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DTSTART;TZID=America/Los_Angeles:20260609T103000
DTEND;TZID=America/Los_Angeles:20260609T130000
DTSTAMP:20260526T194445Z
CREATED:20260526T194326Z
LAST-MODIFIED:20260526T194445Z
UID:10014873-1781001000-1781010000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260609T120000
DTEND;TZID=America/Los_Angeles:20260609T130000
DTSTAMP:20260526T161617Z
CREATED:20260526T161617Z
LAST-MODIFIED:20260526T161617Z
UID:10014865-1781006400-1781010000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260615T130000
DTEND;TZID=America/Los_Angeles:20260615T150000
DTSTAMP:20260609T215214Z
CREATED:20260609T215214Z
LAST-MODIFIED:20260609T215214Z
UID:10014915-1781528400-1781535600@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260618T100000
DTEND;TZID=America/Los_Angeles:20260618T120000
DTSTAMP:20260526T162714Z
CREATED:20260526T162714Z
LAST-MODIFIED:20260526T162714Z
UID:10014867-1781776800-1781784000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/carrion-h-cse-deep-learning-algorithms-for-medical-image-representation-learning-and-understanding/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260618T100000
DTEND;TZID=America/Los_Angeles:20260618T120000
DTSTAMP:20260609T193755Z
CREATED:20260609T193755Z
LAST-MODIFIED:20260609T193755Z
UID:10014912-1781776800-1781784000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/wang-z-cse-from-static-alignment-to-adaptive-safety-toward-reliable-and-capable-ai-systems/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260625T140000
DTEND;TZID=America/Los_Angeles:20260625T160000
DTSTAMP:20260625T183144Z
CREATED:20260625T183144Z
LAST-MODIFIED:20260625T183144Z
UID:10014992-1782396000-1782403200@live-events-ucsc.pantheonsite.io
SUMMARY:Burbano\, L. (CS) - Security of autonomous decision-making agents: From control systems to embodied AI
DESCRIPTION:Due to their increasing complexity\, autonomous decision-making agents rely on increasingly advanced algorithms\, from classical control theory to reinforcement learning (RL) and\, more recently\, large vision-language models. While these algorithms help automate the decision-making in complex systems\, they bring newer attack vulnerabilities that an adversary can exploit. In this dissertation\, we study the security of autonomous decision agents that use control systems\, RL\, and AI. We focus on the security of cyber-physical and autonomous cyber-defense systems. In particular\, we study how an attacker can compromise decision-making agents. \nFor control systems\, this dissertation studies the existence of backdoor attacks against control systems that rely on data and proposes a defense strategy against the sensors of control systems. \nFor reinforcement learning\, we study the security of autonomous cyber-defense (ACD)) agents that automatically respond to attackers’ actions in a network. While previous works focus on creating agents\, we study an adversary who compromises the agent’s own infrastructure\, manipulating the information it observes to steer the network toward an attacker-chosen state. We also propose a defense strategy that focuses on determining if an attacker is compromising the ACD. \nFinally\, we study the security of embodied AI\, where CPS rely on large vision-language models (LVLMs) for decision-making. We propose a novel attack that can cause an agent to make unsafe decisions by presenting a well-designed textual sign via the visual modality. While previous attacks against neural network-based algorithms rely on creating adversarial patches without semantic meaning\, in this work\, we exploit the fact that LVLMs can understand text. \n  \nEvent Host: Luis Burbano\, Ph.D. Candidate\, Computer Science  \nAdvisor: Alvaro Cardenas \nZoom: https://ucsc.zoom.us/j/92373119649?pwd=BLFQMrGkOxJVXnjrJhXqudN1iciZAn.1 \nPasscode: 160434\n   
URL:https://live-events-ucsc.pantheonsite.io/event/burbano-l-cs-security-of-autonomous-decision-making-agents-from-control-systems-to-embodied-ai/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260709T133000
DTEND;TZID=America/Los_Angeles:20260709T153000
DTSTAMP:20260623T160412Z
CREATED:20260623T160248Z
LAST-MODIFIED:20260623T160412Z
UID:10014929-1783603800-1783611000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/carrion-h-cse-deep-learning-algorithms-for-medical-image-representation-learning-and-understanding-2/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260710T110000
DTEND;TZID=America/Los_Angeles:20260710T123000
DTSTAMP:20260626T170310Z
CREATED:20260626T170310Z
LAST-MODIFIED:20260626T170310Z
UID:10014993-1783681200-1783686600@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260713T100000
DTEND;TZID=America/Los_Angeles:20260713T120000
DTSTAMP:20260707T160215Z
CREATED:20260707T160215Z
LAST-MODIFIED:20260707T160215Z
UID:10015010-1783936800-1783944000@live-events-ucsc.pantheonsite.io
SUMMARY:Scott\, J. (CSE) - Mechanistic Specialization Does Not Guarantee Performance: Evidence from Dual AttentionTransformers
DESCRIPTION:Dual Attention Transformers (DATs) extend decoder-only Transformers with a dedicated relational-attention stream\, making them a natural architecture for abstract identity rules such asABA and ABB. Surprisingly\, we find that comparably sized GPT-2 models outperform DATs on these tasks. We investigate this gap with two complementary mechanistic analyses. First\, causal mediation analysis shows that DATs exhibit stronger evidence of hypothesized symbolic mechanisms: symbol abstraction\, symbol induction\, and retrieval\, than GPT-2. Second\, a routing analysis shows why this specialization does not translate into better behavior: DATs make more wrong-copy errors\, can attend to the correct source token while still predicting the wrong token\, and show weak direct contribution from relational attention to the correct-versus-wrong outputmargin. Ablating positive-routing heads hurts performance\, while amplifying those headsimproves DAT more than matched controls. These results show that explicit relational attentioncan shape internal organization without guaranteeing task success. For identity-rule tasks\, performance depends not only on whether relational information is represented\, but whether it is routed to the final output position in a form that affects the next-token prediction. Because pretrained DAT and GPT-2 models differ in training data\, tokenizer\, and other implementation details\, these findings should be interpreted as evidence about the mechanisms used by existing models rather than as a definitive architectural comparison. Follow-up experiments will address these confounders through controlled training comparisons that match data\, scale\, and evaluation conditions across architectures. \nEvent Host: Jonathan Scott\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Leilani Gilpin \nZoom: https://ucsc.zoom.us/j/95404396322?pwd=0e0AegKSxhcFDDKrn08muHcqfHs6WW.1 \nPasscode: 985103
URL:https://live-events-ucsc.pantheonsite.io/event/scott-j-cse-mechanistic-specialization-does-not-guarantee-performance-evidence-from-dual-attentiontransformers/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260713T160000
DTEND;TZID=America/Los_Angeles:20260713T170000
DTSTAMP:20260708T155209Z
CREATED:20260708T155209Z
LAST-MODIFIED:20260708T155209Z
UID:10015011-1783958400-1783962000@live-events-ucsc.pantheonsite.io
SUMMARY:Kembay\, A. (ECE) - Sparse and Continual Foundations for Adaptive General Intelligence
