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DTSTART;TZID=America/Los_Angeles:20260609T103000
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DTSTAMP:20260602T021519
CREATED:20260526T194326Z
LAST-MODIFIED:20260526T194445Z
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SUMMARY:Shen\, G. (CSE) - Library-Level Choreographic Programming
DESCRIPTION:Modern software increasingly relies on distributed systems to provide accessible\, scalable\,\nand reliable services. Choreographic programming brings a global perspective to distributed\nsystem development: programmers write a single program that describes the behavior of a\nwhole system\, and a compiler projects that global description into local programs run by each\nnode. By making distributed control flow explicit\, choreographic programming can rule out\nimportant classes of errors\, including deadlocks. This dissertation investigates library-level\nchoreographic programming\, an approach that embeds choreographic abstractions in existing\nhost languages rather than implementing them as standalone languages. The central claim\nis that the library approach can retain the safety and global reasoning principles of chore-\nographic programming while taking advantage of the host language’s features\, tools\, and\necosystem. First\, we present HasChor\, a first-of-its-kind library-level choreographic program-\nming language in Haskell\, built using freer monads. Next\, we generalize the design underlying\nHasChor to algebraic effects\, giving library-level implementations in Agda and OCaml. Fi-\nnally\, we present Parkour\, a backward-compatible extension to HasChor that adds a construct\nfor expressing parallel behavior in choreographies. Together\, these systems show that chore-\nographic programming can be implemented\, generalized\, and extended at the library level\,\nmaking global programming techniques available within practical host-language settings. \nEvent Host: Gan Shen\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Lindsey Kuper  \nZoom: https://ucsc.zoom.us/j/93790633483?pwd=Jg8JlISsrwjLBaQIi1KdHk36bNMIv7.1 \nPasscode: 902041 \n 
URL:https://events.ucsc.edu/event/shen-g-cse-library-level-choreographic-programming/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260609T120000
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SUMMARY:Kim\, C. (CSE)- Toward Adaptive Graph Processing and Fault-Tolerant Agentic Inference on Heterogeneous Distributed Systems
DESCRIPTION: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 state under routine failures. This dissertation addresses these challenges through two systems: Zsiga\, an adaptive distributed graph processing system for heterogeneous edge clusters\, and Forte\, a fault-tolerant KV cache recovery system for distributed agentic LLM inference. \nZsiga improves connected component computation through capacity-aware graph partitioning and runtime-adaptive boundary migration\, reducing execution time by up to 90.9% while eliminating out-of-memory failures under heterogeneous resource constraints. Forte addresses KV cache recovery for long-running agentic inference workloads\, where failures can erase accumulated reasoning trajectories and tool interaction histories. Forte exploits the observation that not all KV blocks are equally critical\, introducing criticality-aware erasure coding\, domain-diverse placement\, and prioritized foreground recovery to enable efficient recovery under correlated failures. Experimental results show that Forte is the only evaluated scheme that successfully resumes execution under correlated domain failures\, reducing foreground stall by 89.7% and end-to-end recovery latency by 50.6–58.9% at 2.0$\times$ memory overhead. Together\, these systems demonstrate how adaptivity and fault tolerance can improve the efficiency and resilience of distributed systems in heterogeneous and failure-prone environments. \nEvent Host: Chaeeun Kim\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Chen Qian & Liting Hu \nZoom: https://ucsc.zoom.us/j/9863615188?pwd=kTka0aZXJ070tor1EKvrt3X6AveBRp.1 \nPasscode:  cG5SL8 \n  \n 
URL:https://events.ucsc.edu/event/kim-c-cse-toward-adaptive-graph-processing-and-fault-tolerant-agentic-inference-on-heterogeneous-distributed-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260618T100000
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SUMMARY:Carrión\, H. (CSE) - Deep Learning Algorithms for Medical Image Representation Learning and Understanding
DESCRIPTION: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 longitudinal photographs without any human labels\, reaching 90.6% downstream stage-classification accuracy on a small longitudinal cohort. The Fair\, Efficient\, and Diverse Diffusion (FEDD) model then leverages powerful diffusion-model embeddings to build a skin-tone-fair\, data-efficient classifier for skin lesions\, matching or exceeding state-of-the-art performance while using only 5-20% of available labels and contributing explicit skin-tone-stratified fairness evaluation of the work. Next\, Controllable Generation of Diverse Dermatological Imagery (cgDDI) re-tasks this diffusion model to controllably synthesize skin-tone-balanced dermatological imagery\, growing a small biopsy-confirmed dataset by over 400x and reaching state-of-the-art 90.9% accuracy and improved fairness in malignancy classification\, with a +13.9% cross-dataset gain on the Fitzpatrick17k benchmark. Finally\, we introduce D-Synth and DermDepth: a synthetic dermoscopic dataset with pixel-perfect 3D ground truth and a metric-scale foundation model that closes the loop into 3D dermatology\, correcting metric scale error from over 16x to under 1.1x on real dermoscopic data and enabling single-photograph measurement of lesion reconstruction: size\, area\, and volume without specialized hardware. All data\, code\, and models are released openly to support reproducibility and ongoing fairness research. \nEvent Host:  Héctor Carrión\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Narges Norouzi \nZoom: https://ucsc.zoom.us/j/96678782408?pwd=71f0ObEnUMNgkZ9NYnpbFLMlg1Pdm0.1 \nPasscode: 0FMVtz
URL:https://events.ucsc.edu/event/carrion-h-cse-deep-learning-algorithms-for-medical-image-representation-learning-and-understanding/
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CATEGORIES:Ph.D. Presentations
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