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DTSTART;TZID=America/Los_Angeles:20251203T110000
DTEND;TZID=America/Los_Angeles:20251203T123000
DTSTAMP:20260530T192034
CREATED:20251002T164101Z
LAST-MODIFIED:20251002T164101Z
UID:10000529-1764759600-1764765000@datascience.ucsd.edu
SUMMARY:HDSI/TILOS Seminar - Jeremy Schwartz
DESCRIPTION:Talk Details TBA
URL:https://datascience.ucsd.edu/event/hdsi-tilos-seminar-jeremy-schwartz/
LOCATION:CA
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251117T140000
DTEND;TZID=America/Los_Angeles:20251117T153000
DTSTAMP:20260530T192034
CREATED:20251002T163926Z
LAST-MODIFIED:20251029T222512Z
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SUMMARY:HDSI Distingushed Seminar - Yisong Yue
DESCRIPTION:TITLE Design\, Measure\, Interpret: Foundation Models in the Scientific Loop \nABSTRACT “As foundation models become powerful scientific priors\, a central question emerges: how can they drive the full cycle of discovery—from designing experiments to interpreting results? This talk presents a probabilistic framework that unites experiment design and inverse problems under a common foundation model. I will highlight recent progress in adaptive design algorithms and diffusion-based inversion methods\, and discuss how these ideas point toward an AI-driven ecosystem for science.” \nBIO Yisong Yue is a Professor of Computing and Mathematical Sciences at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that\, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong previously served as Senior Program Chair of ICLR 2024 and General Chair of ICLR 2025\, and currently serves on the ICLR board. Yisong’s research interests are centered around machine learning and artificial intelligence\, particularly in getting AI to work in high-stakes and high-expertise domains. To that end\, his research agenda spans both fundamental and applied pursuits\, from novel learning frameworks all the way to deployment in autonomous driving on public roads. His work has been recognized with multiple paper awards and nominations\, including in robotics\, computer vision\, sports analytics\, machine learning for health\, and information retrieval. During his time in industry\, Yisong worked on machine learning approaches to behavior modeling and motion planning for autonomous driving.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-yisong-yue/
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:20251112T110000
DTEND;TZID=America/Los_Angeles:20251112T120000
DTSTAMP:20260530T192034
CREATED:20251106T170549Z
LAST-MODIFIED:20251106T170549Z
UID:10000534-1762945200-1762948800@datascience.ucsd.edu
SUMMARY:TILOS-HDSI Seminar: Adam Oberman - AI Safety Theory: The Missing Middle Ground
DESCRIPTION:The next TILOS-HDSI seminar will be Wednesday\, November 12 at 11am PST with Adam Oberman (McGill University). The title is AI Safety Theory: The Missing Middle Ground. \nTalk Information \nSpeaker: Adam Oberman (McGill University) \nDate & Time: Wednesday\, November 12 @ 11am PST \nVenue: HDSI 123 \nAbstract: Over the past few years\, the capabilities of generative artificial intelligence (AI) systems have advanced rapidly. Along with the benefits of AI\, there is also a risk of harm. In order to benefit from AI while mitigating the risks\, we need a grounded theoretical framework. \nThe current AI safety theory\, which predates generative AI\, is insufficient. Most theoretical AI safety results tend to reason absolutely: a system is a system is “aligned” or “mis-aligned”\, “honest” or “dishonest”. But in practice safety is probabilistic\, not absolute. The missing middle ground is a quantitative or relative theory of safety — a way to reason formally about degrees of safety. Such a theory is required for defining safety and harms\, and is essential for technical solutions as well as for making good policy decisions. \nIn this talk I will: \n\nReview current AI risks (from misuse\, from lack of reliability\, and systemic risks to the economy) as well as important future risks (lack of control).\nReview theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.\nExplain why technical and theoretical safety solutions are valuable\, even by contributors outside of the major labs.\nDiscuss some gaps in the theory and present some open problems which could address the gaps.\n\nBio: Adam Oberman is a Full Professor of Mathematics and Statistics at McGill University\, a Canada CIFAR AI Chair\, and an Associate Member of Mila. He is a research collaborator at LawZero\, Yoshua Bengio’s AI Safety Institute. He has been researching AI safety since 2024. His research spans generative models\, reinforcement learning\, optimization\, calibration\, and robustness. Earlier in his career\, he made significant contributions to optimal transport and nonlinear partial differential equations. He earned degrees from the University of Toronto and the University of Chicago\, and previously held faculty and postdoctoral positions at Simon Fraser University and the University of Texas at Austin.
