BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Events - ECPv6.17.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://events.ucsc.edu X-WR-CALDESC:Events for Events REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20250309T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20251102T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20260308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20261101T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20270314T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20271107T090000 END:STANDARD END:VTIMEZONE 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@events.ucsc.edu 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://events.ucsc.edu/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 ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/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:20260713T160000 DTEND;TZID=America/Los_Angeles:20260713T170000 DTSTAMP:20260708T155209Z CREATED:20260708T155209Z LAST-MODIFIED:20260708T155209Z UID:10015011-1783958400-1783962000@events.ucsc.edu 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://events.ucsc.edu/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 ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 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@events.ucsc.edu 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://events.ucsc.edu/event/scott-j-cse-mechanistic-specialization-does-not-guarantee-performance-evidence-from-dual-attentiontransformers/ CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg LOCATION: 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@events.ucsc.edu 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 ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 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@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-2/ CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png LOCATION: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260708T120000 DTEND;TZID=America/Los_Angeles:20260708T130000 DTSTAMP:20251001T224502Z CREATED:20250923T070000Z LAST-MODIFIED:20251001T224502Z UID:10000287-1783512000-1783515600@events.ucsc.edu SUMMARY:Engineering Teaching Community (Faculty) DESCRIPTION:During the chaos of a quarter\, is it hard to find time to reflect and improve as an instructor? Would you like to be a part of an inclusive\, supportive group of engineering instructors who do this in community? ETC is for sharing teaching experiences\, classroom ideas\, research on learning\, and methods that support instructors and students. All are welcome\, and lunch is provided. Please reach out to Jenny Quynn with questions. URL:https://events.ucsc.edu/event/engineering-teaching-community-faculty/2026-07-08/ LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Meetings & Conferences,Training ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2025/09/b19cd317e2122064e85e5d3d896b4e3426736249.jpg GEO:37.000369;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Engineering Baskin Engineering 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Baskin Engineering 1156 High Street:geo:-122.0632371,37.000369 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260630T173000 DTEND;TZID=America/Los_Angeles:20260630T200000 DTSTAMP:20260603T215647Z CREATED:20260603T215647Z LAST-MODIFIED:20260603T215647Z UID:10014896-1782840600-1782849600@events.ucsc.edu SUMMARY:Inaugural PyTorch Santa Cruz Meetup DESCRIPTION:A community gathering of people interested in PyTorch and the projects that use it – not an official PyTorch organization. Sponsored by Red Hat and University of California Santa Cruz \nLocation: Engineering 2\, Room 180 \n​Food\, Socializing\, and Excellent talks from the PyTorch Ecosystem\n\n5:30 – 6:30 Food and Socializing\n6:30 – 7:00 Talk 1\n​7:00 – 7:30 Talk 2\n7:30 – 8:00 Talk 3\n\nFor detailed agenda and registration – visit the event website. URL:https://events.ucsc.edu/event/inaugural-pytorch-santa-cruz-meetup/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Meetings & Conferences ATTACH;FMTTYPE=image/avif:https://events.ucsc.edu/wp-content/uploads/2026/06/PyTorch_6.30.26_Event.Image_.avif GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 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@events.ucsc.edu 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://events.ucsc.edu/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://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260625T130000 DTEND;TZID=America/Los_Angeles:20260625T140000 DTSTAMP:20260622T225613Z CREATED:20260622T225613Z LAST-MODIFIED:20260622T225613Z UID:10014924-1782392400-1782396000@events.ucsc.edu SUMMARY:BME/Genomics Seminar: Supervised and Unsupervised DeepGene Finding and Genome Foundation Models DESCRIPTION:Presenter: Mario Stanke\, Professor of Bioinformatics\, University of Greifswald \nDescription: This talk will explore recent machine learning approaches for eukaryotic genome annotation. Our supervised ab initio deep gene finder\, Tiberius\, correctly predicts more than four times as many human protein-coding gene structures as its father\, Augustus\, and in some clades\, it approaches the accuracy of evidence-based pipelines such as BRAKER. Genome foundation models can automatically learn annotation-relevant embeddings from unannotated training genomes. I will also present Vipsania\, the unsupervised wife of Tiberius. Vipsania is a genome foundation model that learns hidden Markov models to find gene structures from naked genomes using a BERT-style masked language model objective. Finally\, I will report on ongoing efforts to use phylogenetic teaching signals from whole-genome vertebrate alignments to train a genome foundation model comparatively. \nKeywords: hidden Markov model layer\, linear recurrent unit\, continuous-time Markov chains on trees \nBio: Mario Stanke studied mathematics and computer science at the University of Göttingen and UCBerkeley\, and received his Dr. rer. nat. from the University of Göttingen. He completed a postdoctoral fellowship in the Haussler lab at UC Santa Cruz in 2006–2007. He has been a Professor of Bioinformatics at the Institute of Mathematics and Computer Science at the University of Greifswald