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DTSTART;TZID=America/Los_Angeles:20260128T110000
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DTSTAMP:20260421T163746
CREATED:20260120T191337Z
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SUMMARY:CSE Colloquium - Towards Relational Foundation Models: Zero-Shot Forecasting over Relational Databases
DESCRIPTION:Presenter: Charilaos I. Kanatsoulis\, Stanford University \nAbstract: Foundation models have transformed unstructured domains such as language and vision\, yet relational datasets\, where most enterprise knowledge lives\, still rely on brittle\, task-specific ML pipelines. I will begin by introducing Relational Deep Learning (RDL)\, a general framework for learning directly from heterogeneous multi-table data\, capturing structure across entities\, attributes\, and relationships without handcrafted schemas or features. \nBuilding on this paradigm\, I will present the Relational Transformer (RT)\, a schema-invariant model pretrained across diverse relational databases that performs structural learning with in-context information and transfers zero-shot to new databases and predictive tasks. By modeling both inter- and intra-table dependencies and reframing prediction as pattern recognition inside a unified latent relational space\, RT represents a concrete step toward relational foundation models that can be prompted\, reused\, and generalized for new problems. \nBio: Charilaos I. Kanatsoulis is a Research Scientist in the Department of Computer Science at Stanford University. He previously was a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania and received his Ph.D. in Electrical and Computer Engineering from the University of Minnesota\, Twin Cities. His research lies at the intersection of machine learning and signal processing\, with a focus on Transformer and foundation model design for structured data\, graph representation learning\, tensor analysis\, and explainable AI. His work has been recognized with the Best Paper Award at the KDD Temporal Graph Learning Workshop (2025) and the Best Student Paper Award at IEEE CAMSAP (2023). He co-instructs CS246 and CS224W at Stanford and previously taught ESE 5140 at Penn. He has organized several community events\, including the Graph Signal Processing short course at IEEE ICASSP 2023\, the Stanford Graph Learning Workshop (2024–2025)\, the Relational Deep Learning tutorial at ACM KDD 2025\, and the New Perspectives in Advancing Graph Machine Learning Workshop at NeurIPS 2025. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 (Refreshments such as coffee\, pastries\, and fruit will be provided.) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-towards-relational-foundation-models-zero-shot-forecasting-over-relational-databases/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260128T120000
DTEND;TZID=America/Los_Angeles:20260128T130000
DTSTAMP:20260421T163746
CREATED:20260121T235125Z
LAST-MODIFIED:20260128T171042Z
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SUMMARY:Statistics Seminar:  Inferring Unobserved Trajectories from Multiple Temporal Snapshots
DESCRIPTION:Presenter: Yunyi Shen\, Ph.D. Candidate\, Department of Electrical Engineering and Computer Science\, Massachusetts Institute of Technology \n\nDescription: Practitioners often aim to infer an unobserved population trajectory using sample snapshots at multiple time points. E.g. given single-cell sequencing data\, scientists would like to learn how gene expression changes over a cell’s life cycle. But sequencing any cell destroys that cell. So we can access data for any particular cell only at a single time point\, but we have data across many cells. The deep learning community has recently explored using Schrödinger bridges (SBs) and their extensions in similar settings. However\, existing methods either (1) interpolate between just two time points or (2) require a single fixed reference dynamic (often set to Brownian motion within SBs). But learning piecewise from adjacent time points can fail to capture long-term dependencies. And practitioners are typically able to specify a model family for the reference dynamic but not the exact values of the parameters within it. So I propose a new method that (1) learns the unobserved trajectories from sample snapshots across multiple time points and (2) requires specification only of a family of reference dynamics\, not a single fixed one. I demonstrate the advantages of my method on simulated and real data\, across applications in biology and oceanography. \nBio: Yunyi Shen is currently a Ph.D. candidate in the Department of Electrical Engineering and Computer Science at MIT. He works in probabilistic machine learning and statistics on problems where data are scarce or noisy\, and as a result require adaptive data collection\, incorporation of domain-specific structure\, and careful downstream evaluation. Drawing on a background in the physical and life sciences\, his work is shaped by close interdisciplinary collaborations and motivated by scientific problems in biology and physics\, such as gene regulation\, fluid dynamics in cells\, wildlife monitoring\, and time-domain astronomy. \nHosted by: Statistics Department  \nZoom link: https://ucsc.zoom.us/j/93769232971?pwd=msPkbjtoK3LiI9qHjLT1bv8idV23qU.1
URL:https://events.ucsc.edu/event/statistics-seminar-inferring-unobserved-trajectories-from-multiple-temporal-snapshots/
LOCATION:https://ucsc.zoom.us/j/93769232971?pwd=msPkbjtoK3LiI9qHjLT1bv8idV23qU.1
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260128T160000
DTEND;TZID=America/Los_Angeles:20260128T173500
DTSTAMP:20260421T163746
CREATED:20260126T232823Z
LAST-MODIFIED:20260126T233121Z
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SUMMARY:Ancestral Algorithms: Indigenous Virtual Realities & the Ethics of AI
DESCRIPTION:This talk explores how Indigenous analytic and ancestral technologies\, rooted in technē as craft\, knowledge\, and skilled practice\, inform contemporary digital forms such as Virtual Reality and AI. It traces how ancestral memory is transferred\, adapted\, and sustained across generations through decolonial and anti-colonial frameworks\, while critically engaging both the generative possibilities and the structural pitfalls of digital technologies as they shape relational\, technological futures. \nThis event is presented as part of the Creative Interventions (CI) series and is co-sponsored by the Arts Division’s Creative Technologies program and Porter College at UC Santa Cruz.\n—\nADMISSION\n– Free and open to UCSC affiliates.\n– This is an online event.\n– Registration is required here.\n—\nFULL SCHEDULE OF EVENTS\n– Event dates to be announced throughout the 2025-26 academic year.\n—\nABOUT THE SERIES\nCreative Interventions addresses the interconnected work of artists\, designers\, activists\, and knowledge workers—and the intrinsic and transformative capacity of that work to cultivate a just society. More information about the Creative Technologies program.\n—\nThis program is open to all UC Santa Cruz affiliates consistent with state and federal law.
URL:https://events.ucsc.edu/event/ancestral-algorithms/
LOCATION:CA
CATEGORIES:Lectures & Presentations
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