) building deployable human-centered behavior models and 2) designing interactive user experiences — to establish a complete system to improve end-users’ well-being. Moreover\, his research also goes beyond end-users and supports health experts by designing new human-AI collaboration paradigms in clinical settings. Xu has earned several awards\, including 9 Best Paper\, Best Paper Honorable Mention\, and Best Artifact awards. His research has been covered by media outlets such as the Washington Post and ACM News. He was recognized as the Outstanding Student Award Winner at UbiComp 2022\, the 2023 UW Distinguished Dissertation Award\, and the 2024 Innovation and Technology Award at the Western Association of Graduate Schools. \nZoom: https://ucsd.zoom.us/j/92792843021\nPassword: 741675 URL:https://datascience.ucsd.edu/event/how-do-we-get-there-toward-intelligent-behavior-intervention-xuhai-xu/ LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/HDSI-UCSD-Image_Dark-blue-e1710178042629.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240328T140000 DTEND;TZID=America/Los_Angeles:20240328T153000 DTSTAMP:20240326T031112Z CREATED:20240326T031112Z LAST-MODIFIED:20240326T031112Z UID:10000464-1711634400-1711639800@datascience.ucsd.edu SUMMARY:The Emergence of Reproducibility and Generalizability in Diffusion Models | Qing Qu DESCRIPTION:Abstract: We reveal an intriguing and prevalent phenomenon of diffusion models which we term as “consistent model reproducibility”: given the same starting noise input and a deterministic sampler\, different diffusion models often yield remarkably similar outputs while they generate new samples. We demonstrate this phenomenon through comprehensive experiments and theoretical studies\, implying that different diffusion models consistently reach the same data distribution and scoring function regardless of frameworks\, model architectures\, or training procedures. More strikingly\, our further investigation implies that diffusion models are learning distinct distributions affected by the training data size and model capacity\, so that the model reproducibility manifests in two distinct training regimes with phase transition: (i) “memorization regime”\, where the diffusion model overfits to the training data distribution\, and (ii) “generalization regime”\, where the model learns the underlying data distribution and generate new samples with finite training data. Finally\, our results have strong practical implications regarding training efficiency\, model privacy\, and controllable generation of diffusion models\, and our work raises numerous intriguing theoretical questions for future investigation. \nSpeaker Bio: Qing Qu is an assistant professor in EECS department at the University of Michigan. Prior to that\, he was a Moore-Sloan data science fellow at Center for Data Science\, New York University\, from 2018 to 2020. He received his Ph.D from Columbia University in Electrical Engineering in Oct. 2018. He received his B.Eng. from Tsinghua University in Jul. 2011\, and a M.Sc.from the Johns Hopkins University in Dec. 2012\, both in Electrical and Computer Engineering. His research interest lies at the intersection of foundation of data science\, machine learning\, numerical optimization\, and signal/image processing\, with focus on developing efficient nonconvex methods and global optimality guarantees for solving representation learning and nonlinear inverse problems in engineering and imaging sciences. He is the recipient of Best Student Paper Award at SPARS’15\, and the recipient of Microsoft PhD Fellowship in machine learning in 2016\, and best paper awards in NeurIPS Diffusion Model Workshop in 2023. He received the NSF Career Award in 2022\, and Amazon Research Award (AWS AI) in 2023. He is the program chair of the new Conference on Parsimony & Learning. URL:https://datascience.ucsd.edu/event/the-emergence-of-reproducibility-and-generalizability-in-diffusion-models-qing-qu/ 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:20240328T140000 DTEND;TZID=America/Los_Angeles:20240328T153000 DTSTAMP:20240323T082150Z CREATED:20240304T171827Z LAST-MODIFIED:20240323T082150Z UID:10000452-1711634400-1711639800@datascience.ucsd.edu SUMMARY:The Emergence of Reproducibility and Generalizability in Diffusion Models | Qing Qu DESCRIPTION:Abstract: We reveal an intriguing and prevalent phenomenon of diffusion models which we term as “consistent model reproducibility”: given the same starting noise input and a deterministic sampler\, different diffusion models often yield remarkably similar outputs while they generate new samples. We demonstrate this phenomenon through comprehensive experiments and theoretical studies\, implying that different diffusion models consistently reach the same data distribution and scoring function regardless of frameworks\, model architectures\, or training procedures. More strikingly\, our further investigation implies that diffusion models are learning distinct distributions affected by the training data size and model capacity\, so that the model reproducibility manifests in two distinct training regimes with phase transition: (i) “memorization regime”\, where the diffusion model overfits to the training data distribution\, and (ii) “generalization regime”\, where the model learns the underlying data distribution and generate new samples with finite training data. Finally\, our results have strong practical implications regarding training efficiency\, model privacy\, and controllable generation of diffusion models\, and our work raises numerous