since 2010. \nHosted by: Genomics Institute \nLocation: E2-599 (limited space) \nZoom: https://ucsc.zoom.us/j/95380317295?pwd=0HbwSYKRQqyCtBcPXGfoB0tPOsA16V.1 URL:https://events.ucsc.edu/event/bme-genomics-seminar-supervised-and-unsupervised-deepgene-finding-and-genome-foundation-models/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Lectures & Presentations,Seminars ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/06/stanke-2.jpg GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260624T120000 DTEND;TZID=America/Los_Angeles:20260624T130000 DTSTAMP:20251001T224502Z CREATED:20250923T070000Z LAST-MODIFIED:20251001T224502Z UID:10000286-1782302400-1782306000@events.ucsc.edu SUMMARY:Engineering Teaching Community (Faculty) DESCRIPTION:During the chaos of a quarter\, is it hard to find time to reflect and improve as an instructor? Would you like to be a part of an inclusive\, supportive group of engineering instructors who do this in community? ETC is for sharing teaching experiences\, classroom ideas\, research on learning\, and methods that support instructors and students. All are welcome\, and lunch is provided. Please reach out to Jenny Quynn with questions. URL:https://events.ucsc.edu/event/engineering-teaching-community-faculty/2026-06-24/ LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Meetings & Conferences,Training ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2025/09/b19cd317e2122064e85e5d3d896b4e3426736249.jpg GEO:37.000369;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Engineering Baskin Engineering 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Baskin Engineering 1156 High Street:geo:-122.0632371,37.000369 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@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/ CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg LOCATION: 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@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/ CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png LOCATION: 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@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 ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260610T120000 DTEND;TZID=America/Los_Angeles:20260610T130000 DTSTAMP:20251001T224502Z CREATED:20250923T070000Z LAST-MODIFIED:20251001T224502Z UID:10000285-1781092800-1781096400@events.ucsc.edu SUMMARY:Engineering Teaching Community (Faculty) DESCRIPTION:During the chaos of a quarter\, is it hard to find time to reflect and improve as an instructor? Would you like to be a part of an inclusive\, supportive group of engineering instructors who do this in community? ETC is for sharing teaching experiences\, classroom ideas\, research on learning\, and methods that support instructors and students. All are welcome\, and lunch is provided. Please reach out to Jenny Quynn with questions. URL:https://events.ucsc.edu/event/engineering-teaching-community-faculty/2026-06-10/ LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Meetings & Conferences,Training ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2025/09/b19cd317e2122064e85e5d3d896b4e3426736249.jpg GEO:37.000369;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Engineering Baskin Engineering 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Baskin Engineering 1156 High Street:geo:-122.0632371,37.000369 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@events.ucsc.edu SUMMARY:Kim\, C. (CSE)- Toward Adaptive Graph Processing and Fault-Tolerant Agentic Inference on Heterogeneous Distributed Systems DESCRIPTION:Edge computing and distributed AI systems increasingly operate under heterogeneous resources\, dynamic workloads\, and frequent failures\, requiring both adaptivity and fault tolerance for efficient execution. In heterogeneous edge clusters\, nodes differ significantly in CPU throughput\, memory capacity\, and network bandwidth\, while modern distributed GPU clusters supporting agentic LLM inference must recover large amounts of runtime state under routine failures. This dissertation addresses these challenges through two systems: Zsiga\, an adaptive distributed graph processing system for heterogeneous edge clusters\, and Forte\, a fault-tolerant KV cache recovery system for distributed agentic LLM inference. \nZsiga improves connected component computation through capacity-aware graph partitioning and runtime-adaptive boundary migration\, reducing execution time by up to 90.9% while eliminating out-of-memory failures under heterogeneous resource constraints. Forte addresses KV cache recovery for long-running agentic inference workloads\, where failures can erase accumulated reasoning trajectories and tool interaction histories. Forte exploits the observation that not all KV blocks are equally critical\, introducing criticality-aware erasure coding\, domain-diverse placement\, and prioritized foreground recovery to enable efficient recovery under correlated failures. Experimental results show that Forte is the only evaluated scheme that successfully resumes execution under correlated domain failures\, reducing foreground stall by 89.7% and end-to-end recovery latency by 50.6–58.9% at 2.0$\times$ memory overhead. Together\, these systems demonstrate how adaptivity and fault tolerance can improve the efficiency and resilience of distributed systems in heterogeneous and failure-prone environments. \nEvent Host: Chaeeun Kim\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Chen Qian & Liting Hu \nZoom: https://ucsc.zoom.us/j/9863615188?pwd=kTka0aZXJ070tor1EKvrt3X6AveBRp.1 \nPasscode:  cG5SL8 \n  \n  URL:https://events.ucsc.edu/event/kim-c-cse-toward-adaptive-graph-processing-and-fault-tolerant-agentic-inference-on-heterogeneous-distributed-systems/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260609T103000 DTEND;TZID=America/Los_Angeles:20260609T130000 DTSTAMP:20260526T194445Z CREATED:20260526T194326Z LAST-MODIFIED:20260526T194445Z UID:10014873-1781001000-1781010000@events.ucsc.edu SUMMARY:Shen\, G. (CSE) - Library-Level Choreographic Programming DESCRIPTION:Modern software increasingly relies on distributed systems to provide accessible\, scalable\,\nand reliable services. Choreographic programming brings a global