DESCRIPTION:While the human brain learns continually\, mastering new tasks without forgetting\nthe old and adapting to unfamiliar ones from context alone\, modern neural networks\nstill lack both. To bridge the gap between biological adaptivity and modern AI\, we\nhave established foundational work on sparsity as a computational principle at three\nlevels of neural computation\, through salient feature masking that distills only the most\ninformative knowledge from a teacher\, quantized spiking neural networks whose sparse\nactivations mitigate catastrophic forgetting by updating weights only when new learn-\ning requires it\, and complex-pole value-path dynamics that give Transformer attention\na resonant\, positionally selective memory. Addressing the remaining bottleneck\, that\nthese sparse structures are fixed in advance rather than adapted to the task at hand\,\nwe propose a research roadmap centered on in-context meta-learning with sparse atten-\ntion priors\, enabling models to ‘learn to be sparse’ by inferring task-relevant structure\nfrom context alone\, without any weight update. Taken together\, this research seeks\nto unify brain-inspired sparsity with continual and in-context learning as a foundation\nfor adaptive general intelligence. \nEvent Host: Assel Kembay\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/92202931005?pwd=peVIc4e03fUPwFqlGa6yWx6ZlL33lI.1 \nPasscode: 742766
URL:https://live-events-ucsc.pantheonsite.io/event/kembay-a-ece-sparse-and-continual-foundations-for-adaptive-general-intelligence/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260717T113000
DTEND;TZID=America/Los_Angeles:20260717T133000
DTSTAMP:20260715T163613Z
CREATED:20260715T163517Z
LAST-MODIFIED:20260715T163613Z
UID:10015090-1784287800-1784295000@live-events-ucsc.pantheonsite.io
SUMMARY:Calicchio\, A. (BMEB) - Comparison of long-read sequencing and analysis methods for transcriptome analysis
DESCRIPTION:Alternative splicing\, the process generating different RNA isoforms from a single gene\, is considered one of the main factors driving increased organism complexity in eukaryotes. Variations in isoform and gene expression produce the functional differences that give rise to different cell types and\, in some cases\, result in disease. Long-read RNA sequencing has transformed our ability to characterize isoforms\, since single reads can span full-length transcripts\, but limitations still prevent our identification of all the isoforms in the human transcriptome. Our research proposes to improve both the library preparation and computational analysis steps of the isoform identification process.\nTo do so\, we are updating the isoform identification and quantification tool IG28 (previously called Mandalorion) so that it can analyse both bulk and single-cell long-read sequencing data and. By pairing our analysis with single-cell clustering in Seurat\, we can generate transcriptomes for hundreds of thousands of single cells\, for individual cell types\, and for bulk datasets containing hundreds of millions of reads\, providing a scalable approach to identify isoforms in the largest and most recent datasets.\nFurthermore\, since long reads can carry both the variants defining an allele of origin and the full isoform structure\, we plan to extend IG28 to perform allele-specific transcript usage analysis. We plan to include accurate statistical tests in this module by using beta-binomial and Dirichlet-multinomial models that account for overdispersion\, to provide a tested and integrated pipeline for isoform allelic assignment.\nFinally\, recognizing that isoform detection depends on the quality\, length\, and throughput of the input data\, we are improving library preparation and benchmarking sequencing technologies. We are refining the R2C2 protocol coupled with size selection to overcome the current circularization limit for fragments beyond 6 kb\, and we are generating matched datasets to compare R2C2 to the Kinnex library preparation method\, and ONT against PacBio HiFi sequencing\, to determine which approaches produce the most accurate and longest reads for isoform identification.\nTogether\, these advances will provide a competitive pipeline\, from cDNA preparation to isoform identification and annotation\, enabling accurate isoform annotations that can lead to a deeper understanding of cell differentiation and disease etiology. \nEvent Host: Alessandro Calicchio\, Ph.D. Student\, Biomolecular Engineering & Bioinformatics \nAdvisor: Christopher Vollmers \nZoom: https://ucsc.zoom.us/j/92704819548?pwd=PUqQpq0Soandz8E5DIPCXFdvnFaf00.1 \nPasscode: 760165
URL:https://live-events-ucsc.pantheonsite.io/event/calicchio-a-bmeb-comparison-of-long-read-sequencing-and-analysis-methods-for-transcriptome-analysis/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260722T110000
DTEND;TZID=America/Los_Angeles:20260722T130000
DTSTAMP:20260708T160702Z
CREATED:20260708T160702Z
LAST-MODIFIED:20260708T160702Z
UID:10015012-1784718000-1784725200@live-events-ucsc.pantheonsite.io
SUMMARY:Holmes\, J. (CM) - Towards a Multi-dimensional Model of User Load
DESCRIPTION:Games user researchers (GURs) use various methods to understand when a game is overloading its players. In games research where data-driven multimodal approaches are necessary to drive insights\, the currently available tools to measure user load are coarse\, one-dimensional\, and often aggregated. The more dominant instruments\, such as the Cognitive Load Scale (CLS) and the NASA-TLX\, rely on player reflections of mental effort\, primarily at the end of the playtest session\, to distinguish different cognitive load types. This makes it difficult to: (1) understand where specifically players are struggling and experiencing high load\, especially at the non-reflective subconscious level\, (2) identify where that load is primarily coming from (e.g.\, perceptual clutter or difficulty/skill imbalance)\, and (3) examine user overload at scale\, a crucial component of designing a game with large player bases. Telemetry is the behavioral record of what players are doing from moment to moment\, in varying degrees of granularity. Telemetry has served as a powerful tool to understand player behaviors at scale\, yet is rarely used to measure user load\, especially through a validated multidimensional framework. This dissertation proposes that behavioral signatures of specific load constructs are observable in game telemetry\, and a model built and validated on such telemetry can measure the distinct components of each load at the moment-to-moment granularity of individual play\, as opposed to aggregated magnitude. This dissertation consists of three parts: (1) Validation through construct manipulation and reference measurement. Specifically\, manipulating theoretically grounded load constructs and confirming that the proposed telemetry features respond as predicted\, relative to the established measurements collected alongside them (e.g.\, NASA-TLX\, pupillometry\, secondary-task). (2) Individual-level validation through rigorous longitudinal examination of the same players repeatedly across many sessions such that load constructs can be tracked at the within-person granularity. This is necessary to establish that the measure works for an individual player and not just for population averages. (3) Test the user load model by applying it to naturalistic game telemetry. Additionally\, this phase will entail the development of an insight-oriented measurement tool for GURs based on our validated user load model. The overarching contribution is a behavioral\, telemetry-based method for measuring multidimensional user load in games\, validated to measure load within each person (individual-level). This gives GURs a scalable tool and replicable process for detecting user load in commercial game telemetry. \nEvent Host: Jonattan Holmes\, Ph.D. Student\, Computational Media \nAdvisor: Magy Seif El-Nasr \nZoom: https://ucsc.zoom.us/j/98245962806?pwd=HnkwPMFSamQJFrE5aihbZbKDBbt4s9.1 \nPasscode: 347521