intriguing theoretical questions for future investigation. \nBio: “Qing Qu is an assistant professor in EECS department at the University of Michigan. Prior to that\, he was a Moore-Sloan data science fellow at Center for Data Science\, New York University\, from 2018 to 2020. He received his Ph.D from Columbia University in Electrical Engineering in Oct. 2018. He received his B.Eng. from Tsinghua University in Jul. 2011\, and a M.Sc.from the Johns Hopkins University in Dec. 2012\, both in Electrical and Computer Engineering. His research interest lies at the intersection of foundation of data science\, machine learning\, numerical optimization\, and signal/image processing\, with focus on developing efficient nonconvex methods and global optimality guarantees for solving representation learning and nonlinear inverse problems in engineering and imaging sciences.\nHe is the recipient of Best Student Paper Award at SPARS’15\, and the recipient of Microsoft PhD Fellowship in machine learning in 2016\, and best paper awards in NeurIPS Diffusion Model Workshop in 2023. He received the NSF Career Award in 2022\, and Amazon Research Award (AWS AI) in 2023. He is the program chair of the new Conference on Parsimony & Learning.” URL:https://datascience.ucsd.edu/event/special-seminar-qing-qu/ 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:20240327T140000 DTEND;TZID=America/Los_Angeles:20240327T153000 DTSTAMP:20240323T081955Z CREATED:20240313T191359Z LAST-MODIFIED:20240323T081955Z UID:10000458-1711548000-1711553400@datascience.ucsd.edu SUMMARY:Towards a Machine Capable of Learning Everything | Hao Liu DESCRIPTION:Abstract: Large generative models such as ChatGPT have led to amazing results and revolutionized artificial intelligence. In this talk\, I will discuss my research on advancing the foundation of these models\, centered around addressing the architectural bottlenecks of learning from everything. First\, I will describe our efforts to remove context size limitations of the transformer architecture. Our new model architecture and training method allow for nearly infinitely large context sizes without approximations. Our proposed technique has been used for building state-of-the-art open-source and proprietary models. I will then discuss the applications of large context in world model learning and in reinforcement learning\, including Large World Model\, the world’s first multimodal model of million-length scale\, and the required training methodologies. Next\, I will introduce my research on unsupervised exploration that pioneered learning beyond existing knowledge\, allowing unsupervised pretrained models to outperform human experts in gameplay and paving the road for learning beyond imitating existing knowledge. Finally\, I will envision the modeling and training paradigms for the next generation of large generative models we should build\, focusing on advances in neural net architecture\, efficient scaling\, large context reasoning\, and discovery.” \nBio: Hao Liu is a final-year Ph.D. candidate in the Department of Electrical Engineering and Computer Sciences at UC Berkeley\, where he is advised by Pieter Abbeel. During his PhD\, he has also spent two years part-time at Google Brain and DeepMind. His research interests focus on the foundations of generative models\, including machine learning and neural networks\, with the goal of developing computationally scalable solutions for generalization. He recently developed Large World Model (LWM) and architectural advances (BlockwiseTransformers\, and RingAttention) for scaling transformers. Earlier\, he pioneered general and scalable unsupervised exploration (APT and APS). His work on million-length contexts has been influential at Google\, Meta\, and the broader industry. Several of his papers have been presented as spotlight and oral presentations at top-tier machine learning conferences\, and have also been featured in popular media\, including MarkTechPost\, Business Insider\, and ZDNet. URL:https://datascience.ucsd.edu/event/special-seminar-hao-liu/ 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:20240326T140000 DTEND;TZID=America/Los_Angeles:20240326T153000 DTSTAMP:20240326T030740Z CREATED:20240304T171618Z LAST-MODIFIED:20240326T030740Z UID:10000451-1711461600-1711467000@datascience.ucsd.edu SUMMARY:Making machine learning predictably reliable | Andrew Ilyas DESCRIPTION:Abstract: “Despite ML models’ impressive performance\, training and deploying them is currently a somewhat messy endeavor. But does it have to be? In this talk\, I overview my work on making ML “predictably reliable”—enabling developers to know when their models will work\, when they will fail\, and why. \nTo begin\, we use a case study of adversarial inputs to show that human intuition can be a poor predictor of how ML models operate. Motivated by this\, we present a line of work that aims to develop a precise understanding of the ML pipeline\, combining statistical tools with large-scale experiments to characterize the role of each individual design choice: from how to collect data\, to what dataset to train on\, to what learning algorithm to use.” \n\nBio “Andrew Ilyas is a PhD student in Computer Science at MIT\, where he is advised by Aleksander Madry and Constantinos Daskalakis. His research aims to improve the reliability and predictability of machine learning systems. He was previously supported by an Open Philanthropy AI Fellowship.” URL:https://datascience.ucsd.edu/event/special-seminar-andrew-ilyas/ 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 END:VCALENDAR