perspective to distributed\nsystem development: programmers write a single program that describes the behavior of a\nwhole system\, and a compiler projects that global description into local programs run by each\nnode. By making distributed control flow explicit\, choreographic programming can rule out\nimportant classes of errors\, including deadlocks. This dissertation investigates library-level\nchoreographic programming\, an approach that embeds choreographic abstractions in existing\nhost languages rather than implementing them as standalone languages. The central claim\nis that the library approach can retain the safety and global reasoning principles of chore-\nographic programming while taking advantage of the host language’s features\, tools\, and\necosystem. First\, we present HasChor\, a first-of-its-kind library-level choreographic program-\nming language in Haskell\, built using freer monads. Next\, we generalize the design underlying\nHasChor to algebraic effects\, giving library-level implementations in Agda and OCaml. Fi-\nnally\, we present Parkour\, a backward-compatible extension to HasChor that adds a construct\nfor expressing parallel behavior in choreographies. Together\, these systems show that chore-\nographic programming can be implemented\, generalized\, and extended at the library level\,\nmaking global programming techniques available within practical host-language settings. \nEvent Host: Gan Shen\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Lindsey Kuper  \nZoom: https://ucsc.zoom.us/j/93790633483?pwd=Jg8JlISsrwjLBaQIi1KdHk36bNMIv7.1 \nPasscode: 902041 \n  URL:https://events.ucsc.edu/event/shen-g-cse-library-level-choreographic-programming/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260605T180000 DTEND;TZID=America/Los_Angeles:20260605T203000 DTSTAMP:20260605T010229Z CREATED:20260603T203745Z LAST-MODIFIED:20260605T010229Z UID:10014908-1780682400-1780691400@events.ucsc.edu SUMMARY:Sluggers of the Lost Goal: Mud\, Slime\, and World Cup Madness! DESCRIPTION:Watch robots compete in the Mechatronics Public Demo! Cheer on their sleep-deprived creators as they run their ‘bots through the field.Thrill to contest between autonomous robots navigating the field and score points by shooting ping-pong balls at each other and scoring the Golden Goal! Come see this exciting Sluggers of the Lost Goal competition! \nThe Mechatronics class is having their public demonstration of their final design project\, Sluggers of the Lost Goal: Mud\, Slime\, and World Cup Madness! \, Friday June 5th\, 2026 at 6:15 PM in the UCSC Kresge Auditorium. \nIn this thrilling competition\, teams from UCSC’s Mechatronics course will pit their autonomous robots against each other in an epic Sluggers of the Lost Goal Competition. Each robotic agent will navigate the field\, shoot ping pong balls at each other\, hide behind obstacles\, and try to score the golden goal. The champions will compete in a wild head to head tournament\, until one robot emerges victorious! The Public is welcome (and it is free)! \nThe public is invited (you might have to duck a few ping pong balls) and the teams will be on hand to explain their designs to one and all. Come see what these students have accomplished in 10 weeks and cheer on the competition. \nThere will be a live webcast starting at 6PM: www.twitch.tv/elkaim_ucsc (we will try\, might not work) \nFeel free to forward this to any and all that might be interested\, children (future engineers) especially welcome. URL:https://events.ucsc.edu/event/sluggers-of-the-lost-goal-mud-slime-and-world-cup-madness/ LOCATION:Kresge College\, R-3 Suites\, Santa Cruz\, CA\, 95064 CATEGORIES:Competition,Exhibits,Performances ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/06/SluggersImage.png GEO:36.9977048;-122.0660116 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Kresge College R-3 Suites Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=R-3 Suites:geo:-122.0660116,36.9977048 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260605T132000 DTEND;TZID=America/Los_Angeles:20260605T142500 DTSTAMP:20260529T173530Z CREATED:20260529T173530Z LAST-MODIFIED:20260529T173530Z UID:10014890-1780665600-1780669500@events.ucsc.edu SUMMARY:BME80G Seminar – Sheril Kirshenbaum\, "Science in Policymaking" DESCRIPTION:Please note: Following this lecture\, the Genomics Institute’s Diversity\, Equity\, and Inclusion Committee will host a reception on the Baskin Engineering Lanai with Dr. Kirshenbaum where we can continue the discussion on how to effectively engage lawmakers and the public to value and support genomic science. \nPresenter: Dr. Sheril Kirshenbaum \nAbstract: Science shapes our world\, but meaningful policy engagement and understanding of research and innovation are critical if new advances are to reach their full potential. Dr. Sheril Kirshenbaum will share her research on science in policymaking and reflect on her experiences serving as a science advisor in Congress. The talk will explore effective strategies for engaging policymakers and staff\, countering misinformation\, promoting evidence-based decision-making\, and strengthening the role of science in the policy process. \nAbout the speaker: Dr. Sheril Kirshenbaum is an Emmy Award-winning scientist and author in the Office of Research and Innovation at Michigan State University\, and an assistant professor in the College of Communication Arts and Sciences. Her research explores how senior policymakers in the U.S. government make decisions about science and she has worked in the U.S. Senate with Senator Gary Peters (MI) and Bill Nelson (FL). She also hosts and writes the PBS series Serving Up Science with WKAR about the global food system and its impact on the environment and our health. Kirshenbaum is the author of The Science of Kissing and Unscientific America: How Scientific Illiteracy Threatens Our Future (with Chris Mooney)\, and co-founded the NGO Science Debate. \nHosted by: Professor Karen Miga\, BME Department URL:https://events.ucsc.edu/event/bme80g-seminar-sheril-kirshenbaum-science-in-policymaking/ LOCATION:Jack Baskin Auditorium\, 191 Baskin Cir\, Santa Cruz\, CA\, 95064 CATEGORIES:Lectures & Presentations,Seminars ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/05/Sheril-Kirshenbaum.jpg GEO:37.0001832;-122.0623528 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Auditorium 191 Baskin Cir Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=191 Baskin Cir:geo:-122.0623528,37.0001832 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260605T080000 DTEND;TZID=America/Los_Angeles:20260605T100000 DTSTAMP:20260527T160819Z CREATED:20260527T160819Z LAST-MODIFIED:20260527T160819Z UID:10014878-1780646400-1780653600@events.ucsc.edu SUMMARY:Chen\, Z. (CSE) - GPU Subgroup Semantics for Portable High-Performance Kernels DESCRIPTION:Modern high-performance GPU kernels increasingly rely on subgroup-level execution\, including subgroup-level communication\, subgroup operations\, and matrix operations. These features are essential for workloads such as matrix multiplication and FlashAttention\, but their language-level guarantees remain difficult to reason about. Existing programming models often leave unclear which threads participate in subgroup operations\, when subgroup threads are required to execute together\, and what synchronization is implied by subgroup-level operations. This ambiguity becomes especially important in portable GPU programming\, where the same kernel may run across devices with different subgroup sizes\, compiler stacks\, browser backends\, and hardware execution behavior. \nMy research studies how precise subgroup semantics can support portable and correct high-performance GPU kernels. SIMT-Step\, my main completed work\, develops a formal and flexible operational semantics for GPU subgroup execution. It introduces dynamic blocks to specify converged subgroup execution and subgroup-operation participation\, classifies instructions as independent\, synchronous\, or collective to express a spectrum of candidate subgroup semantics\, and validates these models through a TLA+ implementation and an empirical fuzzing study across real GPUs. My systems work studies how subgroup-dependent kernels behave in practice\, including WebGPU FlashAttention kernels for LLM inference\, tunable WebGPU kernels for performance portability\, and Vulkan-based execution for heterogeneous SoCs. Building on these foundations\, my proposed verification work develops data-race-free checking techniques for ML kernels that rely on subgroup operations and matrix operations. Together\, these projects aim to clarify the execution guarantees that optimized GPU kernels can rely on and to support portable GPU programming systems whose performance and correctness can be reasoned about across diverse hardware. \nEvent Host: Zheyuan Chen\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Tyler Sorensen \nZoom: https://ucsc.zoom.us/j/92175288480?pwd=jGajtqerVbKuW1FPNr3awqOYoxATsp.1&jst=3 \nPasscode: 693354 URL:https://events.ucsc.edu/event/chen-z-cse-gpu-subgroup-semantics-for-portable-high-performance-kernels/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260604T140000 DTEND;TZID=America/Los_Angeles:20260604T153000 DTSTAMP:20260527T164116Z CREATED:20260527T164116Z LAST-MODIFIED:20260527T164116Z UID:10014879-1780581600-1780587000@events.ucsc.edu SUMMARY:Imlau Dagostini\, J. (CSE) - Intent-Driven Orchestration for Scientific Computing DESCRIPTION:The growing complexity of high-performance computing (HPC) systems poses a fundamental challenge for domain scientists\, whose primary objective is to obtain scientifically valid results rather than to optimize resource utilization. Modern leadership-class facilities combine heterogeneous CPUs\, GPUs\, and specialized accelerators across systems that simultaneously support traditional scientific simulations and AI-driven workloads. This creates a vast\, machine-dependent configuration space that even experienced systems researchers find difficult to navigate. In practice\, users must explicitly specify resources\, node counts\, and walltime estimates before submitting jobs to an orchestrator\, resulting in iterative trial-and-error that wastes both human effort and compute resources. \nThis thesis proposes an intent-driven orchestration middleware for scientific computing\, in which domain scientists express high-level computational goals rather than low-level resource parameters\, and the system assumes responsibility for identifying configurations that satisfy those goals efficiently. This thesis proposal builds on a completed study of the computational performance of pangenome mapping\, a representative workload of data-intensive pipelines increasingly common in modern science. We demonstrate that tailoring tuning parameters to specific inputs and architectures yields significant performance improvements while exposing the depth of the configuration search problem that motivates this thesis. We then present an in-progress user-aware\, intent-driven middleware that uses surrogate models to aid this exploration and map high-level goals to suitable configurations. We end this presentation by proposing a cluster-aware orchestrator that enables existing HPC resource managers to support intent-aware decision-making. \nEvent Host: Jessica Imlau Dagostini\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Abel Souza \nZoom: https://ucsc.zoom.us/j/93851280425?pwd=v4ONi9N5UlfZmsMqiI4gSkxFXe0oaX.1 \nPasscode: 835985 \n  URL:https://events.ucsc.edu/event/imlau-dagostini-j-cse-intent-driven-orchestration-for-scientific-computing/ LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png GEO:37.000369;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Engineering Baskin Engineering 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Baskin Engineering 1156 High Street:geo:-122.0632371,37.000369 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260604T130000 DTEND;TZID=America/Los_Angeles:20260604T150000 DTSTAMP:20260526T193652Z CREATED:20260526T193652Z LAST-MODIFIED:20260526T193652Z UID:10014872-1780578000-1780585200@events.ucsc.edu SUMMARY:Lietz\, R. (CM) - Reflecting on Failure: Designing and Evaluating Archetype Profiles as a Tool for Self-Reflection DESCRIPTION:Self-reflection holds significant potential for learning\, behavior change\, and emotional processing\, yet designing technologies that effectively support it remains challenging\, particularly when reflection involves difficult experiences such as failure. Most current technologies avoid negative experiences altogether\, leaving users without support at precisely the moments when reflection could be most valuable.