URL:https://live-events-ucsc.pantheonsite.io/event/holmes-j-cm-towards-a-multi-dimensional-model-of-user-load/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260723T100000
DTEND;TZID=America/Los_Angeles:20260723T120000
DTSTAMP:20260715T162923Z
CREATED:20260715T162923Z
LAST-MODIFIED:20260715T162923Z
UID:10015089-1784800800-1784808000@live-events-ucsc.pantheonsite.io
SUMMARY:Chen\, X. (STAT) - Changepoint Detection and Clustering Methods for Multivariate Time Series and Attributed Networks
DESCRIPTION:Time series data with dependence arise across a wide range of scientific and engineering disciplines\, often presenting challenging inferential problems related to structural change and clustering. This Ph.D. proposal addresses several related problems in statistical inference for multivariate and network-indexed time series. First\, we develop a weighted multivariate $U$-statistic procedure for detecting a single changepoint in the mean of a multivariate stationary time series. The proposed framework accommodates short-range dependence\, encompasses classical CUSUM and Wilcoxon tests as special cases\, and admits a tractable limiting distribution after a pre-whitening transformation. Second\, we study nodal clustering in graphs with dynamic attributes through a decoder-only latent space framework that integrates temporal dynamics and structural information via a graph-fused LASSO regularization. An extension of this framework\, in which the neural network decoder is replaced by an autoregressive structure at each node\, is also introduced. Lastly\, a future research project is proposed on modeling and changepoint inference for Arctic sea ice coverage data\, whose marginal distribution is doubly inflated with point masses at zero and one. A latent Gaussian process transformation approach is outlined that accommodates this exotic marginal structure while permitting temporal and spatial autocorrelation\, trends\, and seasonal dynamics. In tandem\, these efforts aim to provide flexible and theoretically grounded tools for analyzing complex dependent data. \nEvent Host: Xi Chen\, Ph.D. Student\, Statistical Science \nAdvisor: Robert Lund \nZoom: https://ucsc.zoom.us/j/97760514185?pwd=ImfeI5uEdBvq9eoiFXnF5pecmwfVHd.1 \nPasscode: 333103
URL:https://live-events-ucsc.pantheonsite.io/event/chen-x-stat-changepoint-detection-and-clustering-methods-for-multivariate-time-series-and-attributed-networks/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260723T120000
DTEND;TZID=America/Los_Angeles:20260723T140000
DTSTAMP:20260708T162027Z
CREATED:20260708T162027Z
LAST-MODIFIED:20260708T162027Z
UID:10015013-1784808000-1784815200@live-events-ucsc.pantheonsite.io
SUMMARY:Li\, J. (CM) - Detecting Failure to Adapt: Reading Self-Regulated Learning Breakdowns from Game Telemetry through Plan Recognition
DESCRIPTION:Three learners who fail the same level of an educational game the same number of times can be failing in three different ways\, and the difference determines what each should do next. Yet the measures a game’s logs are usually reduced to (completion time\, error counts\, mastery estimates) render the three identical. This proposal takes one breakdown as its object: failure-to-adapt\, the case where the game has repeatedly surfaced evidence that a learner’s current approach is failing and the learner’s approach shows no responsive change. The construct is grounded in Winne and Hadwin’s monitor-and-control model of self-regulated learning and defined at the level of the learner’s plan. To detect it\, a plan-recognition engine maintains a continuously updated probability estimate of which strategy the learner is executing across the whole trace; an episode is flagged when that estimate shows no evidence-responsive revision. Because behavior alone cannot settle what broke down\, flagged episodes are validated against learners’ own verbal reports\, coded blind\, and decomposed into monitoring failure\, control failure\, or control the trace cannot show. Three studies carry the work: detection and diagnosis on real telemetry from an educational game\, including a comparison against the analytics the field already runs; a formative study of what a facilitator (an instructor or teaching assistant running a class play session) must see to judge correctly which learners need attention; and a documented authoring case carrying the detection to a second game. The contribution is knowledge for game-based-learning researchers: a theory-grounded construct\, a validated way to detect it from play\, and the authoring knowledge to embed that detection in new games. \nEvent Host: Jiahong Li\, Ph.D. Student\, Computational Media \nAdvisor: Magy Seif El-Nasr \nZoom: https://ucsc.zoom.us/j/93238603235?pwd=zENRsu82HRj4JYKcMEn9MZibU8kC7F.1 \nPasscode: 835328
URL:https://live-events-ucsc.pantheonsite.io/event/li-j-cm-detecting-failure-to-adapt-reading-self-regulated-learning-breakdowns-from-game-telemetry-through-plan-recognition/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T160000
DTSTAMP:20260716T222234Z
CREATED:20260716T222234Z
LAST-MODIFIED:20260716T222234Z
UID:10015099-1784901600-1784908800@live-events-ucsc.pantheonsite.io
SUMMARY:Gholami\, K. (ECE) - Efficient Language Model Construction and Inference via Sparsity
DESCRIPTION:While large language models can match or exceed human performance\, they do so with memory and energy costs orders of magnitude greater than biological cognition. We investigate sparsity as a brain-inspired computational principle to address both. We first establish a framework for evaluating small language model construction methods\, using the next-token logit distribution as a behavioral fingerprint. Then\, we introduce a semi-structured correlation-aware weight sparsity (CWS) method that uses the full activation covariance to identify and prune correlated weights whose combined removal cost is lower than any individual score predicts. CWS\, improves perplexity over existing criteria up to 70% sparsity. To extend this gain to extreme sparsity\, we propose a hierarchical ADMM framework that optimizes pruning directly against cross-entropy and distillation loss\, first layer-wise for efficiency and then globally for cross-layer coordination. This research establishes brain-inspired principles as a foundation for efficient language models that remain accurate even under extreme compression. \nEvent Host: Kimia Gholami\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/9827512398?pwd=SGpDWGtVVG81dkgyTHhjbG81dEVUZz09&omn=98349793611 \nPasscode: 8398
URL:https://live-events-ucsc.pantheonsite.io/event/gholami-k-ece-efficient-language-model-construction-and-inference-via-sparsity/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T170000
DTSTAMP:20260720T162132Z
CREATED:20260720T162049Z
LAST-MODIFIED:20260720T162132Z
UID:10015109-1784901600-1784912400@live-events-ucsc.pantheonsite.io
SUMMARY:Fontana\, J. (STAT) - When We're Always Wrong: Scalable Variable Selection in M-Open Settings
