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DTSTART;TZID=America/Los_Angeles:20260526T070000
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DTSTAMP:20260601T192714
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SUMMARY:Chou\, Y. (CM) - Exploring Future AI-Mediated Health Creator–Audience Interactions on Social Media: Transparency\, Care\, and Accountability
DESCRIPTION:Health and wellness content creators play an important role in shaping how people receive and engage with health information on social media. Beyond delivering information\, they also convey care\, build trust\, and sustain relationships with audiences. As generative AI (GenAI) becomes increasingly integrated into creator work\, existing research has examined AI disclosure\, AI-mediated communication\, and health communication more broadly\, but less is known about how AI should be integrated into health creator–audience interactions\, where informational support\, emotional care\, accountability\, and relational meaning are often intertwined. My dissertation examines AI-mediated health creator–audience interaction through four connected studies. Study 1 used mock-up interfaces and semi-structured interviews with 16 Instagram users who interact with health and wellness creators to examine audience perceptions of GenAI use disclosure. Study 2 conducts co-design sessions with social media health creators to explore how creators might communicate human labor and personal contribution in a future social media environment where AI-generated content is widespread. Study 3 extends the focus to audience-invoked AI in public comment sections by scraping and analyzing comment data from platfrom X\, examining how audiences invoke AI agents through @-mentions in response to health creator posts\, and how these public AI invocations may shape information credibility\, accountability\, community discussion\, and social dynamics. Finally\, Study 4 will synthesize insights from the first three studies and translate them into interactive prototypes. By examining how audiences and health creators interact with these prototypes\, this study will explore future forms of AI-mediated health creator–audience interaction and broader community engagement on social media. \n  \nEvent Host: Yuling Ruby Chou\, Ph.D. Student\, Computational Media \nAdvisor: Christina Chung \nZoom: https://ucsc.zoom.us/j/94127645445?pwd=dmlMkwbknDZE9pbklAC9jhwDTZPbVL.1 \nPasscode: 190739
URL:https://events.ucsc.edu/event/chou-y-cm-exploring-future-ai-mediated-health-creator-audience-interactions-on-social-media-transparency-care-and-accountability/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260526T093000
DTEND;TZID=America/Los_Angeles:20260526T113000
DTSTAMP:20260601T192714
CREATED:20260519T162948Z
LAST-MODIFIED:20260519T162948Z
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SUMMARY:Weber\, Z. (ECE) - Sustainable Bioinspired Polymer–Mineral Composites for Adaptable Repair in Conservation Applications
DESCRIPTION:Every year\, tens of thousands of tons of plaster-based materials are used in restoration and conservation applications\, many of which are derived from non-renewable sources and discarded at the end of their service life. Here\, we introduce a biodegradable\, bio-derived composite based on chitosan and calcium carbonate that is composed of simple\, widely available constituents and designed for adaptable repair applications. By varying polymer molecular weight\, concentration\, and mineral content\, the composite can be formulated to span injectable\, paste-like\, and putty-like behaviors\, enabling accommodation of diverse structural filling and stabilization needs. We examine relationships between composition\, flow behavior\, and mechanical performance through rheological characterization of the wet composite and measurements of bulk density\, porosity\, and compressive strength in the hardened state. Rather than targeting a single optimized formulation\, this work demonstrates a tunable material platform in which relationships between composition\, flow behavior and mechanical performance guide selection of material behavior based on application requirements. Future applications of this approach include sustainable repair and conservation materials for exhibits\, architectural restoration\, and other contexts where adaptable handling\, mechanical integrity\, and biodegradability are desired. \nEvent Host: Zoë Weber\, Ph.D. Student\, Electrical & Computer Engineering  \nAdvisor: Marco Rolandi \nZoom: https://ucsc.zoom.us/j/96509847894?pwd=Q5w4oFaXQQD4rbEehZHxuevh12Piar.1 \nPasscode: 324003