\nThis dissertation investigates how technology can better support self-reflection through three mixed-methods studies. The first examines how people experience and reflect on failure\, revealing how identity\, self-blame\, and emotional avoidance create barriers to productive reflection. These findings informed an iterative design process through which archetype profiles emerged as a promising reflective format. The second study evaluated archetype profiles against standard graph-based visualizations\, finding that the quiz-profile sequence effectively scaffolded reflection by supporting emotional re-engagement followed by cognitive reframing. The third study extended this work into a collaborative context\, examining archetype profiles derived from sleep tracking data as shareable artifacts for social reflection. Across these studies\, this dissertation contributes empirical insights into reflection on failure and design knowledge about archetype profiles as a reflective format. \nEvent Host: Rebecca Lietz\, Ph.D. Candidate\, Computational Media \nAdvisor: Steve Whittaker \nZoom: https://ucsc.zoom.us/j/7855885795?pwd=RS9mWXhQOXNyNmRVSzQrd1MzamJVQT09 \nPasscode: 172404 URL:https://events.ucsc.edu/event/lietz-r-cm-reflecting-on-failure-designing-and-evaluating-archetype-profiles-as-a-tool-for-self-reflection/ LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg GEO:37.3796975;-121.9765484 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Silicon Valley Campus 3175 Bowers Avenue Santa Clara CA 95054 United States;X-APPLE-RADIUS=500;X-TITLE=3175 Bowers Avenue:geo:-121.9765484,37.3796975 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260604T114000 DTEND;TZID=America/Los_Angeles:20260604T133000 DTSTAMP:20260601T153627Z CREATED:20260601T153627Z LAST-MODIFIED:20260601T153627Z UID:10014894-1780573200-1780579800@events.ucsc.edu SUMMARY:BME 280B Seminar: Accelerating the diagnosis of rare diseases using multi-omics DESCRIPTION:Presenter: Stephen Montgomery\, Endowed Professor of Pathology\, Genetics\, Biomedical Data Science\, Computer Science\, Stanford University \n  \nDescription: N/A \n  \nBio: Stephen Montgomery is an Endowed Professor of Pathology\, Genetics\, Biomedical Data Science and\, by courtesy\, Computer Science at Stanford University. He has trained in multiple countries including Canada\, Germany\, England\, and Switzerland. He is best known for his work mapping the effects of genetic variation to gene expression and authored the first publications that compared whole genomes and transcriptome data within a human population and pioneered the use of molecular outliers to identify impactful rare variants (Montgomery et al\, 2010\, Montgomery et al\, 2011). \nHosted by: Professor Karen Miga\, BME Department URL:https://events.ucsc.edu/event/bme-280b-seminar-accelerating-the-diagnosis-of-rare-diseases-using-multi-omics/ LOCATION:Biomedical Sciences\, Biomedical Sciences Building Red Hill Road\, Santa Cruz\, CA\, 95064 CATEGORIES:Lectures & Presentations,Seminars ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/06/2024-02-07-E2-Caribe-Royale-107-scaled.jpg GEO:36.999785;-122.061118 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Biomedical Sciences Biomedical Sciences Building Red Hill Road Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Biomedical Sciences Building Red Hill Road:geo:-122.061118,36.999785 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260604T100000 DTEND;TZID=America/Los_Angeles:20260604T120000 DTSTAMP:20260528T203838Z CREATED:20260528T203838Z LAST-MODIFIED:20260528T203838Z UID:10014885-1780567200-1780574400@events.ucsc.edu SUMMARY:Okamoto\, F. (BMEB) - Improving read-to-pangenome alignment in complicated genomic regions DESCRIPTION:Many genetics pipelines start by aligning sequencing reads to a reference genome. Aligners attempt to find the position in the reference sequence which best matches the read sequence\, but this breaks down when the reads come from a sample with variation relative to the reference. A proposed alternative\, pangenome graphs\, is supposed to fix such “reference bias” by including known variation within the reference itself. Yet read alignment is still difficult in graph regions featuring certain complex variation. I will address specific known limitations of pangenome read alignment by developing better methods to align reads to pangenomes (1) in centromeres\, (2) in regions with cycles\, (3) when a “split”/supplementary alignment is required\, and (4) for RNA-seq reads. \nEvent Host: Faith Okamoto\, Ph.D. Student\, Biomolecular Engineering & Bioinformatics \nAdvisor: Benedict Paten \nZoom: https://ucsc.zoom.us/j/3543092299?pwd=5xbPfPhxvoJlx24tusiOwPuLSjzwzb.1 \nPasscode: 767376 URL:https://events.ucsc.edu/event/okamoto-f-bmeb-improving-read-to-pangenome-alignment-in-complicated-genomic-regions/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260604T100000 DTEND;TZID=America/Los_Angeles:20260604T120000 DTSTAMP:20260512T171434Z CREATED:20260512T161057Z LAST-MODIFIED:20260512T171434Z UID:10014625-1780567200-1780574400@events.ucsc.edu SUMMARY:Kordonowy\, S. (CS) - The Role of Circuits in Near-Term Quantum Computation DESCRIPTION:As quantum computing transitions from theory to practice\, understanding which algorithms suit near-term devices becomes critical. Current quantum computers are severely constrained by limited qubit counts\, short coherence times\, and high error rates that quickly degrade computation into noise. This thesis addresses two interconnected questions: what non-trivial computational tasks can near-term devices execute and how should algorithms be implemented to exploit available hardware? We examine circuit design as the bridge between these concerns\, analyzing how gate choices determine algorithmic efficiency and computational hardness. By deriving explicit circuit constructions\, we obtain tangible cost estimates for practical quantum computation\, enabling precise comparisons to classical approaches and identification of