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DTSTART;TZID=America/Los_Angeles:20260302T104000
DTEND;TZID=America/Los_Angeles:20260302T114500
DTSTAMP:20260418T010612
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SUMMARY:ECE 290 Seminar: Precision Nuclear Medicine: Engineering Solutions from Acquisition to Analysis
DESCRIPTION:Presenter: Spencer L. Bowen\, Assistant Professor in the Departments of Radiology and Biomedical Engineering\, UT Southwestern Medical Center \nDescription: The Bowen Lab focuses on the development of tools for positron emission tomography (PET) and hybrid systems (e.g. PET/CT)\, to advance precision imaging for the care and study of oncology\, neurology\, and cardiology patients. Quantitative metrics from PET are integral to both patient workup and clinical research. However\, current approaches to enable quantitative imaging have substantial performance limitations that can compromise study conclusions\, fail to generalize across exams and scanners\, expose patients to additional ionizing radiation\, or necessitate invasive procedures. To address these key barriers\, Dr. Bowen and his team investigate advanced acquisition techniques\, image reconstruction algorithms\, and post-processing methods. Their studies span from digital simulations to human subjects research. This lecture will cover recent developments by the Bowen Lab\, including 1) advanced PET data correction methods for low-dose and standalone exams\, 2) non-invasive fully quantitative imaging\, and 3) leveraging topical sensors to detect faulty radiotracer injections. \nBio: Spencer L. Bowen\, Ph.D.\, is an Assistant Professor in the Departments of Radiology and Biomedical Engineering at UT Southwestern Medical Center. He earned his doctorate in biomedical engineering from University of California\, Davis\, where he developed hardware and algorithmic solutions to enable quantification with a breast PET/CT scanner. Dr. Bowen then worked as a research fellow at Massachusetts General Hospital on precision PET imaging methods for combined PET/MR. Prior to joining the UT Southwestern faculty in 2020\, he served as a research assistant professor at the Fralin Biomedical Research Institute at Virginia Tech-Carilion. Dr. Bowen’s research program is funded by both industry and the NIH. His work has been featured on the cover of the Journal of Nuclear Medicine and detailed by the press. \nHosted by: Professor Soumya Bose\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-290-seminar-precision-nuclear-medicine-engineering-solutions-from-acquisition-to-analysis/
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:20260302T123000
DTEND;TZID=America/Los_Angeles:20260302T133000
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CREATED:20260223T222143Z
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SUMMARY:CM Seminar - "From Sibelius to Game: Crafting Adaptive Music for 'Kingdom Come: Deliverance'"
DESCRIPTION:Presented by: Adam Sporka \nDescription: “This talk explores the technical and creative processes behind the music of Kingdom Come: Deliverance II\, where I served as a music programmer\, and soundtrack contributor. Using our proprietary Sequence Music Engine and music logic module\, we authentically scored the game’s 1400s Bohemia setting with segment-based adaptive music driven by in-game variables. Our workflow centers around the notation program Sibelius and our custom tool Converdi\, which streamlines the production by converting the score symbols to preliminary MIDI streams per individual VSTs and by extracting the precise timing data necessary for the segment transitions. This enabled us to spend more time on the creative aspects of music and less time on production and implementation.” \nThe key takeaways from the talk are as follows: \n* Production should start with a complete score\, and not just the MIDI exports therefrom\n* Custom automation tools can streamline the music creation workflow and reduce the production time\n* Resequencing is more suitable for classical and medievalesque music than layering\n* Rapid music prototyping allows for early testing of adaptive music directly in the game \nBio: Adam Sporka is a software developer by trade\, a musician by domain\, and a scientist by approach. As a researcher\, developer\, and educator in game audio\, he places a special focus on interactive music. He has served as the technical music director for Kingdom Come: Deliverance II (Warhorse Studios) and is the author of the Sequence Music Engine\, a proprietary adaptive music middleware used in both games. As a composer\, he contributed to the soundtrack of both Kingdom Come: Deliverance games\, writing some of the most memorable medievalesque and early renaissance pieces on the soundtrack. Currently\, he teaches game audio at the University of California\, Santa Cruz. He received his Ph.D. and habilitation in human-computer interaction from the Czech Technical University (Czech Republic) and was a postdoctoral researcher at the University of Trento (Italy). He has published more than 50 articles in the proceedings of international conferences and academic journals. Adam is currently appointed as a principal engineer at Embody\, a Sunnyvale-based game audio software company focused on the commercial applications of the head-related transfer function. \nHosted by: Professor Christina Chung \nWhen: March 2\, 2026 from 12:30PM to 1:30PM \nLocation:  \nIN-PERSON @  SVC 3212. \nViewing room @ UCSC Main Campus\, E2-280. \nLUNCH WILL BE PROVIDED AT BOTH LOCATIONS! Faculty and students are highly encouraged to attend. \nZoom info: \nhttps://ucsc.zoom.us/j/98763397019?pwd=XUG5pnMjgFgCEOlpunV41oRjNMZiO6.1 \nMeeting ID: 987 6339 7019\nPasscode: 273556
