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DTSTART;TZID=America/Los_Angeles:20260615T130000
DTEND;TZID=America/Los_Angeles:20260615T150000
DTSTAMP:20260612T104839
CREATED:20260609T215214Z
LAST-MODIFIED:20260609T215214Z
UID:10014915-1781528400-1781535600@events.ucsc.edu
SUMMARY:Tang\, M. (STAT) - Bayesian Modeling and Scalable Inference for Count Time Series in Infectious Disease Surveillance
DESCRIPTION:Real-time monitoring of infectious disease outbreaks calls for statistical models that recover interpretable quantities such as the time-varying reproduction number from noisy count data\, track posterior uncertainty\, and run on time scales compatible with daily updates. Existing methods address these aims through separate model classes. Discretized Hawkes processes\, Poisson autoregressions\, and distributed lag models each capture self-exciting transmission through alternative parameterizations of the same conditional mean structure\, but they have been developed across separate software packages with model-specific inference routines\, which makes structural model comparison cumbersome in practice. This dissertation develops a unified Bayesian framework for count time series in disease surveillance\, organized around three threads. First\, a class of dynamic generalized transfer function models places the three modeling families inside a common modular state-space class built from six independent components. A hybrid variational algorithm combines sequential Monte Carlo on the latent trajectory with stochastic gradient ascent on the static parameters. Second\, a multivariate extension to spatially connected regions\, a Bayesian network Hawkes model\, jointly estimates time-varying source-specific reproduction numbers and a sparse transmission network learned from data through a regularized horseshoe prior. The observed reproduction number at each\nlocation is decomposed into a local component and an imported component. Posterior inference proceeds through a blocked Markov chain Monte Carlo sampler\, with a particle Laplace variational counterpart developed for routine refits at larger spatial scales. Third\, an R package implements the unified univariate framework through a compositional specification interface aligned with the six modular components\, with the two inference engines available behind a single entry point. The methods are illustrated through simulation studies and applications to daily COVID-19 case counts from Santa Cruz County and from ten California counties. \nEvent Host: Meini Tang\, Ph.D. Candidate\, Statistical Science  \nAdvisor: Raquel Prado \nZoom: https://ucsc.zoom.us/j/97990210796?pwd=e59WbsNrYgYSITmMw0OIT5f1SQThEN.1 \nPasscode:  479460
URL:https://events.ucsc.edu/event/tang-m-stat-bayesian-modeling-and-scalable-inference-for-count-time-series-in-infectious-disease-surveillance/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260618T100000
DTEND;TZID=America/Los_Angeles:20260618T120000
DTSTAMP:20260612T104839
CREATED:20260526T162714Z
LAST-MODIFIED:20260526T162714Z
UID:10014867-1781776800-1781784000@events.ucsc.edu
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/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260618T100000
DTEND;TZID=America/Los_Angeles:20260618T120000
DTSTAMP:20260612T104839
CREATED:20260609T193755Z
LAST-MODIFIED:20260609T193755Z
UID:10014912-1781776800-1781784000@events.ucsc.edu
SUMMARY:Wang\, Z. (CSE) - From Static Alignment to Adaptive Safety: Toward Reliable and Capable AI Systems
DESCRIPTION:Modern AI systems are rapidly moving beyond static text generation toward capable models and agents that reason\, use tools\, store memories\, and update persistent state\, yet safety methods still often assume a fixed model whose behavior can be controlled by output-level refusal. This leaves critical gaps in understanding why aligned models fail under adversarial pressure\, how to align reasoning models without suppressing their useful capabilities\, and how to preserve safety once capability and control are externalized into editable agent state. My research proposes a static-to-adaptive safety framework for building reliable and capable AI systems: studying the mechanisms that shape behavior inside models\, using reasoning capability as a substrate for safety alignment\, and governing persistent state as agents learn and adapt over time. We instantiate this agenda through two completed works and three proposed directions. AttnGCG studies adversarial failures in aligned language models\, showing how jailbreak attacks can manipulate model attention and expose limitations of output-level safety analysis. STAR-1 studies safety alignment for large reasoning models\, showing that policy-grounded reasoning data can improve safety while largely preserving general reasoning capability. Building on these foundations\, we further study when editable agent harnesses meaningfully affect future behavior\, how persistent state creates new safety risks\, and how adaptive agents can safely update state while preserving useful learning. Together\, my research aims to move beyond static alignment alone\, toward AI systems whose safety remains reliable as their capabilities expand through reasoning and adaptation. \nEvent Host: Zijun Wang\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Cihang Xie  \nZoom ID:  962 8317 0929 \nPasscode: 687715
URL:https://events.ucsc.edu/event/wang-z-cse-from-static-alignment-to-adaptive-safety-toward-reliable-and-capable-ai-systems/
LOCATION:
CATEGORIES:Ph.D. Presentations
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