break-even points in system size and error rates. Understanding these trade-offs is essential for near-term quantum computing\, where experiments are expensive and error-prone. \nWe apply these ideas to three domains:\n1. Streaming: we provide circuit implementations for the Boolean Hidden Matching problem\, a combinatorial problem which exhibits exponential space separation compared to classical algorithms. We give explicit resource estimates and experimentally validate on Quantinuum’s trapped-ion hardware. We demonstrate that quantum advantage persists even when accounting for error correction overhead. \n2. Variational eigensolving: We examine how gate set choices influence trainability of variational quantum eigensolvers and provide Lie algebraic decompositions for differing gate sets. These decompositions are in turn used as a warm-starting heuristic to overcome barren plateaus\, a common problem in quantum machine learning tasks\, and improve convergence. We apply this technique to three combinatorial problems with primary focus on portfolio optimization. \n3. Cryptography: We develop a digital signature scheme based on circuit learning hardness and classical shadows. Error detection plays a direct role in the circuits considered\, with a focus on practical implementation for near-term devices. \nThese case studies demonstrate how careful circuit design can either mitigate near-term\nconstraints or expose where error correction becomes necessary to achieve quantum\nadvantage. \n  \nEvent Host: Steven Kordonowy\, Ph.D. Candidate\, Computer Science  \nAdvisor: Alexandra Kolla  \nZoom: https://ucsc.zoom.us/j/9524731001?pwd=MzdrNmhidVBsTXNFbktBcjEvNmZIQT09&omn=96338496668  \nPasscode: J29XGi \n  \n  URL:https://events.ucsc.edu/event/kordonowy-s-cs-the-role-of-circuits-in-near-term-quantum-computation/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260604T093000 DTEND;TZID=America/Los_Angeles:20260604T113000 DTSTAMP:20260526T174336Z CREATED:20260526T174336Z LAST-MODIFIED:20260526T174336Z UID:10014869-1780565400-1780572600@events.ucsc.edu SUMMARY:Xie\, Y. (CM) - Crop Circles of Play: Forces and Formation in the Dyadic Magic Circle DESCRIPTION:Cooperative two-player play produces distinctive social experiences between players: intimacy\, trust\, cooperation\, communitas. Since Huizinga\, the frame within which these experiences arise has been called the Magic Circle: a temporarily-set-apart space through which play does its social work. It has been a central organizing concept across game studies\, performance theory\, and HCI because it points to a basic human capacity: the way play transforms activity that\, on its own\, would mean nothing into shared experiences of intimacy\, trust\, and communitas. Yet a century on\, after generations of theoretical elaboration and equally vigorous contestation\, the Magic Circle remains theoretically rich but empirically elusive\, invoked by Huizinga\, Goffman\, Stenros\, and others but never located in observable interaction. Locating it empirically would let us observe what shapes any given Magic Circle and how that shape develops over the course of play: the game itself\, each player’s prior experience with games and streams\, the histories they bring to each other\, and whatever else is pressing on the shared frame. It would help explain why two dyads playing the same game produce different experiences\, a particular concern for educational games\, serious games\, and art games that aim to deliver a specific message or outcome to players. This proposal argues that the dyadic Magic Circle becomes observable when two players meet over a shared game and must negotiate their individual senses of “what this play is” into a shared frame. It treats this negotiated frame as a Crop Circle: a pattern pressed into recorded interaction by forces (player pulls\, designer prescriptions\, external audiences)\, reconstructable through close multimodal reading. The proposal therefore asks: where\, in the recorded interaction of dyadic play\, can the negotiated Magic Circle be caught taking shape\, and what does its observable form reveal about how a designed game becomes a lived experience between two people? \nThis proposal examines the dyadic Magic Circle through five connected studies. Study 1 conducts a PRISMA systematic review of two-player game scholarship in the ACM Digital Library\, showing that the field has already documented Magic Circle phenomena and closely related interactional dynamics without naming them as such. Study 2 applies Interaction Analysis (Jordan and Henderson\, 1995) to publicly available stream footage of two-player cooperative gameplay performed for an external audience. Study 3 conducts a controlled lab study of dyadic cooperative gameplay\, using multimodal recording and post-session stimulated recall to capture the negotiated Magic Circle under private play conditions. Study 4 conducts a comparative reading of the Study 2 and Study 3 corpora to examine how the audience-versus-private frame\, as an external force\, imprints on the dyadic Magic Circle. Finally\, Study 5 reads across Studies 1-4 to identify what gives the Magic Circle its “magic”: the configurations of force and trace that produce the distinctive social experiences a century of play scholarship has been chasing\, and to articulate “design for the Magic Circle\, not for the experience” as a generative principle for cooperative game design. \nEvent Host: Yi Xie\, Ph.D. Student\, Computational Media \nAdvisor: Elin Carstensdottir \nZoom: https://ucsc.zoom.us/j/94258671135?pwd=qEkTZAQKI5avLf060hOycY1hgER2tX.1 \nPasscode: 650205 URL:https://events.ucsc.edu/event/xie-y-cm-crop-circles-of-play-forces-and-formation-in-the-dyadic-magic-circle/ CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg LOCATION: END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260603T150000 DTEND;TZID=America/Los_Angeles:20260603T180000 DTSTAMP:20260602T193539Z CREATED:20260602T193539Z LAST-MODIFIED:20260602T193539Z UID:10014898-1780498800-1780509600@events.ucsc.edu SUMMARY:Xu\, D. (BMEB) - Interplay Between CENP-A\, DNA Methylation\, and H3K9me3 in Defining Centromere Identity DESCRIPTION:Centromeres ensure proper chromosome segregation during