Audience: Prospective Students
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Wang, H. (CSE) – Accelerating RTL Simulation with Specialized Graph Partitioners
Register transfer level (RTL) simulation is an invaluable tool for developing, debugging, verifying, and validating hardware designs. However, the performance of RTL simulation has long been a limiting factor in industry. Despite the inherent parallelism of hardware, current RTL simulators have not achieved practical performance gains due to fundamental challenges in communication, synchronization, memory bandwidth,…
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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 – but when we try to define it or implement it, we seldom get the same definition twice. Balance appears differently to whoever is judging it,…
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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 them. This dissertation investigates how to build human-centered multimodal AI agents, framing human-centeredness as an agent-level objective that requires both accessible, assistive interaction and reliable,…
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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 difficult because data come from different scanners and sites, some modalities are missing, labeled cohorts are limited, and factors such as age and scanner/site effects…
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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 memory architecture. Although these shared-memory SoCs enable more efficient communication and data sharing between different processing units, they are notoriously difficult to program and tune…
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2026 Right Livelihood International Conference
The Right Livelihood International Conference is a five-week global conference exploring how education can strengthen democracy, collective intelligence, and just futures. Bringing together Right Livelihood Laureates, students, faculty, and community partners across continents, the conference combines asynchronous learning with participatory dialogue and collaborative action. Rather than advocating specific outcomes, the conference positions education as a democratic…
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What’s new in AI?
Lead innovation as a machine learning engineer Want to learn what’s new in AI? Join Praveen Krishna, chair of the Artificial Intelligence Application Development certificate program, in an informal discussion about the AI topic of the month and an open Q&A. You’ll get an insider’s look at what you need to know for where you…
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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, and political economic processes that have set the stage for contemporary wildfires, in what has become known as the “Wildland Urban Interface” (WUI). Come and…
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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 it scales poorly, can degrade utility, and can fail under adversarial prompting that recovers supposedly forgotten content. This prospectus proposes inference-time behavioral unlearning: rather than…
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Fredrickson, K. (CSE) – Practical Anonymity with Formal Resistance to Traffic Analysis
Anonymous communication systems hide who is talking to whom, not just what is said. However, existing systems are either vulnerable to traffic analysis attacks–attacks where adversaries observe and correlate the network traffic of users–or are forced to rely on unrealistic and unenforceable assumptions about how users behave. Worse, existing theory lacks tools to rigorously model…