URL:https://events.ucsc.edu/event/weber-z-ece-sustainable-bioinspired-polymer-mineral-composites-for-adaptable-repair-in-conservation-applications/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260526T100000
DTEND;TZID=America/Los_Angeles:20260526T110000
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CREATED:20260518T185313Z
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SUMMARY:Harsh\, B. (CSE) - SUPERSCALAR\, MULTIPLE TAKEN BRANCH PREDICTOR
DESCRIPTION:This work addresses improvements in branch prediction mechanism to support high perfor-\nmance processors. The state of the art aims to balance the prediction latency and prediction\naccuracy using multi level correcting predictors [27]. Prior published work focusses on scalar\ndesigns and prediction accuracy improvement for hard to predict branches employing tailor\nmade\, non generic and non transferrable solutions [8]. Recent work also proposes ahead pre-\ndiction [42–44] to solve the problem of low accuracy of L0 predictor. \nThis work proposes efficent\, generic and transferrable solutions to reduce mispredic-\ntions and to use the fetch bandwidth more efficiently. This includes a biased overriding multi-\nlevel hierarchy with three predictor levels (L0\, L1\, L2). L0 uses a High-Confidence-Only Taken\n(HOTP) predictor that only predicts high-confidence taken control-flow instructions. This work\nfurther uses L1-L2 biased training to decrease mispredictions by L2 while it trains on branches\non which L1 has reached high confidence. This work proposes a superscalar predictor built\nusing the state of the art scalar predictor. Superscalar predictor is implemented by sizing a su-\nperscalar TAGE variant (BATAGE) using Optuna-based search. with varying table sizes and\naspect ratios. The work further proposes a branch predictor frontend design (nTakenBP) to de-\nliver multiple taken branch predictions per cycle. Unlike prior work\, nTakenBP achieves this by\nextending the existing BTB and TAGE tag-comparison logic rather than deepening lookahead. \n  \nEvent Host: Bhawandeep Singh Harsh\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Jose Renau \nZoom: https://ucsc.zoom.us/j/4166778865?pwd=cS9NcnVjRjArYlRRcDcrY3d5N0ZKQT09
URL:https://events.ucsc.edu/event/harsh-b-cse-superscalar-multiple-taken-branch-predictor/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260526T103000
DTEND;TZID=America/Los_Angeles:20260526T123000
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SUMMARY:Castro\, S. (CSE) - Agentic AI for Security: Adversarial Foundations for Autonomous Cyber Operations
DESCRIPTION:Autonomous Cyber Operations (ACO) agents promise effective security automation with minimal human intervention\, yet their deployment raises three interconnected challenges: agents must be realistic (reproducing diverse attacker sophistication)\, secure (preventing autonomy from becoming an attack surface)\, and feasible (safely replicating human behavior at full autonomy). \nWe argue that these three properties are requirements for ACO agents. Existing approaches do not address them together and lack diverse adversarial coverage\, formal threat models for attacks against the agents themselves\, and systematic evaluation of multi-agent topologies. \nWe advance all three ACO properties: (1) For realism\, we establish adversarial foundations by discovering Windows OS vulnerabilities and releasing two exploits reliable across XP through 11. (2) For security\, we formalize ACO meta-attacks and meta-defenses\, propose the first invariant-based Meta-IDS detecting both sensor and actuator meta-attacks\, and introduce the first hybrid LLM–RL ACO integration for defense with a novel inter-agent communication protocol. (3) For feasibility\, we present MaLO\, the first dynamic-topology multi-agent ACO system\, achieving a 78.6\% success rate across a new 42-task security benchmark and solving operations up to 40× faster than human experts. We further propose the Security Operation Complexity Index (SOCX) classification and the T×V×O taxonomy as the first objective-driven evaluation methodology for coding-agent attacks. \nTogether\, these contributions demonstrate that ACO agents can match real-world adversarial sophistication\, resist meta-attacks\, and outperform human operators on complex security tasks. Open challenges remain in adaptive adversaries\, LLM–RL co-training\, dynamic topology selection\, and deployment beyond simulated environments. \n  \nEvent Host:  Sebastián R. Castro\, PhD Candidate\, Computer Science & Engineering \nAdvisor: Alvaro A. Cárdenas \nZoom: https://ucsc.zoom.us/j/2267557290?pwd=S0dNTTV3emZGUzlqV3dLbTg3a0NFUT09&omn=92791061627 \nPasscode: G20c06