URL:https://datascience.ucsd.edu/event/tilos-hdsi-seminar-adam-oberman-ai-safety-theory-the-missing-middle-ground/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:HDSI Event,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251110T140000
DTEND;TZID=America/Los_Angeles:20251110T143000
DTSTAMP:20260530T192034
CREATED:20251002T163809Z
LAST-MODIFIED:20251104T223610Z
UID:10000527-1762783200-1762785000@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Tianhao Wang
DESCRIPTION:Speaker: Tianhao Wang\n\nDate & Time: Monday Nov 10th\, 2pm\nLocation: HDSI Multipurpose Room 123\, 1st floor \n\n\nTitle: Adaptive Optimizers: From Structured Preconditioners to Adaptive Geometry\n\nAbstract: Adaptive optimizers such as Adam and Shampoo are workhorses of modern machine learning\, enabling efficient training of large-scale models across architectures and domains. In this talk\, we will present a unified framework for adaptive optimizers with structured preconditioners\, encompassing a variety of existing methods and introducing new ones. Our analysis reveals the fundamental interplay between preconditioner structures and loss geometries\, highlighting in particular that more adaptivity is not always helpful. Furthermore\, the dominance of adaptive methods has recently been challenged by the surprising effectiveness of simpler normalized steepest descent (NSD)–type methods such as Muon\, while a consensus has emerged that both families of methods succeed by exploiting the non-Euclidean geometry of the loss landscape. Building on the proposed framework\, we show that the convergence of adaptive optimizers is governed by a notion of adaptive smoothness\, which contrasts with the standard smoothness assumption leveraged by NSD. In addition\, although adaptive smoothness is a stronger condition\, it enables acceleration via Nesterov momentum\, which cannot be achieved under the standard smoothness assumption in non-Euclidean settings. Finally\, we develop a notion of adaptive gradient variance that parallels adaptive smoothness and yields qualitatively improved guarantees compared to those based on standard gradient variance.\n\n\nSpeaker Bio: Tianhao Wang is an Assistant Professor at the Halıcıoğlu Data Science Institute\, University of California\, San Diego. Prior to UCSD\, he was a Research Assistant Professor at Toyota Technological Institute at Chicago. He received his PhD from the Department of Statistics and Data Science at Yale University in 2024. His research focuses on theoretical foundations at the intersection of deep learning\, optimization\, and statistics.\nMore info is available on Professor Wang’s website: https://tiiao.github.io/
URL:https://datascience.ucsd.edu/event/hdsi-seminar-tianhao-wang/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251007T140000
DTEND;TZID=America/Los_Angeles:20251007T150000
DTSTAMP:20260530T192034
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:20250929T140000
DTEND;TZID=America/Los_Angeles:20250929T153000
DTSTAMP:20260530T192034
CREATED:20250918T200519Z
LAST-MODIFIED:20250918T205524Z
UID:10000523-1759154400-1759159800@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Regina Liu - Fusion Learning: Fusing Inferences from Diverse Data Sources
DESCRIPTION:HDSI will be hosting its first Seminar Series speaker of the academic year at the end of this month. Regina Liu (Rutgers University) will be giving a talk Monday Sept 29th at 2pm in the HDSI Multipurpose room\, 1st floor Room 123. \n\nSpeaker: Regina Liu\nDate & Time: Monday Sept 29th\, 2pm\nLocation: HDSI Multipurpose Room 123\, 1st floor \n\nTalk Title: Fusion Learning: Fusing Inferences from Diverse Data Sources\n\nAbstract: \nAdvanced data acquisition technology has greatly increased the accessibility of complex inferences\, based on summary statistics or sample data\, from diverse data sources. Fusion learning refers to combining complex inferences from multiple sources to yield a more effective overall. We focus on the tasks: 1) Whether/When to combine inferences? 2) How to combine inferences efficiently? 3) How to combine inferences to enhance an individual study\, thus named i-Fusion?\n\nWe present a general framework for nonparametric and efficient fusion learning. The main tool underlying this framework is the new notion of depth confidence distribution (depth-CD)\, developed by combining data depth\, bootstrap and confidence distributions. We show that a depth-CD is an omnibus form of confidence regions\, whose contours of level sets shrink toward the true parameter value\, and thus an all-encompassing inferential tool. The approach is efficient\, general and robust\, and readily applies to heterogeneous studies covering a broad range of complex settings. The approach is demonstrated with an aviation safety analysis application in tracking aircraft landing performance and a zero-event studies in clinical trials with non- estimable parameters. \n\nKey words: confidence distribution\, data depth\, fusion learning\, heterogeneous studies\n\nSpeaker Bio:\nRegina Liu is Distinguished Professor\, Rutgers University. Her research areas include data depth\, resampling\, nonparametric statistics\, confidence distribution\, and fusion learning. Aside from theoretical and methodological research\, she has long collaborated with the FAA on aviation safety research projects on process control\, text mining and risk management. \nShe is an elected fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). She is the recipient of 2021 Noether Distinguished Scholar Award (ASA)\, 2024 Elizabeth Scott Award (Committee of Presidents of Statistical Societies (COPSS))\, and the IMS 2025 Neyman Award & Lecture. She has served as Co-Editor for the Journal of the American Statistical Association and as Associate Editor for several journals. She was elected President of the Institute of Mathematical Statistics (IMS)\, 2020-2021.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-reginia-liu-fusion-learning-fusing-inferences-from-diverse-data-sources/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250528T130000
DTEND;TZID=America/Los_Angeles:20250528T143000
DTSTAMP:20260530T192034
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:20250423T130000
DTEND;TZID=America/Los_Angeles:20250423T143000
DTSTAMP:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20260530T192034
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:20240724T100000
DTEND;TZID=America/Los_Angeles:20240724T110000
DTSTAMP:20260530T192034
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:20260530T192034
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:20240529T140000
DTEND;TZID=America/Los_Angeles:20240529T153000
DTSTAMP:20260530T192034
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:20260530T192034
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:20260530T192034
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240508T070000
DTEND;TZID=America/Los_Angeles:20240508T200000
DTSTAMP:20260530T192034
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240423T110000
DTEND;TZID=America/Los_Angeles:20240423T120000
DTSTAMP:20260530T192034
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240417T100000
DTEND;TZID=America/Los_Angeles:20240417T110000
DTSTAMP:20260530T192034
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240416T110000
DTEND;TZID=America/Los_Angeles:20240416T120000
DTSTAMP:20260530T192034
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240411T160000
DTEND;TZID=America/Los_Angeles:20240411T170000
DTSTAMP:20260530T192034
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240410T140000
DTEND;TZID=America/Los_Angeles:20240410T153000
DTSTAMP:20260530T192034
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
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