DESCRIPTION:A ubiquitous task in statistical practice is that of variable selection\, identifying which of a large set of features are the relevant ones. As data sets with a large number of observations have become increasingly common\, new theoretical and computational challenges for model selection have emerged. We consider the variable selection problem for linear models in the M-open setting\, where the data generating process is outside the model space. We focus on the novel problem of “model superinduction”\, which refers to the tendency of model selection procedures to select larger models at an exponential rate as the sample size grows\, resulting in overparametrized models which collapse model interpretability and induce severe computational difficulties. For a set of popular frequentist information criteria and the Bayesian case of mixtures of g-priors\, we prove that when comparing nested models\, the larger model will always be asymptotically selected. We seek to minimize this effect for large n while preserving variable selection consistency.We propose utilizing a mixture of g-priors\, where the hyper-prior on g has hyper-parameters chosen to result in a slowly diminishing rate of prior influence on the posterior\, which favors simpler models while preserving consistency. We also propose a model space prior which induces stronger model complexity penalization for large sample sizes. The posterior model probabilities under our prior choices further provide an alternative information criterion that is resistant to the effects of model superinduction. \nNext\, we extend our results to other classes of popular variable selection priors\, the family of spike and slab priors\, the non-local priors\, and selection procedures that correspond to posterior modes such as the LASSO. We show that these procedures are all afflicted with model superinduction\, except for the continuous spike and slab priors when a Student-t distribution is used for both the spike and the slab. \nFinally\, we address existing bottlenecks in the computation efficacy of spike and slab based variable selection. We demonstrate that Gibbs samplers scale poorly to large sample sizes\, and the proposed alternatives in the literature result in posterior surrogates that are afflicted with superinduction. Instead\, we propose a search strategy based off easy to compute approximations of the posterior model probabilities. We show this procedure\, Fast Approximate Stochastic Search (FASS)\, coupled with post-hoc inference on parameters via either a Block-Variational Bayes approach or an Expectation Propagation approach\, results in competitive performance. We demonstrate the aforementioned phenomena\, and the efficacy of our proposed solutions\, via synthetic data examples and case studies using albedo data from GOES satellites and LCA application data. \nEvent Host: Jacob Fontana\, Ph.D. Candidate\, Statistical Science \nAdvisor: Bruno Sansó \nZoom: https://ucsc.zoom.us/j/99965109575?pwd=uGkOwWM3Rl3zP66aL1RecoOfB8Yat0.1 \nPasscode: 879019
URL:https://live-events-ucsc.pantheonsite.io/event/fontana-j-stat-when-were-always-wrong-scalable-variable-selection-in-m-open-settings/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260803T160000
DTEND;TZID=America/Los_Angeles:20260803T170000
DTSTAMP:20260724T212920Z
CREATED:20260724T212920Z
LAST-MODIFIED:20260724T212920Z
UID:10015115-1785772800-1785776400@live-events-ucsc.pantheonsite.io
SUMMARY:Le\, A. (STAT) -  Bayesian Nonparametric Analysis of Densities for Replicated Point Patterns
DESCRIPTION:Many scientific applications produce repeated point pattern realizations across subjects\, regions\, or time. While such point patterns exhibit individual variation\, we assume they arise from related point processes that share a common distributional structure. This dissertation develops a Bayesian nonparametric modeling framework built around an interpretable baseline. We work with Poisson processes\, such that the point process stochastic mechanism is characterized by the total intensity and a density with compact support. Flexible\, parsimonious weighted combinations of beta densities represent both the baseline and the replicate-specific densities. The weights corresponding to each replicate encode the features that characterize its density and are pooled hierarchically across replicates to estimate the shared baseline. Throughout\, we illustrate the framework using bike-share demand at a Chicago Divvy station\, where weekly demand shares a common daily pattern while its features evolve across weeks. \nWe lay the foundation with a conditionally independent model\, relating the replicates through a Dirichlet process prior centered on the shared baseline. The model admits a Pólya urn representation that yields fully conjugate posterior updates and partially parallel computation across replicates. Inference proceeds at both the shared and replicate-specific levels\, and predictive inference on the density of demand for a new week follows readily through the baseline. \nMoving to a dynamic extension\, we use the representation of the replicate-specific discrete random distributions to express structured dependence separately through the atoms and the weights. Stochastic processes on these components relate the locations of density features and their relative importance. The weights follow a geometric construction that keeps the model parsimonious. For the atoms\, we develop a novel stochastic process with the baseline as its marginal distribution\, so its interpretation is unchanged under temporal dependence. Across the Divvy weeks\, the contributions of these two forms of dependence are visible in the forecast uncertainty\, and both forecast the weekly densities more precisely than the conditionally independent model. \nFinally\, we model the full intensity of the underlying nonhomogeneous Poisson process by exploiting its factorization into a total intensity and a density. The factorization keeps the likelihood tractable and separates the volume of demand from its shape across the day. The dynamic density model carries directly over\, while the total intensity follows a stationary autoregressive process centered hierarchically on a baseline total intensity. This gives a baseline intensity\, extending the idea from densities to intensities. In the Divvy application\, the model yields smoother intensity estimates and sharper forecasts of the weekly intensities. \nThe common thread throughout the framework is the preservation of the baseline\, which retains the same interpretation as dependence and intensity modeling are introduced. Together\, the models give a unified characterization of the shared\, replicate-specific\, and dynamic structure of replicated point patterns. \nEvent Host: Andrew Le\, Ph.D. Candidate\, Statistical Science \nAdvisor: Athanasios Kottas \nZoom: https://ucsc.zoom.us/j/94954212320?pwd=lmcDG6LvDQTb73BOE2NqabU9M2Uzms.1 \nPasscode: 772566 \n 
URL:https://live-events-ucsc.pantheonsite.io/event/le-a-stat-bayesian-nonparametric-analysis-of-densities-for-replicated-point-patterns/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260810T103000
DTEND;TZID=America/Los_Angeles:20260810T123000
DTSTAMP:20260721T182603Z
CREATED:20260721T182603Z
LAST-MODIFIED:20260721T182603Z
UID:10015110-1786357800-1786365000@live-events-ucsc.pantheonsite.io
SUMMARY:Zhao\, Z. (CSE) - TOWARD VERIFIABLE REASONING IN LLMS