URL:https://events.ucsc.edu/event/castro-s-cse-agentic-ai-for-security-adversarial-foundations-for-autonomous-cyber-operations/
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:20260526T110000
DTEND;TZID=America/Los_Angeles:20260526T130000
DTSTAMP:20260601T192714
CREATED:20260515T173857Z
LAST-MODIFIED:20260515T174024Z
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SUMMARY:Liu\, P. (CM) - Reimagining Workplace Concern Reporting: From Emotional Harm to Co-Designed Futures
DESCRIPTION:Workplace concern reporting infrastructure\, including human resources (HR) portals\, grievance procedures\, and whistleblower hotlines\, is the formal channel through which employees in most organizations raise concerns about harassment\, discrimination\, and retaliation. Yet existing research consistently finds that these systems fail the employees they are meant to protect: reports stall\, concerns get filtered\, retaliation occurs\, and marginalized employees face disproportionate risk. This dissertation examines workplace concern reporting as relational\, emotional\, and processual rather than procedural and discrete\, and pursues this account through three studies. Study 1\, drawing on semi-structured interviews with 12 HR professionals and 10 employees in California\, develops the concept of emotional re-victimization to describe how reporting infrastructure produces additional harm at multiple stages of the reporting process. Study 2 returns to the same corpus with a different theoretical lens to develop the concept of buffer spaces: intermediary practices through which employees navigate the gap between informal sense-making and formal escalation. Study 3 will move the dissertation from diagnostic to practical work in two phases. Phase 1 uses speculative co-design with employees and HR professionals to surface what each group would build if they could redesign concern reporting infrastructure together. Phase 2 translates design directions from Phase 1 into prototypes\, iterated with participants across both groups to develop design artifacts that have been shaped by the people who would use them. The dissertation as a whole moves from documenting harm\, through identifying workarounds\, to imagining redesign\, contributing to HCI/CSCW scholarship on workplace technology\, labor studies on employee voice and accountability\, and methodological work on cross-stakeholder speculative design. \nEvent Host: Peiyao Liu\, Ph.D. Student\, Computational Media \nAdvisor: Norman Makoto Su \nZoom: https://ucsc.zoom.us/j/99335305923?pwd=xP6QlNwzobLNQqnCxG3muuZD36C4rn.1 \nPasscode: 946352 \n 
URL:https://events.ucsc.edu/event/liu-p-cm-reimagining-workplace-concern-reporting-from-emotional-harm-to-co-designed-futures/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T090000
DTEND;TZID=America/Los_Angeles:20260527T110000
DTSTAMP:20260601T192714
CREATED:20260515T163555Z
LAST-MODIFIED:20260515T163555Z
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SUMMARY:Baskaran\, D. (CM) - More than Just Fun: Exploring Meaningful Play\, Communities of Play\, and Relatedness of Play