URL:https://events.ucsc.edu/event/cm-seminar-from-sibelius-to-game-crafting-adaptive-music-for-kingdom-come-deliverance/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20260302T160000
DTEND;TZID=America/Los_Angeles:20260302T170000
DTSTAMP:20260418T010612
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SUMMARY:Statistics Seminar: Decoding Phytoplankton Responses to a Changing Ocean
DESCRIPTION:Presenter: Francois Ribalet\, Research Associate Professor\, School of Oceanography\, University of Washington \nDescription: François Ribalet will present new observational technologies and computational approaches for studying phytoplankton responses to ocean warming. Using SeaFlow\, a custom-built automated flow cytometer deployed on over 100 research cruises\, his team has collected nearly 850 billion cell measurements across global oceans. Matrix population models applied to these data reveal how temperature affects phytoplankton division rates and biomass. The research shows that Prochlorococcus\, the ocean’s most abundant photosynthetic organism\, experiences sharp declines in growth above 28°C. Climate projections incorporating these metabolic constraints predict a 40-60% decrease in Prochlorococcus production in tropical regions by 2100\, with Synechococcus partially compensating through a 20-40% increase. These shifts between dominant phytoplankton groups will likely disrupt ocean food webs and carbon cycling\, raising questions about whether tropical ecosystems can adapt to warming oceans. \n\n\n\n\n\n\n\n\n\nBio: François Ribalet is a research associate professor at the University of Washington studying phytoplankton and their role in ocean food webs and carbon cycling. He combines field observations with statistical models to understand how environmental changes affect the growth and community dynamics of these microscopic organisms. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-decoding-phytoplankton-responses-to-a-changing-ocean/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260302T160000
DTEND;TZID=America/Los_Angeles:20260302T170000
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SUMMARY:AM Seminar: The Evolving Landscape of AI for Science and Engineering: Bridging Simulation\, Experiment\, and Multi-scale Dynamics
DESCRIPTION:Presenter: Aditi Krishnapriyan\, Assistant Professor\, UC Berkeley \nDescription: Recent advances in large-scale scientific datasets are creating new opportunities for machine learning (ML) methods to more effectively capture scientific phenomena with greater accuracy and reach. In this talk\, I will discuss how these advances are both shifting ML design paradigms and enabling new scientific inquiries. This includes investigations into understanding if neural networks can autonomously discover fundamental physical relationships from data\, and demonstrating how more flexible machine learning modeling design choices enable capturing physical dynamics across multiple scales. I will also explore how generative modeling approaches rooted in statistical physics can be applied to accelerate the sampling of dynamic pathways\, and as a framework to align and bridge the gap between simulated data and experimental observations. \nBio: Aditi Krishnapriyan is an Assistant Professor at UC Berkeley where she is part of Chemical and Biomolecular Engineering\, Electrical Engineering and Computer Sciences\, and Berkeley AI Research; as well as a faculty scientist in the Applied Mathematics division at Lawrence Berkeley National Laboratory. She holds a PhD from Stanford University\, supported by the DOE Computational Science Graduate Fellowship\, was the Luis W. Alvarez Fellow in Computing Sciences at Lawrence Berkeley National Laboratory\, and is a recipient of the Department of Energy Early Career Award and RCSA Scialog. Her research focuses on developing physics-inspired machine learning methods that bridge machine learning with physical science applications to capture phenomena across multiple length and timescales. \nHosted by: Applied Mathematics
URL:https://events.ucsc.edu/event/am-seminar-the-evolving-landscape-of-ai-for-science-and-engineering-bridging-simulation-experiment-and-multi-scale-dynamics/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260304T110000
DTEND;TZID=America/Los_Angeles:20260304T121500
DTSTAMP:20260418T010612
CREATED:20260217T182353Z
LAST-MODIFIED:20260217T182353Z
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SUMMARY:CSE Colloquium - Improving Efficiency and Reliability of Foundation Models in Clinical AI
DESCRIPTION:Presenter: Vasiliki “Vicky” Bikia\, PhD\, Stanford Department of Biomedical Data Science and Institute for Human-Centered AI (HAI) \nAbstract: \nDeploying foundation models in health requires both computational efficiency and reliable generation. In this talk\, I present two studies that address these dimensions separately but with a shared goal of real-world clinical deployment. The first study focuses on reduced-resolution distillation for multimodal clinical data\, particularly medical imaging. As model and input sizes increase\, inference cost and memory constraints become major barriers to deployment. We investigate how high-capacity teacher models can transfer structured knowledge to compact student models trained on downsampled images\, using embedding-space supervision to preserve clinically meaningful representations while reducing computational footprint. The second study examines the reliability of AI-generated clinical text. Foundation models are increasingly used to produce discharge