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DTSTART;TZID=America/Los_Angeles:20260205T150000
DTEND;TZID=America/Los_Angeles:20260205T160000
DTSTAMP:20260531T014031
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SUMMARY:HDSI Seminar Series - Stephen Pizer - A geometric model for anatomic objects targeted for statistical analysis
DESCRIPTION:Title:\nA geometric model for anatomic objects targeted for statistical analysis\nSpeaker:\nStephen Pizer\, Kenan Professor of Computer Science\, University of North Carolina at Chapel Hill \nAbstract:\nA novel representation called the evolutionary s-rep represents anatomic objects by encompassing an unusually rich collection of geometric features\, image intensity based\nfeatures\, genetic features\, etc.  It can be used for shape-based classification of anatomic objects\, hypothesis testing on inter-class shape variations\, and object segmentation\, using web-\nresident software. The evolutionary s-rep and example applications will be discussed. This new representation provides better statistics than alternatives because its across-object positional correspondences are based on geometry not just on the object boundary but also in its interior\, on second-order and not just positional (0th order) features\, and on finding for each case a\ndiffeomorphism from a basic\, ellipsoidal object. \nSpeaker Bio\n“My PhD dissertation\, in 1967\, was the first in medical image computing\, and I have been teaching\, writing\, and doing research in that area continuously since my first summer job at Massachusetts General Hospital in 1962 and since my joining the UNC Computer Science faculty in 1967. I have had many collaborations across the UNC Medical School\, in some of whose departments I have had adjunct appointments. I led the committee that led to the formation of the Biomedical Research Imaging Center at UNC. 61 PhDs have been produced with me as principal advisor. \nMy early research emphases were on image quality restoration and on 2D and 3D display\, as well as related models of human vision. I helped advise Charles Metz’s dissertation in that area\, and we collaborated for some years\, including a paper that was an early component of the EM algorithm. However\, for the last 4.5 decades my focus has been on geometric models of anatomic objects\nespecially suited for statistics\, statistical analyses of these\, and the applications of that in diagnosis\, treatment planning\, and object segmentation and registration\, with clinical targets all over the body and with many image sources. Major medical targets were radiation oncology\, neuroscience\, and recently colonoscopy. The form of model I have developed is skeletal\, and in particular what we call the evolutionary s-rep\, with its advantages over object boundary based models of also locally capturing interior properties such as object curvature and cross-object width and of providing an object-relative coordinate system important in accessing image intensities as they are used for segmentation and registration. Towards diagnosis and other statistical objectives\, my statistics professor colleague JS Marron and I have made important contributions in methods of statistics of shape that recognize that object geometry cannot be directly analyzed by Euclidean methods because abstractly it resides on a curved manifold. Most especially\, the method called Principal Nested Spheres (CPNS)\, allowing statistical analysis of directional data\, was developed in our laboratory. Collaborations with Kitware\, Inc. in software development and tutorials related to shape analysis in the salt.slicer.org toolkit including s- reps uses\, especially by Jared Vicory\, have been and continue to be important. Successes of a variety of types for statistics on s-reps include the commercial success of the company\, Morphormics\, now part of\nAccuray\, that we spun off and whose main product at that time\, built upon statistics of skeletal models\, focused on segmentation of male pelvic organs from CT for radiation treatment planning. Other work showed registrations and segmentations of mobile structures across medical imaging modalities. Our work over the last decade or so shows that our latest form of s-reps that evolve from ellipsoids while according to rich geometric properties of the object interior and boundary yield notable improvements over boundary point distribution models and models based on smooth deformations of means for object representation in classification\, hypothesis testing\, and production of correspondence across a population of neuroanatomic objects.”
URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-stephen-pizer-a-geometric-model-for-anatomic-objects-targeted-for-statistical-analysis/
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/2026/01/steve-p.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250331T110000
DTEND;TZID=America/Los_Angeles:20250331T120000
DTSTAMP:20260531T014031
CREATED:20250318T195914Z
LAST-MODIFIED:20250318T195914Z
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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:20250205T140000
DTEND;TZID=America/Los_Angeles:20250205T153000
DTSTAMP:20260531T014031
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:20240904T130000
DTEND;TZID=America/Los_Angeles:20240904T173000
DTSTAMP:20260531T014031
CREATED:20240821T160959Z
LAST-MODIFIED:20240821T161832Z
UID:10000496-1725454800-1725471000@datascience.ucsd.edu
SUMMARY:Encore Industry Day
DESCRIPTION:The UC San Diego EnCORE is excited to host Industry Day on Wednesday\, September 4th\, 2024\, at the UCSD campus. This all-day event will showcase exciting research in foundations of data science\, ML systems and AI\, with contributions from leading experts in industry and academia. Attendees will have the opportunity to network with entrepreneurs\, industry researchers\, faculty and peers from leading institutions. The event introduces a dynamic exploration of cutting-edge advancements and collaborative opportunities centered around machine learning and AI. Register Now!  \n \nTime: Noon – 1:00 pm (PST)\, and 4:30 pm- 5:30 pm PST\n\nLocation: Atkinson Hall\, 4th Floor/EnCORE Space\, UC San Diego \n  \n\n\nWho should apply? CSE PhD Students and Postdocs.  We will consider Masters students and undergraduate students as well if accompanied by an advisor letter on the quality of the work. The space is limited. Apply soon to be considered. – Register Now!  \n  \n\nPoster Requirements\n\n\n\nA pdf of your poster must be submitted by August 29th at 9 AM (PST) to Zaray Aguilar\, EnCORE Executive Assistant\, zaaguilar@ucsd.edu or uploaded to this form\nIf you do not submit your poster file in time\, you MUST PRINT & BRING your poster to the event.\nPosters must be 36″ x 24″ vertical orientation\nEnCORE Students must download and use this poster template\n\n  \n\n\nLightning Talk Requirements\n\n\n\nA .pdf of your 1 slide must be submitted by August 29th at 9 AM (PST) to Zaray Aguilar\, EnCORE Executive Assistant\, zaaguilar@ucsd.edu or uploaded to this form. \nWe will not accept any other file types or more than 1 slide.\nYou will have 1 minute to present what your poster is about. If you go over 1 minute\, you will be asked to stop.\n\nEnCORE Students must download and use this slide template\n\n  \n\n\n\n\nWe will welcome posters on the topics on:\n\n\n\n\n\nTheoretical Computer Science\nAI foundations and applications \nResponsible Machine Learning\nInformation Theory\nOptimization\nComputational Games\nFoundations of Neuroscience\n\n\n \n\nThere will be several PRIZES for best posters! \nGeneral Registration ( For all ) \n\n\n\nPost Doc & Grad Registration ( For Post Doc & Grad students only )\n\n 
URL:https://datascience.ucsd.edu/event/encore-industry-day/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Industry
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/08/Encore_Industry_Day_Flyer.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240423T110000
DTEND;TZID=America/Los_Angeles:20240423T120000
DTSTAMP:20260531T014031
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:20240401T110000
DTEND;TZID=America/Los_Angeles:20240401T123000
DTSTAMP:20260531T014031
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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240220T130000
DTEND;TZID=America/Los_Angeles:20240220T143000
DTSTAMP:20260531T014031
CREATED:20240220T182547Z
LAST-MODIFIED:20240220T182547Z
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SUMMARY:Learning Inductive Representations for Reasoning over Knowledge Graphs | Zhaocheng Zhu
DESCRIPTION:Abstract: Reasoning\, the ability to logically draw conclusions from existing knowledge\, has been long pursued as a goal of artificial intelligence. Although numerous learning algorithms have been developed for reasoning\, most of them are limited to the domain they are trained on. By contrast\, humans often derive high-level rules or principles from experience and apply them to new domains — an ability referred as inductive generalization. In this talk\, we present a series of works that learn inductive representations for reasoning over knowledge graphs. First\, we introduce Neural Bellman-Ford Networks (NBFNet) that captures paths between entities and can generalize to graphs of new entities. Then we discuss Graph Neural Network Query Executor (GNN-QE)\, an extension of NBNet that answers multi-hop logical queries and generalizes well on our inductive benchmark. Finally\, by learning inductive representations for both entities and relations\, we demonstrate that a model can generalize to any graph with arbitrary entity and relation vocabularies\, paving the way for foundation models for knowledge graph reasoning. \n \nBio: Zhaocheng Zhu is a final-year Ph.D. candidate advised by Prof. Jian Tang at Mila – Quebec AI Institute\, University of Montreal. His research interests include reasoning\, knowledge graphs and large language models. His works\, among the first to study inductive generalization across structures\, have led to a paradigm shift away from traditional knowledge graph embedding methods that have been used for years. He gave a tutorial on knowledge graph reasoning at AAAI 2022. He is also an active developer of machine learning systems\, and led the development of two open-source libraries\, GraphVite for large-scale embedding training and TorchDrug for drug discovery research.
URL:https://datascience.ucsd.edu/event/learning-inductive-representations-for-reasoning-over-knowledge-graphs-zhaocheng-zhu/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240201
DTEND;VALUE=DATE:20240203
DTSTAMP:20260531T014031
CREATED:20240201T173318Z
LAST-MODIFIED:20240201T173318Z
UID:10000433-1706745600-1706918399@datascience.ucsd.edu
SUMMARY:ENCORE: NSF TRIPODS Workshop
DESCRIPTION:
URL:https://encore.ucsd.edu/nsf-tripods-workshop/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Workshops
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/Encore-logo_HDSI-Website.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240124T140000
DTEND;TZID=America/Los_Angeles:20240124T153000
DTSTAMP:20260531T014031
CREATED:20240121T234257Z
LAST-MODIFIED:20240122T001144Z
UID:10000424-1706104800-1706110200@datascience.ucsd.edu
SUMMARY:Statistics | Enric Boix
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/statistics-enric-boix/
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
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