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SUMMARY:Merrill College Undergraduate Research Mentorship Program
DESCRIPTION:The program offers Merrill students an invaluable professional opportunity: the chance to work closely with a faculty member and get the kind of experience and advice that can prepare students for graduate or professional school or a career. Students who participate in the program will be employed as Research Assistants. \nThis program enables Merrill College Faculty Fellows to benefit from paid research assistance from an undergraduate affiliate of Merrill College.  \nThe application is submitted by the faculty member rather than the student.  \nFor additional information and application: DEADLINE EXTENDED to January 23\, 2026 \nMerrill College Undergraduate Research Mentorship Program
URL:https://events.ucsc.edu/event/merrill-college-undergraduate-research-mentorship-program/
LOCATION:CA
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SUMMARY:Sharma\, R. (CSE) - Automatically Evolving GPU Libraries for Performance Portable AI Kernels
DESCRIPTION:GPUs are the workhorses of modern AI\, widely deployed and developed by many vendors including Apple\, Qualcomm\, Intel\, AMD\, and NVIDIA. While these GPUs all offer high compute potential\, programming them effectively is difficult because they differ in performance-critical features like SIMT width\, cache capacity\, and memory bandwidth\, demanding different optimization strategies. Tunable kernels address this by exposing parameters such as tiling dimensions and workgroup sizes\, enabling per-device specialization. Yet this produces static libraries: tuned once\, then frozen\, degrading as new hardware emerges. We propose automatically evolving libraries that expand their tuning knowledge as new hardware emerges\, with minimal impact on user experience. \nTo build such libraries\, we first need to understand the tuning landscape. We address this through GPU Goldmines\, a WebGPU-based framework for exhaustively collecting tuning data across diverse devices. Our tuned matrix multiplication kernels outperform an optimized baseline by 8.4x on average\, while matrix-vector kernels achieve 93% of platform bandwidth. We find that hyper-tuning for a single GPU causes 50% performance degradation on other devices\, whereas data-driven portability methods recover 88% of peak performance. These kernels are fundamental to the prefill and decode phases of LLM inference. We integrate them into llama.cpp as our evaluation platform\, where they outperform CPU and Vulkan backends. \nBuilding on this data\, we are developing Living Libraries to improve performance continuously without disrupting users. This means choosing good parameters upfront\, learning from real-world execution\, and knowing when to keep searching versus when to stop\, though hand-designed parameter spaces remain inherently bounded. To move beyond this\, we extend toward LLM-based kernel evolution\, where language models propose entirely new kernel variants\, opening a less structured but higher potential search space. \nEvent Host: Rithik Sharma\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Tyler Sorensen & Yuanchao Xu   \n  \nZoom: https://ucsc.zoom.us/j/92739836317?pwd=0ydDzimUFIoaLDUKst96dk27th4lvW.1 \nPasscode: 089560
URL:https://events.ucsc.edu/event/sharma-r-cse-automatically-evolving-gpu-libraries-for-performance-portable-ai-kernels/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260123T120000
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SUMMARY:Statistics Seminar: Heterogeneous Statistical Transfer Learning
DESCRIPTION:Presenter: Subhadeep Paul\, Associate Professor\, Ohio State University \nDescription: In the first part of the talk\, we consider the problem of Transfer Learning (TL) under heterogeneity from a source to a new target domain for high-dimensional regression with differing feature sets. Most homogeneous TL methods assume that target and source domains share the same feature space\, which limits their practical applicability. In applications\, the target and source features are frequently different due to the inability to measure certain variables in data-poor target environments. Conversely\, existing heterogeneous TL methods do not provide statistical error guarantees\, limiting their utility for scientific discovery.  Our method first learns a feature map between the missing and observed features\, leveraging the vast source data\, and then imputes the missing features in the target. Using the combined matched and imputed features\, we then perform a two-step transfer learning for penalized regression. We develop upper bounds on estimation and prediction errors\, assuming that the source and target parameters differ sparsely but without assuming sparsity in the target model. We obtain results for both when the feature map is linear and when it is nonparametrically specified as unknown functions.  Our results elucidate how estimation and prediction errors of HTL depend on the model’s complexity\, sample size\, the quality and differences in feature maps\, and differences in the models across domains. In the second part of the talk\, going beyond linear models\, I will discuss a transfer learning method for nonparametric regression using a random forest. The unknown source and target regression functions are assumed to differ for a small number of features. Our method obtains residuals from a source domain-trained Centered RF (CRF) in the target domain\, then fits another CRF to these residuals with feature splitting probabilities proportional to feature-residual distance covariance. We derive an upper bound on the mean square error rate of the procedure that theoretically brings out the benefits of transfer learning in random forests. Our results explain why shallower trees in the residual random forest in the target domain provide implicit regularization. \nBio:Subhadeep Paul is an Associate Professor in the Department of Statistics at The Ohio State University. He is also a faculty fellow and previously served as a co-director of the foundations of data science and AI community at the Translational Data Analytics Institute at Ohio State. He received his PhD in Statistics from the University of Illinois at Urbana-Champaign in 2017. His research focuses on statistical analysis of complex network-linked data and transfer and federated statistical learning. His research has been funded by two NSF grants from the algorithms of threat detection and mathematics of digital twins programs. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/94465292273?pwd=bQ6MCX0OHYxHqgqNwbEYfgbKWqgNVy.1
URL:https://events.ucsc.edu/event/statistics-seminar-heterogeneous-statistical-transfer-learning/
LOCATION:https://ucsc.zoom.us/j/94465292273?pwd=bQ6MCX0OHYxHqgqNwbEYfgbKWqgNVy.1
CATEGORIES:Lectures & Presentations,Seminars
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SUMMARY:Ecology of Presence: Pathways to the Natural World
DESCRIPTION:Norris Center Art + Science Graduate Fellowship Exhibition \nEcology of Presence: Pathways to the Natural World brings together the work of ten graduate students supported by the Kenneth S. Norris Center for Natural History Art + Science Fellowship\, a program dedicated to creative research connecting art with the natural world. Across media – including sound\, moving image\, music\, performance\, installation\, comics\, social practice\, photography\, and storytelling – the artists in Ecology of Presence emphasize relationality and careful attention to place as essential to building relationships with environs. As accelerating environmental change and technological dependency threaten ways of belonging\, the works in this exhibition maintain a steadfast commitment to interdisciplinary approaches that propose kinship with the natural world. By coming together\, Art + Science Fellows artworks and social practices suggest ways of imagining human life in relation to the more-than-human world.
URL:https://events.ucsc.edu/event/ecology-of-presence-pathways-to-the-natural-world/2026-01-23/
LOCATION:Eloise Pickard Smith Gallery\, 11 Cowell Service Rd\, Santa Cruz\, CA\, 95064
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