cell division\, yet the organization and regulation of centromeric chromatin within satellite DNA arrays remain incompletely understood. Here\, we leverage the complete diploid human genome benchmark (T2T-HG002) to provide a detailed study of centromeric sequence and chromatin architecture on individual haplotypes. Using adaptive-sampling-enriched\, ultra-long-read DiMeLo-seq\, we achieve single-molecule chromatin profiling across all centromeres\, revealing that along single chromatin fibers\, CENP-A\, the histone variant specifying centromere identity\, forms multiple discrete subdomains within hypomethylated centromere dip regions (CDRs) that are flanked by H3K9me3-enriched heterochromatin. Despite underlying sequence variation\, CDRs localize to sequence-homogeneous domains and maintain relatively balanced CENP-A dosage and aggregate length across all chromosomes and between haplotypes. Further\, we show that bidirectional changes to centromeric and pericentromeric DNA methylation are accompanied by changes to centromeric chromatin architecture. In passaged cells with centromeric hypomethylation\, subdomain boundaries are eroded\, and adjacent CENP-A domains tend to merge and expand. Conversely\, in pluripotent stem cells with centromeric hypermethylation\, CDRs are fundamentally reorganized\, such that discrete hypomethylated domains are frequently consolidated into broader contiguous tracts. These methylation-associated CDR restructuring events suggest that DNA methylation acts as a principal regulator of human centromere organization\, with implications for understanding centromere plasticity\, epigenetic inheritance\, and chromosomal instability in development and disease. \nEvent Host: Daniel Xu\, PhD Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Karen Miga \nZoom: https://ucsc.zoom.us/j/99197563825?pwd=meEWoi4ffdZ0K4Syo09Jr0ZbpPThMk.1 URL:https://events.ucsc.edu/event/xu-d-bmeb-interplay-between-cenp-a-dna-methylation-and-h3k9me3-in-defining-centromere-identity/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260603T110000 DTEND;TZID=America/Los_Angeles:20260603T121500 DTSTAMP:20260529T172740Z CREATED:20260529T172740Z LAST-MODIFIED:20260529T172740Z UID:10014889-1780484400-1780488900@events.ucsc.edu SUMMARY: DESCRIPTION:Presenter: Sai Teja Peddinti\, Google \nAbstract: As the digital landscape expands\, traditional models of threat mitigation and user support are failing to keep pace with the unprecedented security\, privacy\, and safety challenges. Fortunately\, the rise of large language models (LLMs) offers a powerful new paradigm for defense. This talk explores how LLMs are being leveraged to improve digital privacy\, security\, and safety from the network layer down to the individual user. We will examine how LLMs are opening new frontiers in cybersecurity and solving complex challenges\, such as: inferring device identities through semantic analysis of network traffic\, mapping global privacy trends by distilling over a decade of app reviews\, and analyzing user help-seeking behaviors across millions of social media interactions. Ultimately\, this talk will demonstrate how AI is evolving from a technological novelty into an essential foundation for scalable\, proactive\, and human-centric digital defense. \nBio: Sai Teja Peddinti (https://www.saitejapeddinti.com) is a Staff Research Scientist at Google\, where his research focuses on the intersection of Privacy\, Security\, Artificial Intelligence\, and Data Mining. His research employs a multidisciplinary approach\, blending qualitative and quantitative methods to investigate user and developer privacy preferences and translate those insights into scalable privacy/security features using LLMs and large-scale data analysis. Sai Teja holds a Ph.D. in Computer Science from the NYU Tandon School of Engineering (2014). His research has garnered industry recognition\, including the IAPP SOUPS Privacy Award and finalist placements in major applied research competitions. Throughout his education\, he has been honored with numerous accolades. \nHosted by: Professor Ram Sundara Raman \nDate and Time: Wednesday\, June 3\, from 11:00 am – 12:15 pm \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3 URL:https://events.ucsc.edu/event/12348/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Lectures & Presentations,Seminars ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/03/BElogoWHITE.png GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260603T090000 DTEND;TZID=America/Los_Angeles:20260603T110000 DTSTAMP:20260529T161208Z CREATED:20260529T161208Z LAST-MODIFIED:20260529T161208Z UID:10014887-1780477200-1780484400@events.ucsc.edu SUMMARY:Morey\, C. (BMEB) - Innovations in Interdependence: Genomic and Functional Evolution in Invertebrates and Their Intracellular Symbionts DESCRIPTION:Intracellular symbionts are microorganisms\, such as bacteria\, that live within host cells. These associations are widespread throughout the invertebrate tree of life\, and can perform a diversity of key metabolic\, immune-response\, or other functions that the host is dependent on for survival or reproduction. Intracellular symbioses allow both the host and the symbiont to occupy new ecological niches\, and thus can have profound impacts on their evolution. Recent and rapid growth of available sequencing data provides new opportunities to investigate the genomic alterations underpinning functional and morphological changes during the evolution of these relationships\, and how they reshape both host and symbiont biology. \nHere\, I propose investigating unique mechanisms of genomic innovation across three levels of host-symbiont evolution: symbiont genome evolution\, host-symbiont regulatory co-evolution\, and host genome evolution. In aim 1\, I will investigate how mobile genetic elements drive episodic genome expansion and functional innovation in obligate chemosynthetic symbionts of deep-sea clams\, further challenging the notion that reductive genome evolution is an inevitable or linear fate for host-restricted lineages. In aim 2\, I will explore the potential for symbiont-derived small-RNA molecules to participate in cross-kingdom gene regulation of their hosts across a diversity of