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  • Three silhouetted figures talking, overlaid with graphics of digital data, charts, and technology interfaces.

    Chen, Z. (CSE) – GPU Subgroup Semantics for Portable High-Performance Kernels

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

    Modern high-performance GPU kernels increasingly rely on subgroup-level execution, including subgroup-level communication, subgroup operations, and matrix operations. These features are essential for workloads such as matrix multiplication and FlashAttention, but their language-level guarantees remain difficult to reason about. Existing programming models often leave unclear which threads participate in subgroup operations, when subgroup threads are required […]

  • Three silhouetted figures talking, overlaid with graphics of digital data, charts, and technology interfaces.

    Shen, G. (CSE) – Library-Level Choreographic Programming

    Hybrid Event

    Modern software increasingly relies on distributed systems to provide accessible, scalable, and reliable services. Choreographic programming brings a global perspective to distributed system development: programmers write a single program that describes the behavior of a whole system, and a compiler projects that global description into local programs run by each node. By making distributed control […]

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    Kim, C. (CSE)- Toward Adaptive Graph Processing and Fault-Tolerant Agentic Inference on Heterogeneous Distributed Systems

    Hybrid Event

    Edge computing and distributed AI systems increasingly operate under heterogeneous resources, dynamic workloads, and frequent failures, requiring both adaptivity and fault tolerance for efficient execution. In heterogeneous edge clusters, nodes differ significantly in CPU throughput, memory capacity, and network bandwidth, while modern distributed GPU clusters supporting agentic LLM inference must recover large amounts of runtime […]

  • Close-up abstract image of a circuit board with glowing lines and interconnected pathways.

    Carrión, H. (CSE) – Deep Learning Algorithms for Medical Image Representation Learning and Understanding

    Virtual Event

    AI-assisted clinical decisions in medicine, and particularly in dermatology, demand fine-grained understanding across diverse skin tones, body sites, and disease types, yet expert-annotated datasets are scarce, demographically imbalanced, and almost devoid of rare presentations. This dissertation develops four deep learning systems for this low-label, low-coverage regime. We introduce HealNet, which learns wound healing stages from […]