DESCRIPTION:Play is often seen as a form of entertainment\, leisure\, or childhood development. However\, it also acts as a meaningful experience that shapes how people connect with others and interact with the world around them throughout their lives. Prior work on meaningful play and communities of play has mainly focused on individual experiences and participation\, giving less attention to how meaning is socially co-constructed through playful interactions and to how these experiences contribute to relatedness\, or the human need to feel connected to and belong with others\, across physical\, digital\, and hybrid environments. \nUsing qualitative methods\, this dissertation proposal explores how meaningful play is collectively constructed within communities of play and how it shapes relatedness among members. This work positions meaningful play as a socially and technologically embedded relational phenomenon rather than solely an individual experience. Across case studies of PlayStation trophy hunting\, Pokémon Nuzlocke\, LEGO\, and theme park communities of play\, this research explores how meaningful play within these communities contributes to relatedness among members. Ultimately\, this dissertation proposal aims to advance a more holistic understanding of play as a process through which people build shared meaning\, connection\, and belonging in increasingly digital and hybrid social spaces. \n  \nEvent Host: Derusha Baskaran\, Ph.D. Student\, Computational Media \nAdvisor: Kathryn Ringland \n  \nZoom: https://ucsc.zoom.us/j/96290198842?pwd=xtoEw1aIa2fciTbhr6eB9s3PqbWGdF.1 \nPasscode: 404425
URL:https://events.ucsc.edu/event/baskaran-d-cm-more-than-just-fun-exploring-meaningful-play-communities-of-play-and-relatedness-of-play/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T090000
DTEND;TZID=America/Los_Angeles:20260527T110000
DTSTAMP:20260601T192714
CREATED:20260518T163634Z
LAST-MODIFIED:20260518T163634Z
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SUMMARY:Tu\, H. (CSE) - From Evaluation to Adaptation: Building Reliable Multimodal Intelligence
DESCRIPTION:Multimodal large language models (MLLMs) are rapidly becoming general-purpose AI systems\, yet their capabilities are advancing faster than our ability to evaluate\, improve\, and validate their reliability in realistic use. Standard benchmarks mainly measure in-distribution final-answer accuracy\, leaving critical gaps in safety\, robustness\, fine-grained reasoning evaluation\, and reliability in real-world agentic settings. My research proposes an evaluation-to-adaptation framework for building reliable multimodal intelligence: developing rigorous evaluations that expose failures beyond conventional benchmarks\, learning feedback models that guide inference-time reasoning\, and studying how multimodal systems can adapt through experience. We instantiate this agenda through two completed works and two proposed directions. Unicorn evaluates safety and robustness under out-of-distribution and adversarial conditions\, revealing substantial vulnerabilities across 22 vision-language models. ViLBench studies vision-language process reward modeling as both an evaluation challenge and a mechanism for inference-time improvement\, showing that process-guided reasoning selection can improve reliability. Building on these foundations\, we further study test-time experience accumulation and explore reliable multimodal agents for GUI and computer-use tasks. Together\, my research aims to move beyond capability-driven progress alone\, toward multimodal AI systems whose reliability can be evaluated\, improved\, and tested in realistic deployment settings. \nEvent Host: Haoqin Tu\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Cihang Xie \nZoom: 964 1355 0550 \nPasscode: zWxU8A
URL:https://events.ucsc.edu/event/tu-h-cse-from-evaluation-to-adaptation-building-reliable-multimodal-intelligence/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T120000
DTEND;TZID=America/Los_Angeles:20260527T140000
DTSTAMP:20260601T192714
CREATED:20260518T162624Z
LAST-MODIFIED:20260518T162624Z
UID:10014651-1779883200-1779890400@events.ucsc.edu
SUMMARY:Zheng\, Y. (CSE) - Extending eBPF Beyond Kernel Extensions: Verified Interfaces for Runtime System Extensibility
DESCRIPTION:Modern system software increasingly needs runtime extensibility: userspace applications need safe ways to expose domain-specific extension points\, GPU resource management needs workload-specific memory and scheduling policies\, and kernel eBPF JIT compilers need different runtime optimizations as workloads and hardware vary. However\, built-in policies are safe but difficult to specialize across rapidly changing workloads and hardware environments\, limiting efficiency\, while code modifications are flexible but difficult to deploy safely. This dissertation argues that verified eBPF interfaces can turn eBPF from a kernel-extension mechanism into a general substrate for safe runtime extensibility. In this