host-symbiont systems using publicly available genome and RNA-sequencing data. In aim 3\, I will explore the convergent evolution of gut loss across independently derived marine bivalve lineages that depend nutritionally on chemosynthetic symbionts\, identifying host genomic changes associated with the transition to a symbiotic lifestyle. Together\, these aims leverage the expanding wealth of genomic data to illuminate how host-symbiont relationships reshape the genomes of both partners and generate novel adaptations across evolutionary time. \nEvent Host: Camryn Morey\, Ph.D. Student\, Biomolecular Engineering & Bioinformatics \nAdvisor: Shelbi Russell and Russ Corbett-Detig \nZoom: https://ucsc.zoom.us/j/92296748824?pwd=kabPBvby5xZbAHBbxBX6IIHNka8sLX.1 \nPasscode: 153631 URL:https://events.ucsc.edu/event/morey-c-bmeb-innovations-in-interdependence-genomic-and-functional-evolution-in-invertebrates-and-their-intracellular-symbionts/ LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png 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:20260602T140000 DTEND;TZID=America/Los_Angeles:20260602T160000 DTSTAMP:20260527T204156Z CREATED:20260527T204156Z LAST-MODIFIED:20260527T204156Z UID:10014880-1780408800-1780416000@events.ucsc.edu SUMMARY:Bose\, S. (ECE) - Learning-Augmented Optimization\, Control\, and Inference in Modern Power Systems DESCRIPTION:The electric grid is essential to modern society\, and recent developments such as renewable energy sources (RESs)\, battery energy storage systems (ESSs)\, and microgrids (MGs) have necessitated novel computational methods for planning and operations. Machine learning offers a promising lever here\, both as an accelerator for and proxy to traditional optimization-based problems. In this thesis\, we consider learning-based algorithms for three such problems: load restoration in islanded microgrids\, accelerated optimal power flow\, and short-term load forecasting. \nWe first address load restoration of islanded MGs containing RESs\, battery ESSs\, microturbines\, and inverter-based devices. We formulate the problem as a multi-timestep nonconvex optimization and decompose it via model predictive control (MPC). We develop novel convex relaxations of the nonconvex constraints\, including power flow\, ESS charge/discharge complementarity\, and inverter voltage-reactive power relations\, to generate approximately feasible solutions\, and then improve on them via a reinforcement learning method based on constrained policy optimization (CPO) that respects the original nonconvexity. \nWe then turn to accelerating convexified optimal power flow (C-OPF) via constraint screening\, presenting an analysis that reduces screening for certain C-OPF families to a rank-based test. Building on this\, we introduce Mixture of Gradient Experts (MoGE)\, an architecture that learns optimal dual variables from historical C-OPF solutions and combines them with the KKT conditions to eliminate likely non-binding constraints\, with a recovery step that guarantees the reduced problem’s solution matches the original’s. We demonstrate speedups on grids with up to 10\,000 buses. \nFinally\, we consider short-term load forecasting (STLF) from smart-meter data\, motivated by the role of forecasts as inputs to the optimization problems above. To address consumer-data privacy and the heterogeneity of consumption patterns\, we introduce personalization layers for federated learning (PL-FL)\, in which each client trains a model with a local personalized component and a shared aggregated component\, and extend it to a privacy-preserving variant (PPFL) that applies differential privacy to the shared component. Separately\, we present an empirical study of forecasting architectures spanning classical recurrent networks to fine-tuned time-series foundation models\, holding dataset size and parameter count constant to isolate architectural contribution. All methods are evaluated on subsets of the NREL ComStock dataset. \nEvent Host: Shourya Bose\, Ph.D. Candidate\, Electrical & Computer Engineering  \nAdvisor: Yu Zhang \nZoom: https://ucsc.zoom.us/j/93511298189?pwd=eAyDKdMirlVqYGUsbhQCccoBM9gDV6.1 \nPasscode: 462014 URL:https://events.ucsc.edu/event/bose-s-ece-learning-augmented-optimization-control-and-inference-in-modern-power-systems/ LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png GEO:37.0009723;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260602T134500 DTEND;TZID=America/Los_Angeles:20260602T153000 DTSTAMP:20260529T163203Z CREATED:20260529T163203Z LAST-MODIFIED:20260529T163203Z UID:10014888-1780407900-1780414200@events.ucsc.edu SUMMARY:Figuerres\, S. (ECE) - Ion Transport Mechanisms for Bioelectronics DESCRIPTION:Ion transfer as the movement of charged species across spaces and interfaces is the basis of signaling in nearly all biological systems. My research is grounded in the idea that precise control over ion transfer enables direct manipulation of biological function. Specifically\, I focus on how ion transport can be engineered to regulate both collective behavior in microbial communities\, as well as cellular sensing through ion channels. In comparison to traditional means such as passive diffusion\, mediated ion transfer via ion pumps and ion channels creates opportunity for high precision control of biological signaling. My work centers on ion transfer as a fundamental mechanism for biological signaling and control across systems. Using bioelectronic ion pumps and mechanosensitive ion channels to precisely manipulate the movement of charged species\, I aim to investigate ion transfer at the interface of biology and electronics. \nEvent Host: Sydnie Figuerres\, Ph.D. Student\, Electrical & Computer Engineering  \nAdvisor: Marco Rolandi URL:https://events.ucsc.edu/event/figuerres-s-ece-ion-transport-mechanisms-for-bioelectronics/ LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064 CATEGORIES:Ph.D. Presentations ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg GEO:37.000369;-122.0632371 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Engineering Baskin Engineering 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Baskin Engineering 1156 High Street:geo:-122.0632371,37.000369 END:VEVENT END:VCALENDAR