summaries and patient-facing explanations\, yet fluency does not guarantee safety. We develop a structured evaluation framework grounded in clinical error taxonomies and clinician-calibrated metrics to quantify hallucinations\, omissions\, and semantic misalignment. Together\, these studies emphasize that scalable clinical AI requires not only smaller and faster models\, but also rigorous evaluation of generative reliability before deployment. \nBio: \nVasiliki Bikia is a Postdoctoral Researcher at Stanford University\, affiliated with the Department of Biomedical Data Science and the Stanford Institute for Human-Centered Artificial Intelligence (HAI). She received an Advanced Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki\, and a Ph.D. in Bioengineering from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her research focuses on medical foundation models\, structured representations of health data\, and the evaluation of generative systems in clinical settings. Previously\, she was a Machine Learning Scientist at the Mussallem Center for Biodesign at Stanford University\, where she developed software pipelines to improve data accessibility and interoperability in digital health applications. Vasiliki was selected as an MIT Rising Star in EECS (2025) and as an Emerson Consequential Scholar (2025)\, and is actively engaged with the Silicon Valley entrepreneurial ecosystem through collaborations at the intersection of research\, industry\, and healthcare. She is an organizing member of the Conference on Health\, Inference\, and Learning (CHIL) and serves as Unconference Chair for the 2025 and 2026 editions\, where she leads the design and execution of the entrepreneurship-focused track bridging academic research and real-world deployment. Her work has appeared in venues including IEEE journals\, npj Digital Medicine\, Nature Communications\, and leading AI conferences\, and she has contributed to multiple funded research proposals and clinical studies at the intersection of AI\, medicine\, and translational impact. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, E2-180 (*Refreshments such as coffee\, tea\, fresh fruit\, and pastries will be provided) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-improving-efficiency-and-reliability-of-foundation-models-in-clinical-ai/
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:20260305T114000
DTEND;TZID=America/Los_Angeles:20260305T131500
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SUMMARY:BME 280B Seminar: Artificial intelligence systems to advance engineered T cell immunotherapy designs
DESCRIPTION:Presenter: Zinaida Good\, Assistant Professor of Medicine in the Division of Immunology and Rheumatology and the Division of Computational Medicine\, Stanford University \nDescription: T cell immunotherapies have reshaped the treatment landscape for hematologic malignancies and are rapidly extending to solid tumors\, autoimmune diseases\, and transplant tolerance. Yet durable benefit remains inconsistent\, and toxicities remain clinically significant. The current discovery proceeds one edit at a time\, and existing preclinical models do not represent patient biology\, which often results in failure upon clinical translation. Overcoming these challenges to improve patient outcomes and reduce toxicities requires a systems-level understanding of the multiscale factors governing T cell function and toxicity in patients. Artificial intelligence (AI) approaches offer an exciting opportunity to tackle this problem by learning unified representations from diverse data types spanning molecular\, cellular\, and clinical modalities. I will provide an overview on our team’s approaches building AI systems that harness primary patient datasets to directly inform advanced T cell designs optimized for clinical outcomes\, with validation in preclinical models. \nBio: Zinaida Good\, Ph.D.\, is an Assistant Professor of Medicine in the Division of Immunology and Rheumatology and the Division of Computational Medicine at Stanford University. She also serves as the Director of the Stanford Center for Cancer Cell Therapy Data Hub. The goal of her research program is to understand and enhance engineered T cell immunotherapies for cancer and immune-mediated diseases through innovative computational approaches and systems immunology. Her lab leverages innovation in machine learning and clinical multiomic datasets to build artificial intelligence systems for advanced T cell therapy design. Dr. Good earned her Ph.D. in Computational & Systems Immunology from Stanford University. Her work includes 4 first-author papers (Nature Medicine 2018 & 2022\, Nature Biotechnology 2019\, Trends in Immunology 2019)\, 18+ co-authored papers (including Nature 2019\, 2022\, 2024\, Science 2021\, Nature Methods 2016\, 2022\, and NEJM 2024)\, and an initial senior author papers (ICML 2025\, NeurIPS 2025\, Frontiers in Immunology 2025). Her research is supported by the NIH/NCI Pathway to Independence Award\, NIH/OD Multimodal AI Initiative Award\, NIH/NCI Program Project Grant\, and the Weill Cancer Hub West. Dr. Good has been named an Arthur & Sandra Irving Cancer Immunology Fellow in 2022\, Parker Bridge Fellow in 2023\, and an AACR-Woman in Cancer Research Scholar in 2024. \nHosted by: Professor Vanessa Jonsson\, BMEbe Department
URL:https://events.ucsc.edu/event/bme-280b-seminar-artificial-intelligence-systems-to-advance-engineered-t-cell-immunotherapy-designs/
LOCATION:Biomedical Sciences\, Biomedical Sciences Building Red Hill Road\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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