model\, trusted mechanisms expose narrow\, constrained programmable hooks; extensions declare their requirements; verifier-enforced checks preserve safety; and execution remains low-overhead. \nI develop this thesis through three systems spanning userspace applications\, heterogeneous GPU subsystems\, and the kernel eBPF compiler itself. EIM\, implemented in bpftime\, applies verified eBPF interfaces to userspace applications\, allowing application behavior to be extended through explicit constraints and efficient userspace eBPF execution. gpu_ext extends the same idea to heterogeneous systems by exposing programmable resource management hooks for GPU memory and scheduling policy across driver and device. BpfReJIT with kinsn makes the eBPF JIT compiler itself extensible: it enables runtime-guided optimization through dynamic recompilation and extends eBPF bytecode to express diverse hardware capabilities. Together\, these systems show how verified eBPF interfaces can support safe programmability\, separation of policy and mechanisms\, and runtime specialization across applications\, GPU subsystems\, and the kernel JIT infrastructure. \nEvent Host: Yusheng Zheng\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Andi Quinn \nZoom: 504 350 0245 \nPasscode: 521336
URL:https://events.ucsc.edu/event/zheng-y-cse-extending-ebpf-beyond-kernel-extensions-verified-interfaces-for-runtime-system-extensibility/
LOCATION:
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260528T110000
DTEND;TZID=America/Los_Angeles:20260528T120000
DTSTAMP:20260601T192714
CREATED:20260522T165248Z
LAST-MODIFIED:20260522T165248Z
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SUMMARY:Oh\, S. (CSE) - Efficient Instruction Supply for Datacenter Processors
DESCRIPTION:Modern datacenter CPUs lose 25–66% of execution cycles to instruction-delivery stalls. This bottleneck persists\, despite the recent trend towards accelerators and GPUs\, as there is continuing demand by applications that only execute on CPUs. Two workload classes dominate today’s datacenter execution cycles: hyperscale server software (databases\, build systems\, and content stores)\, whose large instruction footprints create severe frontend pathologies; and agentic AI systems\, in which large-language-model agents plan\, dispatch tools\, and maintain growing conversational contexts\, causing CPUs to account for up to 88% of end-to-end agent latency. Reflecting this shift\, major CPU vendors have publicly repositioned the CPU as the orchestration layer of the AI stack and have begun shipping processors optimized for agent-centric workloads. \nThis dissertation argues that instruction delivery is the dominant CPU bottleneck across both workload classes and that the recent trend towards agentic AI further exacerbates this challenge. In hyperscale server binaries\, the primary pathologies are wrong-path prefetch pollution and post-recovery instruction-delivery gaps across large\, irregular call graphs. In agentic AI systems\, the bottleneck shifts to an orchestration substrate composed of protocol stacks\, dynamic-runtime dispatch\, and agent-specific extensions that is even more frontend-bound than traditional warehouse-scale workloads. \nTo address these bottlenecks\, this dissertation presents three technical contributions\, together with a companion infrastructure contribution. First\, Utility-Driven Prefetching (UDP) extends fetch-directed instruction prefetching (FDIP) with a learned per-prefetch utility model that admits candidates based on their historical contribution to demand-fetch hits\, including those reached along wrong-path execution. Second\, Junction-based Unified Miss-point Prefetching (JUMP) addresses the post-recovery instruction-delivery gap that UDP and prior FDIP optimizations cannot reach by launching a lightweight secondary FDIP thread at a learned miss point following each branch-prediction failure. Across a suite of datacenter workloads\, UDP improves IPC by 3.6% on average (up to 16.1%) over a state-of-the-art FDIP baseline\, while JUMP improves IPC by 2.0% on average (up to 14.9%). Combined\, the two mechanisms substantially close the gap between FDIP and a perfect L1 instruction cache at a storage cost of only a few tens of kilobytes.