DESCRIPTION:Chain-of-thought (CoT) prompting can improve final-answer performance\, but it does not guarantee that intermediate reasoning steps are faithful\, valid\, or checkable. This proposal studies how formal methods can make natural-language reasoning more reliable by translating CoT rationales into Lean artifacts\, checking the resulting theorem statements and proofs\, and using compiler feedback to diagnose and repair failures. The completed work evaluates direct zero-shot and few-shot auto-formalization pipelines for quantity- and logic-focused reasoning problems\, measuring proof type-check rate\, theorem-statement validity\, assumption faithfulness\, and repair behavior. The ongoing work extends this pipeline with AMR-guided semantic representations and altered-rationale stress tests. The planned work proposes methods of modeling LLM Agent thinking in Lean. Together\, these components separate two questions that are often conflated: whether a model translated the reasoning into the right formal goal\, and whether that goal can be proved once translated. The research goal is to develop an evaluation framework and a tool-supported workflow to improve the reliability\, auditability\, and semantic faithfulness of LLM reasoning. \nEvent Host: Zekun Zhao\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Jeffrey Flanigan \nZoom: https://ucsc.zoom.us/j/96068207641?pwd=anTpLhBXhIdaIhXKDTB32HlDMA6uIO.1 \nPasscode: 656037
URL:https://live-events-ucsc.pantheonsite.io/event/zhao-z-cse-toward-verifiable-reasoning-in-llms/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260814T083000
DTEND;TZID=America/Los_Angeles:20260814T103000
DTSTAMP:20260811T162729Z
CREATED:20260811T162729Z
LAST-MODIFIED:20260811T162729Z
UID:10015331-1786696200-1786703400@live-events-ucsc.pantheonsite.io
SUMMARY:Krishnaswamy\, L. (CSE) - Network Load Balancing for Geographically Distributed Datacenters
DESCRIPTION:As datacenters scale up and become more geographically distributed\, wide-area network inter-datacenter traffic\, which typically consists of data-heavy tasks\, has become increasingly prevalent. Some of the noteworthy challenges raised by the coexistence and interaction between inter- and intra-datacenter traffic are the differences in their QoS requirements\, link utilization\, and round-trip times. To the best of our knowledge\, these challenges have not yet been addressed by current datacenter load balancers. To highlight this gap\, we conducted a comparative performance study of state-of-the-art datacenter load balancers. Through extensive simulations\, we study how they perform under different network topologies and workloads\, including intra-datacenter\, inter-datacenter\, and mixed intra- and inter-datacenter workloads that reflect how datacenters have evolved to keep up with their continuously changing driving application landscape. Our study shows that current load balancers are not able to adequately distribute load under inter-DC workloads as well as mixed intra- and inter-datacenter traffic coexistence.\nMotivated by our observations\, we introduce Balancia\, a transport agnostic\, lightweight network load balancer that dynamically switches between per-flow and per-packet control in order to provide adequate performance for both intra- and inter-DC traffic given their different characteristics and quality-of-service (QoS) requirements. We show that\, when compared against state-of-the-art load balancers\, Balancia achieves close to 80% reduction in the 99% tail flow completion times for inter-datacenter traffic in the presence of intra- and inter-datacenter workload coexistence. Further in this work\, we explore proactively monitoring for congestion with the help of phantom queues and rerouting flows in a timely manner. Through Balancia2.0 we decouple congestion control and load balancing signaling\, and examine its effects on DC and WAN traffic. \nEvent Host: Lakshmi Krishnaswamy\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Katia Obraczka \nZoom: https://ucsc.zoom.us/j/94414934371?pwd=7P3Umt0QQ930ESV02jMvCVHVbkIp9r.1 \nPasscode: 905786
URL:https://live-events-ucsc.pantheonsite.io/event/krishnaswamy-l-cse-network-load-balancing-for-geographically-distributed-datacenters/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260814T141500
DTEND;TZID=America/Los_Angeles:20260814T161500
DTSTAMP:20260810T163804Z
CREATED:20260810T163804Z
LAST-MODIFIED:20260810T163804Z
UID:10015329-1786716900-1786724100@live-events-ucsc.pantheonsite.io
SUMMARY:Aliamooei Lakeh\, S. (ECE) - Optimization and Decision-Support Frameworks for Resilient Power Systems Under Large-Scale Electrification
DESCRIPTION:The rapid electrification of transportation is creating new interdependencies between power and transportation systems\, particularly during extreme events and disasters. As electric vehicle (EV) adoption increases\, evacuation-related charging demand\, infrastructure disruptions\, and limited access to energy resources introduce challenges that conventional power system planning and operation frameworks were not designed to address. Wildfires provide a critical example: transmission outages and public safety power shutoffs can reduce network capacity while evacuation simultaneously concentrates charging demand along affected transportation corridors. Improving resilience therefore requires coordinated decision-making across the full disaster lifecycle\, from infrastructure preparedness to emergency operation and post-disaster recovery.\nThis research develops optimization and decision-support methods for resilient power systems under large-scale transportation electrification\, addressing three complementary stages of resilience. First\, the research will extend existing infrastructure planning models through a two-stage stochastic mixed-integer programming framework for the strategic siting and sizing of distributed generation\, energy storage systems\, and EV charging infrastructure under disaster uncertainty. Second\, building on a developed single-period nonlinear AC optimal power flow formulation\, the research will extend the framework to multi-period operation to coordinate priority-based EV evacuation charging with mobile EV charger dispatch during grid contingencies while explicitly representing voltage and thermal operating constraints. Third\, a mixed-integer routing and scheduling framework is proposed for the deployment of mobile energy resources\, including energy tankers and vehicle-to-everything (V2X)-capable fleets\, to support electric transportation and critical loads when conventional infrastructure is disrupted.\nTogether\, these components connect long-term infrastructure planning\, emergency grid operation\, and post-disaster energy recovery within an integrated optimization and decision-support framework. The research will build on preliminary results obtained using IEEE benchmark systems and will incorporate California case studies representing wildfire and flooding scenarios. Resilience will be evaluated using technical and operational metrics such as load not served\, priority-weighted EV energy served\, and recovery time. The overall goal is to provide decision-support tools for utilities\, transportation agencies\, and emergency planners to support resilient planning\, operation\, and recovery in increasingly electrified energy and transportation systems. \nEvent Host: Saeed Aliamooei Lakeh\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisors: Keith Corzine and Leila Parsa \nZoom: https://ucsc.zoom.us/j/95295718011?pwd=1Y5vBhoBX9V5OVQ3MJzy4FgyhtO9eb.1 \nPasscode: 545834
URL:https://live-events-ucsc.pantheonsite.io/event/aliamooei-lakeh-s-ece-optimization-and-decision-support-frameworks-for-resilient-power-systems-under-large-scale-electrification/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260817T100000
DTEND;TZID=America/Los_Angeles:20260817T120000
DTSTAMP:20260810T162940Z
CREATED:20260810T162940Z
LAST-MODIFIED:20260810T162940Z
UID:10015328-1786960800-1786968000@live-events-ucsc.pantheonsite.io
SUMMARY:Nikolakakis\, M. (ECE) - Learned Gridless Representations of Cone Beam Computed Tomography Scans
