Audience: Undergraduate Students
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Shields, S. (CM) – Procedural, Player-Centric Game Balancing
Game balance is a term widely used among players, researchers, and designers of games. It is a concept that feels vitally important to how we make and play games – […]
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Fan, Y. (CSE) – Building Human-Centered Multimodal AI Agents
As multimodal artificial intelligence systems become increasingly embedded in everyday technology, there is a growing need to design human-centered AI agents that support and amplify human capabilities rather than replace […]
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Mashhadi, N. (CSE) – Compositional, Clinically Conditioned, and Confound-Aware Deep Learning for Alzheimer’s Disease Neuroimaging
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia. Neuroimaging and clinical biomarkers can reveal early disease changes, but building reliable machine learning models is […]
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Xu, Y. (CSE) – Right Place, Right Time: Accelerating Edge Computation on Modern Heterogeneous SoCs
Modern edge computing increasingly relies on heterogeneous System-on-Chip (SoC) architectures. These chips tightly integrate general-purpose CPUs with various specialized accelerators, including GPUs, FPGAs, and AI accelerators, all under a shared […]
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CSE Colloquium – Improving Efficiency and Reliability of Foundation Models in Clinical AI
Presenter: Vasiliki “Vicky” Bikia, PhD, Stanford Department of Biomedical Data Science and Institute for Human-Centered AI (HAI) Abstract: Deploying foundation models in health requires both computational efficiency and reliable generation. […]
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Unexpected Returns: The Historic Entanglements of Fire, Settlement, and Stewardship in the Santa Cruz Mountains
March 4th, 2026 from 6:00 p.m. – 7:30 p.m. Miriam Greenberg and Andrew Matthews will present the findings of UCSC researchers who have spent three years studying the ecological, social, […]
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Liu, C. (CSE) – Enabling LLM Unlearning at Inference Time by Decomposing Detection and Intervention
Machine unlearning addresses the “right to be forgotten” under GDPR and enables privacy, copyright, and safety compliance in large language models. Training-based unlearning can remove targeted behavior on benchmarks, but […]