\nThird\, this dissertation introduces the Agentic Tax\, the first CPU characterization study of agentic AI workloads across three runtime families. The study is packaged as a deterministic-replay benchmark infrastructure that enables repeatable\, cycle-level evaluation under controlled conditions. The characterization shows that the orchestration substrate of agentic AI workloads is significantly more frontend-bound than the hyperscale datacenter workloads examined in prior work\, and that it introduces new dominant function families with no analog in traditional warehouse-scale systems. These findings motivate two architectural directions proposed as future work: extending UDP and JUMP to optimize the orchestration substrate itself\, and designing heterogeneous CPU cores that allocate frontend resources according to the execution phase. \nEvent Host: Surim Oh\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Heiner Litz \nZoom: https://ucsc.zoom.us/j/94753352649?pwd=7vQxlnSJkUb0KfG3t6STo639LhRv7j.1 \nPasscode: 205162
URL:https://events.ucsc.edu/event/oh-s-cse-efficient-instruction-supply-for-datacenter-processors/
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:20260528T120000
DTEND;TZID=America/Los_Angeles:20260528T140000
DTSTAMP:20260601T192714
CREATED:20260526T163353Z
LAST-MODIFIED:20260526T163353Z
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SUMMARY:Ortiz Barbosa\, D. (CSE) - HARDENING AUTONOMOUS CYBER-PHYSICAL SYSTEMS AGAINST ADVERSARIAL CONDITIONS
DESCRIPTION:Autonomous systems\, such as Autonomous Vehicles (AVs) and drones\, are increasingly\ndeployed across a wider array of contexts for both civilian and military use. As these\nsystems become more common\, they may be targeted by malicious actors seeking to\nexploit and abuse them\, compromising safety-critical operations. Among the ways to\nprotect these systems simulation based testing frameworks have been developed. How-\never\, existing testing frameworks primarily focus on identifying logical flaws or system\nvulnerabilities\, often emphasizing static scenarios and paying less attention to an adap-\ntive intelligent adversary.\nTo help reduce this gap\, this dissertation develops and applies adaptive\, adversary-\naware methodologies to discover\, formalize\, and mitigate security vulnerabilities in au-\ntonomous systems spanning vehicle platooning\, drone swarms\, and vision-based drone\nrecovery. We first apply NLP techniques to discover and formalize driving rules across\nNorth American and Australian jurisdictions\, identifying possible restriction that an\nadversary can exploit. Likewise\, these rules can be used to test the adaptability of AVs\nto new contexts and to establish a formal basis for context-aware AV testing. Next\,\nwe apply optimization-based adversarial search to both ACC-controlled vehicle pla-\ntoons and obstacle-avoiding drone swarms. We uncover maneuvers that an adversary\ncan use against the system that range from crash-inducing patterns against platooning\ncontrollers to herding strategies that divert swarms from their objectives. Finally\, to\naddress the gap regarding the possible solutions to an adversarial attack we explore how\na drone can recover from it by using LVLMs to understand its context and select a safe\nlanding surface. \nEvent Host: Diego Ortiz Barbosa\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Alvaro A Cardenas
URL:https://events.ucsc.edu/event/ortiz-barbosa-d-cse-hardening-autonomous-cyber-physical-systems-against-adversarial-conditions/
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:20260528T130000
DTEND;TZID=America/Los_Angeles:20260528T150000
DTSTAMP:20260601T192714
CREATED:20260514T160341Z
LAST-MODIFIED:20260514T160625Z
UID:10014635-1779973200-1779980400@events.ucsc.edu
SUMMARY:Yang\, D. (CSE) - Inner Monologue: a Pathway to Human-Like Reasoning for Complex Tasks