DESCRIPTION:Medical image representation has long been dominated by voxel-grid matrices. While\ntheir inherent structure and order work efficiently for various linear transformations and\nprovide a seamless visualization method on monitors\, they fail to preserve the topology\nof the scan and to encode sparse information in a memory-efficient way.   The recent emergence of machine learning-based continuous coordinate-based\nscene representations such as neural radiance fields and Gaussian splatting has provided alternative representation techniques. These approaches overfit the weights of\na model by iterative differentiable rendering and have been shown to be more compact than grid representations. They are then able to perform novel view\nsynthesis from any given camera pose.\nOff-grid representations translate directly to Cone Beam Computed Tomography\nsparse-view acquisitions\, where streaking and quantum noise artifacts are dominant.\nUsing differentiable rendering\, a continuous representation is achieved\, with interpolation providing a path to recover some of the lost signal.\nIn this dissertation\, we apply a variety of methodologies\, including Gaussian splatting\, implicit occupancy fields\, and Neural Attenuation Fields regularized with an\nanatomic prior\, to Cone Beam Computed Tomography reconstruction\, and evaluate\ntheir performance across a range of anatomic datasets. Our models show that learned\ngridless representations achieve substantial memory reduction\, recover signal under\nextreme view sparsity\, and preserve scene topology. \nEvent Host: Manolis Nikolakakis\, Ph.D. Candidate\, Electrical and Computer Engineering  \nAdvisor: Razvan Marinescu \nZoom: https://ucsc.zoom.us/j/5964517596?pwd=c1AwRlJLNk5pVzFBUENibEw3by85Zz09
URL:https://live-events-ucsc.pantheonsite.io/event/nikolakakis-m-ece-learned-gridless-representations-of-cone-beam-computed-tomography-scans/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260817T130000
DTEND;TZID=America/Los_Angeles:20260817T150000
DTSTAMP:20260813T194341Z
CREATED:20260813T194341Z
LAST-MODIFIED:20260813T194341Z
UID:10015338-1786971600-1786978800@live-events-ucsc.pantheonsite.io
SUMMARY:Condon\, C. (BMEB) - Genomic conflict across scales
DESCRIPTION:Genomes are often viewed as cooperative systems in which genes work together to support organismal function. Yet genetic elements can also act in ways that favor their own transmission or persistence\, creating conflict within the genome. In this talk\, I examine the evolutionary and functional consequences of such genomic conflict across three systems. First\, I investigate segregation distortion in Arabidopsis hybrids and its potential role in the early evolution of reproductive isolation. Second\, I characterize the population dynamics and functional effects of introners\, mobile elements that generate new introns in the green alga Micromonas pusilla. Finally\, I explore widespread splicing dysfunction in algal mating-type chromosomes and its consequences for transcript diversity. Together\, these studies highlight how departures from genome cooperation can shape inheritance\, genome evolution\, and gene regulation. \nEvent Host: Chris Condon\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Russell Corbett-Detig
URL:https://live-events-ucsc.pantheonsite.io/event/condon-c-bmeb-genomic-conflict-across-scales/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260818T100000
DTEND;TZID=America/Los_Angeles:20260818T110000
DTSTAMP:20260810T162245Z
CREATED:20260810T162245Z
LAST-MODIFIED:20260810T162245Z
UID:10015327-1787047200-1787050800@live-events-ucsc.pantheonsite.io
SUMMARY:Gutie\, J. (SciCAM) -  SORh: Hyperbolic Relaxation Methods For Elliptic Problems In Computational Fluid Dynamics
DESCRIPTION:This thesis explores iterative methods for solving elliptic partial differential equations (PDEs)\, which are used in computational fluid dynamics (CFD) to model a wide range of physical phenomena. The primary application of interest here is self-gravity\, modeled by Poisson’s equation. Although many numerical approaches exist\, including direct matrix inversion\, FFT-based methods\, and classical iterative methods such as Jacobi and Gauss-Seidel\, these approaches involve tradeoffs in computational cost\, scalability\, implementation complexity\, and adaptability to changing boundary conditions and problem configurations. \nTherefore\, we introduce SORh\, a simple and efficient relaxation method derived from a hyperbolic reformulation of Poisson’s equation. SORh generalizes classical successive over-relaxation (SOR) by providing independent control of residual relaxation and the directional propagation of Gauss–Seidel corrections. We present formulations of SORh in one and two spatial dimensions and investigate its stability\, accuracy\, and computational performance through analytical derivations and numerical comparisons with established relaxation methods. The results identify favorable SORh formulations\, clarify their relationships to classical relaxation methods\, and demonstrate improved convergence on selected test problems. Finally\, we demonstrate applications of SORh to astrophysical self-gravity simulations in the FLASH code and to magnetohydrodynamic (MHD) divergence cleaning. \nEvent Host: Jonathan Guite\, M.S. Candidate\, Scientific Computing & Applied Mathematics  \nAdvisor: Dongwook Lee \nZoom: https://ucsc.zoom.us/j/92153750104?pwd=ZdLiDZeLqOAlVNX9C4bCloKno9tAeB.1 \nPasscode: 769232
URL:https://live-events-ucsc.pantheonsite.io/event/gutie-j-scicam-sorh-hyperbolic-relaxation-methods-for-elliptic-problems-in-computational-fluid-dynamics/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260818T140000
DTEND;TZID=America/Los_Angeles:20260818T160000
DTSTAMP:20260817T160201Z
CREATED:20260817T155423Z
LAST-MODIFIED:20260817T160201Z
UID:10015341-1787061600-1787068800@live-events-ucsc.pantheonsite.io
SUMMARY:Lupin-Jimenez\, L. (AM) - Data-Driven Deep Learning for Turbulent Phenomena: Regional Ocean Prediction and Assimilation\, Spectral Bias in Diffusion Models\, and Equation Discovery
DESCRIPTION:Deep learning models trained on simulation and reanalysis data can now emulate turbulent geophysical flows at a small fraction of the computational cost of numerical solvers.\nTheir scientific utility depends on physical consistency\, which for the systems studied here\nrests in large part on spectral fidelity\, the accurate reconstruction of variance across spatial\nscales. This document presents two published studies and two studies in progress that develop\, analyze\, and apply data-driven methods for turbulent phenomena along that thread.\nThe first study develops FCDS\, a framework that autoregressively emulates surface ocean\ndynamics over the Gulf of Mexico at 8 km resolution and simultaneously downscales and\nbias-corrects the emulated fields to 4 km\, with a spectral loss that keeps decadal integrations stable and statistically consistent with a high-resolution reanalysis. The second study\ndevelops a neural-operator-conditioned denoising diffusion model that reconstructs regional\nsurface ocean states from Lagrangian-like observations at 99% and 99.9% sparsity without a\nbackground dynamical model\, and shows that the recovered small-scale dynamics are visible\nin spectral diagnostics but not in pointwise metrics. The third study derives a signal-tonoise theory of spectral bias in diffusion models for 2D turbulence\, organized around the\ncrossover wavenumber kc(τ) at which signal and noise contribute equal power\, and validates\nits predictions on a sweep of 28 models spanning seven forcing wavenumbers and four noise\nschedulers. The fourth study develops a window-pair spectral method for discovering governing equations from single-point sensor measurements of soliton dynamics in a superfluid\nwave flume\, replacing noise-amplifying instantaneous derivatives with finite-time spectral\nshifts and verifying the discovered equations against a measured-scalar null model. A concluding chapter summarizes the results and outlines future work on novel architectures and\nmethods for data-driven emulation of physical simulations. \nEvent Host: Leonard Lupin-Jimenez\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Ashesh Chattopadhyay \nZoom: https://ucsc.zoom.us/j/97866640488?pwd=UJdTs3sxKfFbz5mabKLIyx5ZYF90J9.1 \nPasscode: 815911
