• Harrison, D. (CS) – Multi-Level Control in Neural Dialogue Generation: Style, Semantics, and Selection through Over-Generation and Ranking

    End-to-end neural generation models have largely displaced the modular architectures that once gave dialogue system designers explicit control over what is said and how it is said. While these models produce fluent text, they collapse content planning, sentence planning, and surface realization into a single undifferentiated decoding step, sacrificing the controllable structure that earlier systems […]

  • Mashhadi, N. (CSE) – Compositional, Clinically Conditioned, and Confound-Aware Deep Learning for Alzheimer’s Disease Neuroimaging

    Virtual Event

    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 […]

  • CSE Colloquium: Co-Active AI-Assisted Programming

    Engineering 2 Engineering 2 1156 High Street, Santa Cruz, CA

    Presenter: Nadia Polikarpova, UCSD Abstract: AI-assisted programming has rapidly moved from novelty to default. Today, most developers use AI coding tools, and increasingly rely on agentic systems capable of making multi-step design and implementation decisions with minimal human guidance. While these systems boost productivity, they also introduce new risks: developers may disengage from the reasoning behind […]

    Free
  • Fan, Y. (CSE) – Building Human-Centered Multimodal AI Agents

    Virtual Event

    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, […]

  • Moghadam, M. (CE) – Constraint-Aware Scene Understanding and Trajectory Generation Using Deep Reinforcement Learning for Autonomous Vehicles

    Engineering 2 Engineering 2 1156 High Street, Santa Cruz, CA
    Hybrid Event

    Advanced driver-assistance systems (ADAS) are commonly organized as modular pipelines that transform raw sensor measurements into low-level actuation commands through perception, planning, and control. While learning-based methods have achieved state-of-the-art performance in perception and environment modeling, the planning layer remains a key bottleneck for reliable autonomy. Highway driving in particular requires long-horizon reasoning and socially […]

  • Wang, H. (CSE) – Accelerating RTL Simulation with Specialized Graph Partitioners

    Jack Baskin Engineering Baskin Engineering 1156 High Street, Santa Cruz, CA

    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, […]