DESCRIPTION:A central goal on the path toward general AI is to build systems capable of deliberative reasoning before action. Such systems should inspect what they know\, identify what they need\, seek or construct useful information\, and revise their reasoning through intermediate cognitive states. This dissertation studies this goal through the lens of Inner Monologue (IM)\, a mechanism that enables AI systems to coordinate internal components\, acquire external information\, and reason through structured intermediate states. \nI will first introduce IM as a mechanism for internal coordination in static information systems\, where multiple models collaborate within one AI system to solve reasoning tasks. I will then extend IM to dynamic information systems\, where AI system is learned to retrieve external information. Finally\, I will present how IM can move beyond verbal reasoning toward multimodal thinking\, where generated visual states represent the system’s current understanding and support iterative refinement. \nTogether\, this dissertation demonstrates the success and potential of human-inspired Inner Monologue mechanisms for improving complex multi-step reasoning in AI systems. \nEvent Host: Diji Yang\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Yi Zhang \nZoom: https://ucsc.zoom.us/j/99915235963?pwd=7Jqo6fc83LWobTEYRZCUzbrWbeov3Y.1 \nPasscode: 7Jqo6fc83LWobTEYRZCUzbrWbeov3Y.1
URL:https://events.ucsc.edu/event/yang-d-cse-inner-monologue-a-pathway-to-human-like-reasoning-for-complex-tasks/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260529T110000
DTEND;TZID=America/Los_Angeles:20260529T123000
DTSTAMP:20260601T192714
CREATED:20260515T164420Z
LAST-MODIFIED:20260515T164420Z
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SUMMARY:Zhou\, K. (CSE) - Toward Safer Frontier AI: From Evaluation and Red-Teaming to Alignment and Oversight
DESCRIPTION:This dissertation investigates how to make modern AI systems safer as they grow more capable. It addresses two central sources of risk: malicious misuse\, in which adversarial users coerce models into harmful behavior\, and internal misalignment\, in which models themselves pursue goals that diverge from human intent through deception\, sandbagging\, or other covert behaviors. The dissertation identifies novel safety risks in frontier multimodal large language models and AI agents\, introduces a black-box red-teaming framework for AI agents\, proposes new safety alignment algorithms\, and builds the first probe-based misalignment monitoring system\, developing practical approaches for evaluating\, red-teaming\, aligning\, and overseeing frontier language models and agents. The central conclusion is that responsible AI cannot rest on any single guardrail: capability-scaled evaluation\, active red-teaming\, training-time alignment\, and scalable monitoring together form a coordinated stack for frontier AI safety. \nEvent Host: Kaiwen Zhou\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Xin Wang \nZoom: https://ucsc.zoom.us/j/94196702062?pwd=b9LJMfL232ixG2THMab8XuJ32a4FVD.1 \nPasscode:  584794
URL:https://events.ucsc.edu/event/zhou-k-cse-toward-safer-frontier-ai-from-evaluation-and-red-teaming-to-alignment-and-oversight/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260529T113000
DTEND;TZID=America/Los_Angeles:20260529T133000
DTSTAMP:20260601T192714
CREATED:20260522T161630Z
LAST-MODIFIED:20260522T161630Z
UID:10014862-1780054200-1780061400@events.ucsc.edu
SUMMARY:Qureshi\, A. (ECE) - ISoC: A Universal Impedance Spectroscopy Instrument-on-Chip in SKY130 130 nm CMOS
DESCRIPTION:Electrochemical impedance spectroscopy (EIS) is the workhorse measurement behind lithium-ion battery diagnostics\, biosensing\, and corrosion science — yet no integrated circuit has ever delivered the complete capability of a benchtop analyzer on a single die. \nThis dissertation presents ISoC\, the first universal Impedance Spectroscopy instrument-on-chip. Designed in SkyWater 130 nm CMOS process\, ISoC supports all four standard electrochemical measurement modes and performs Fourier analysis\, calibration\, and model fitting directly on-chip. The work introduces a new delta-sigma transimpedance amplifier that breaks a long-standing sensitivity–bandwidth tradeoff in current measurement. It also presents the first application of digital predistortion — a technique borrowed from wireless transmitter design — to electrochemical instrumentation\, reducing calibration error by more than an order of magnitude. The design is validated through a ten-level verification methodology spanning from transistor-level simulation to FPGA emulation — an approach that uncovered silicon-critical bugs prior to fabrication. \nEvent Host: Azzam Qureshi\, Ph.D. Candidate\, Electrical & Computer Engineering \nAdvisor: Ken Pedrotti \nZoom: https://ucsc.zoom.us/j/93312223921?pwd=jzCP7f8gbzqbkFGabEd4wM7O5TgHIH.1 \nPasscode: 342251