URL:https://live-events-ucsc.pantheonsite.io/event/lupin-jimenez-l-am-data-driven-deep-learning-for-turbulent-phenomena-regional-ocean-prediction-and-assimilation-spectral-bias-in-diffusion-models-and-equation-discovery/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260820T130000
DTEND;TZID=America/Los_Angeles:20260820T150000
DTSTAMP:20260814T163909Z
CREATED:20260814T163815Z
LAST-MODIFIED:20260814T163909Z
UID:10015340-1787230800-1787238000@live-events-ucsc.pantheonsite.io
SUMMARY:Penunuri\, G. (BMEB) - Genomic\, Proteomic\, and Computational Approaches to the Study of Host-Microbe Systems
DESCRIPTION:Host-microbe systems are core to some of biology’s most consequential interactions\, from the pathogens that drive infectious disease to symbionts affecting agricultural pest control and vector-borne disease transmission. Yet unlike the model organisms that have driven most of modern molecular biology\, the microbes at the center of these interactions are rarely genetically tractable: many cannot be cultured outside a host\, resist standard tools for genetic manipulation\, and are annotated largely by homology to distantly related free-living relatives. This dissertation develops genomic\, proteomic\, and computational methods to work around this lack of infrastructure and contribute techniques and tools to the study and further understanding of host-microbe systems. Using Wolbachia cultured in Drosophila melanogaster cell lines\, I demonstrate that chemical mutagenesis can be used to perturb intracellular genomes leaving a detectable mutational signal. I employ a low error rate sequencing technique to record and model the mutational landscape left by the mutagen ethyl methanesulfonate (EMS) demonstrating its use for mutagenesis screens of intracellular bacteria. I next utilize structural proteome datasets to screen host-microbe proteomes for strong candidates of molecular mimicry\, microbe proteins that have coevolved a eukaryotic like domain or structure and suggest use for host manipulation or microbe survival in the host environment. Building off of this screen for novel effectors through structural alignments I develop and test a distributed computing system for performing large scale systematic literature reviews. Altogether these projects represent generalizable approaches to the study of host-microbe systems reaching from classically studied and thoroughly understood to novel and non-model systems. \nEvent Host: Gabriel Penunuri\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Russell Corbett-Detig \nZoom: https://ucsc.zoom.us/j/98216883331?pwd=uqmUSQba2X6GVNBhOhAGRwgCZyjAyj.1 \nPasscode: 730377
URL:https://live-events-ucsc.pantheonsite.io/event/penunuri-g-bmeb-genomic-proteomic-and-computational-approaches-to-the-study-of-host-microbe-systems/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
GEO:46.1226939;-64.7891251
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Biomedical Sciences Building 575 McLaughlin Drive;X-APPLE-RADIUS=500;X-TITLE=575 McLaughlin Drive:geo:-64.7891251,46.1226939
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260821T110000
DTEND;TZID=America/Los_Angeles:20260821T120000
DTSTAMP:20260820T171344Z
CREATED:20260820T171314Z
LAST-MODIFIED:20260820T171344Z
UID:10015349-1787310000-1787313600@live-events-ucsc.pantheonsite.io
SUMMARY:Nava\, A. (AM) - Machine-Learning Methods for Prediction of Biological Systems
DESCRIPTION:Advances in microscopy have enabled the collection of high-quality single-cell datasets\, providing new opportunities to identify the mechanisms underlying complex biological processes. In this work\, we develop machine-learning frameworks using single-cell temporal data with the goal of predicting and providing insights into these mechanisms. We produce frameworks for two biological systems\, bacterial spore germination\, the process in which bacteria begin metabolic activity\, and embryonic stem cell organization. Bacterial spore germination is a critical transition in which the spore becomes susceptible to control techniques\, however the mechanisms governing this transition are unknown. We develop a machine-learning framework that predicts germination timing at the single-spore level\, enabling identification of predictive features associated with germination that may reflect underlying biological mechanisms. Embryonic stem cell organization has been shown to closely recapitulate formations seen in embryonic development\, but the mechanisms driving their spatial organization remain unclear. Here\, we develop a simple agent-based model in which spatial organization is driven by cell-cell interaction parameters. We then train a machine-learning framework to infer these underlying interaction parameters from simulated data and propose that this approach can be extended to other agent-based models calibrated to experimental stem cell data. These studies demonstrate that machine-learning models can be used for prediction using single-cell temporal data\, as well as tools to develop mechanistic hypotheses. \n  \nEvent Host: Alexandra Nava\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Marcella Gomez \nZoom: https://ucsc.zoom.us/j/98821445104?pwd=OFAKwGrObh02bPLgXieXsDcTxxS1Cj.1 \nPasscode: 392769
URL:https://live-events-ucsc.pantheonsite.io/event/nava-a-am-machine-learning-methods-for-prediction-of-biological-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260821T150000
DTEND;TZID=America/Los_Angeles:20260821T160000
DTSTAMP:20260817T160248Z
CREATED:20260817T160020Z
LAST-MODIFIED:20260817T160248Z
UID:10015342-1787324400-1787328000@live-events-ucsc.pantheonsite.io
SUMMARY:Huang\, X. (CSE) - Scalable and Verifiable Reasoning for Medical Foundation Models
DESCRIPTION:This PhD research focuses on developing reliable medical foundation models capable of reasoning across textual\, visual\, and interactive clinical information. The work investigates three complementary directions: improving medical reasoning through test-time scaling\, training multimodal medical models with verifiable rewards\, and synthesizing high-quality visual question-answering data from biomedical literature using generator-verifier frameworks. Building on these efforts\, the proposed research will extend medical language and multimodal models toward agentic systems that can gather evidence\, use external tools\, integrate multimodal information\, and verify decisions over sequential interactions. Overall\, this research aims to improve the reliability\, efficiency\, and transparency of medical AI reasoning while supporting reproducible and human-supervised applications in healthcare. \nEvent Host: Xiaoke Huang\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Yuyin Zhou \nZoom: https://ucsc.zoom.us/j/8855787311 \nPasscode: 197379
URL:https://live-events-ucsc.pantheonsite.io/event/huang-x-cse-scalable-and-verifiable-reasoning-for-medical-foundation-models/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260824T140000
DTEND;TZID=America/Los_Angeles:20260824T160000
DTSTAMP:20260818T161442Z
CREATED:20260818T161442Z
LAST-MODIFIED:20260818T161442Z
