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DTSTART;TZID=America/Los_Angeles:20260709T133000
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DTSTAMP:20260630T090032
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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-2/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260710T110000
DTEND;TZID=America/Los_Angeles:20260710T123000
DTSTAMP:20260630T090032
CREATED:20260626T170310Z
LAST-MODIFIED:20260626T170310Z
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SUMMARY:Levine\, R. (CSE) - Validating GPU Memory Consistency and Safety at Scale
DESCRIPTION:Graphics Processing Units (GPUs) have become essential platforms for parallel computing\, supporting applications far beyond graphics. Central to GPU programming models is its memory consistency specification (MCS)\, which defines the semantics of concurrent shared-memory operations and interacts with other language features to determine security guarantees such as memory safety. Understanding whether implementations conform to an MCS\, and whether the MCS provides a sound abstraction of real hardware\, is essential for reasoning about GPU programs and validating implementations. \nThis thesis develops techniques and large-scale studies for validating GPU memory consistency and memory safety. First\, it introduces MC Mutants\, a mutation testing methodology that systematically evaluates GPU MCS test environments. Applied to WebGPU\, MC Mutants generates a suite of conformance tests and uncovers two implementation bugs. Next\, it presents GPUHarbor\, a browser- and Android-based framework for large-scale testing across commodity GPUs. GPUHarbor enables a study of 106 GPUs from seven vendors\, reveals two previously unknown memory consistency bugs\, and provides new insights into GPU behavior that inform subsequent architectural and security studies. Finally\, this thesis presents SafeRace\, a collection of security assessments and specification proposals for preserving WebGPU memory safety in the presence of data races. Evaluated across dozens of GPUs and 21 WebGPU compilation stacks\, SafeRace identifies vulnerabilities in multiple GPU implementations\, including one assigned a CVE\, and proposes a validated path toward stronger memory safety guarantees in WebGPU. \nEvent Host: Reese Levine\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Tyler Sorensen \nZoom: https://ucsc.zoom.us/j/94641390195?pwd=RWXp9aprCMqmaAo8nq7oKwqTt02zwN.1 \nPasscode: 628349
URL:https://events.ucsc.edu/event/levine-r-cse-validating-gpu-memory-consistency-and-safety-at-scale/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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