UID:10015343-1787580000-1787587200@live-events-ucsc.pantheonsite.io
SUMMARY:Pawar\, M. (CSE) - Understanding Representations\, Reasoning\, and Decision-Making in Autonomous Driving Models
DESCRIPTION:Modern autonomous-driving models increasingly rely on learned representations and generated reasoning to interpret complex scenes and produce predictions or actions. However\, it remains unclear what information these models encode\, how that information is exposed through common interpretation methods\, and whether their stated reasoning meaningfully influences their behavior. This research investigates these questions across motion-forecasting and vision-language-action models. \nEvent Host: Manasi Pawar\, Ph.D. Student\, Computer Science & Engineering  \nAdvisor: Leilani Gilpin \nZoom: https://ucsc.zoom.us/j/92653015420?pwd=xEyOcy5ZuTcHN04La9w2KP1cmVY1ao.1 \nPasscode: 309299
URL:https://live-events-ucsc.pantheonsite.io/event/pawar-m-cse-understanding-representations-reasoning-and-decision-making-in-autonomous-driving-models/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260825T110000
DTEND;TZID=America/Los_Angeles:20260825T130000
DTSTAMP:20260812T161153Z
CREATED:20260812T161153Z
LAST-MODIFIED:20260812T161153Z
UID:10015333-1787655600-1787662800@live-events-ucsc.pantheonsite.io
SUMMARY:Gomez\, J. (CSE) - Toward Sustainable and Secure Open Source Software: Discovery\, Measurement\, and Defense
DESCRIPTION:In March 2024\, a backdoor was discovered in xz Utils\, a widely used open source data compression library present in nearly every major Linux distribution. The attack was discovered days before merging into major distributions\, and if this had happened\, it would have allowed attackers to execute arbitrary code on millions of systems worldwide via SSH. \nThe success of this backdoor was enabled by two failures. The first was technical: weaknesses in the software supply chain allowed a malicious actor to inject code into a widely trusted release. The second was human: the project’s only maintainer\, overwhelmed and burned out after years of maintaining critical infrastructure alone\, was the target of a multi-year social engineering campaign\, in which a malicious actor built trust under a false identity and gradually obtained commit access to the project. This incident shows that software security failures and sustainability failures are not independent: an overburdened\, unsupported maintainer is itself an attack surface. \nAcademic and scientific open source software (OSS) faces both of these crises simultaneously. Projects that critical infrastructure depends on are maintained by researchers\, students\, and faculty who contribute in their spare time\, without dedicated security training or institutional support. Existing security frameworks including NIST’s SSDF\, OWASP’s SCVS\, and SLSA were not designed with these communities in mind\, and policy efforts such as the EU Cyber Resilience Act have shown that mandates developed without community input risk harming the ecosystems they are meant to protect. \nThis dissertation addresses the sustainability and security of academic open source software through two parallel empirical research tracks. The sustainability track combines GitHub’s REST API with LLM-based filtering to discover and characterize over 216\,000 institutionally affiliated repositories across 32 academic and research institutions\, finding that while 84\% include a README\, only 23.4% carry a detectable license and fewer than 2% include a Contributing Guide. Building on this dataset\, we develop a maturity-staged sustainability framework that classifies projects into four lifecycle stages and generates targeted recommendations for Open Source Program Offices (OSPOs). \nThe security track examines whether post-9/11 trade security programs offer a workable model for OSS supply-chain policy\, finding that effective frameworks require voluntary incentives and direct community engagement rather than top-down mandates. We further evaluate five large language models on the OWASP Benchmark for vulnerability triage\, finding that o1-mini reduces false positives by 20% over the Semgrep baseline\, demonstrating the potential for automation to reduce the security burden on individual maintainers. \nTogether\, these contributions treat sustainability and security as interconnected problems. A project that cannot sustain itself cannot secure its code\, and this dissertation takes steps toward closing both gaps. \n  \nEvent Host: Juanita Gomez\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Alvaro Cardenas  \nZoom: https://ucsc.zoom.us/j/91057980344?pwd=XMMjHZVgbbLXfwxKehrTEbat18066o.1 \nPasscode: 292091
URL:https://live-events-ucsc.pantheonsite.io/event/gomez-j-cse-toward-sustainable-and-secure-open-source-software-discovery-measurement-and-defense/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260827T150000
DTEND;TZID=America/Los_Angeles:20260827T170000
DTSTAMP:20260814T163137Z
CREATED:20260814T163137Z
LAST-MODIFIED:20260814T163137Z
UID:10015339-1787842800-1787850000@live-events-ucsc.pantheonsite.io
SUMMARY:Kramer\, A. (BMEB) - Scalable phylo-pangenomics
DESCRIPTION:The COVID-19 pandemic generated genomic data at unprecedented scale\, with tens of millions of SARS-CoV-2 genomes deposited in public repositories and thousands of new sequences added each day. This dissertation develops methods for analyzing genomic datasets at this scale\, unified by the idea that encoding genomes according to their evolutionary relationships can make otherwise intractable computations practical. First\, I evaluate online phylogenetic inference\, in which new genomes are continuously added to an existing tree\, and compare parsimony-based methods with maximum-likelihood approaches under pandemic time constraints. For densely sampled SARS-CoV-2 genomes\, online inference with UShER and matOptimize produces trees comparable to established maximum-likelihood methods while requiring orders of magnitude less time and memory. I then develop tools that make phylogenies containing millions of genomes useful for downstream analysis and visualization. ShUShER enables privacy-preserving phylogenetic placement within a web browser\, allowing laboratories to analyze sensitive sequences without transmitting them to an external server. Treenome Browser co-visualizes the genomic variation of millions of samples alongside their phylogenetic relationships by operating directly on a compressed mutation-annotated tree. Finally\, I describe Panmap\, which uses Pangenome Mutation-Annotated Networks (PanMANs) to place\, align\, and genotype sequencing reads and to estimate haplotype abundances against reference collections containing up to millions of genomes. Panmap produces indexes hundreds of times smaller than graph-based alternatives\, improves genome reconstruction over single-reference workflows at low coverage\, and supports applications ranging from pathogen genome assembly to ancient environmental DNA analysis. Together\, these results show that evolutionary history can serve not only as an object of inference but as a scalable computational infrastructure for phylogenomic and pangenomic analyses as genomic datasets continue to grow. \nEvent Host: Alexander Kramer\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics \nAdvisor: Russell Corbett-Detig \nZoom: https://ucsc.zoom.us/j/91364025182?pwd=Tq95CuaBqrePatRjopy6uJ9bbjsrIH.1 \nPasscode: 318268
URL:https://live-events-ucsc.pantheonsite.io/event/kramer-a-bmeb-scalable-phylo-pangenomics/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
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END:VEVENT
END:VCALENDAR