URL:https://events.ucsc.edu/event/qureshi-a-ece-isoc-a-universal-impedance-spectroscopy-instrument-on-chip-in-sky130-130-nm-cmos/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260529T140000
DTEND;TZID=America/Los_Angeles:20260529T160000
DTSTAMP:20260601T192714
CREATED:20260512T162505Z
LAST-MODIFIED:20260512T163221Z
UID:10014627-1780063200-1780070400@events.ucsc.edu
SUMMARY:Zhu\, R. (ECE) - From Neuromorphic Principles to Efficient Neural Language Architectures
DESCRIPTION:This dissertation investigates how neuromorphic and brain-inspired principles can guide the design of efficient neural language architectures. It addresses two central limitations of modern Transformer-based language models: memory growth with context length and high computational cost from dense matrix multiplication. Through studies of spiking neural networks\, linear-recurrent language models\, hybrid attention architectures\, MatMul-free models\, and looped language models\, the dissertation develops practical approaches for bounded-memory and bounded-compute language modeling. The central conclusion is that recurrent state\, temporal decay\, sparse computation\, and parameter reuse can provide useful design principles for scalable language models\, even when they are abstracted beyond literal biological spiking. \nEvent Host: Ridger Zhu\, Ph.D. Candidate\, Electrical & Computer Engineering  \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/96672322005?pwd=3MSitgbm5WboIENbf1hKpxwXnt9VXh.1
URL:https://events.ucsc.edu/event/zhu-r-ece-from-neuromorphic-principles-to-efficient-neural-language-architectures/
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:20260602T130000
DTEND;TZID=America/Los_Angeles:20260602T150000
DTSTAMP:20260601T192714
CREATED:20260526T162137Z
LAST-MODIFIED:20260526T162137Z
UID:10014866-1780405200-1780412400@events.ucsc.edu
SUMMARY:Sheaves\, T. (CSE) - Timing Side-Channels in Commercial ReRAM: Toward ReRAM Pentimenti
DESCRIPTION:Recently\, a class of non-invasive hardware side-channel attacks has been discovered in field-programmable gate arrays (FPGAs). These attacks extract remnants of prior users’ activity that persist as transistor defect states within reconfigurable routing resources. These remnants are known as FPGA Pentimenti. Resistive random-access memory (ReRAM) is a compelling candidate for pentimenti-like attacks beyond FPGAs. However\, unlike FPGAs\, where sophisticated on-chip sensors capable of detecting pentimenti have been well-studied\, non-invasive pentimenti recovery in commercial ReRAM must rely on measurements of observable write latency. These measurements are dominated by data-dependent structural biases that obscure any underlying defect-dynamics signal. In this dissertation\, we demonstrate that the structural and stochastic components of commercial ReRAM write latency can be decoupled and recovered through non-invasive timing analysis alone. Our results provide the reverse engineering and measurement infrastructure for future study of ReRAM pentimenti by isolating the component of programming latency sensitive to defect dynamics. \nEvent Host: Tyler Sheaves\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Dustin Richmond  \nZoom: https://ucsc.zoom.us/j/92729427179?pwd=BpYLqft18YdOU0mDdQWs8erID2VcHi.1 \nPasscode: 939530
URL:https://events.ucsc.edu/event/sheaves-t-cse-timing-side-channels-in-commercial-reram-toward-reram-pentimenti/
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:20260602T134500
DTEND;TZID=America/Los_Angeles:20260602T153000
DTSTAMP:20260601T192714
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260602T140000
DTEND;TZID=America/Los_Angeles:20260602T160000
DTSTAMP:20260601T192714
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260603T090000
DTEND;TZID=America/Los_Angeles:20260603T110000
DTSTAMP:20260601T192714
CREATED:20260529T161208Z
LAST-MODIFIED:20260529T161208Z
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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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260603T130000
DTEND;TZID=America/Los_Angeles:20260603T170000
DTSTAMP:20260601T192714
CREATED:20260529T164521Z
LAST-MODIFIED:20260529T164521Z
UID:10014860-1780491600-1780506000@events.ucsc.edu
SUMMARY:22nd Annual Graduate Research Symposium
DESCRIPTION:This event celebrates and highlights the work of UCSC graduate students in all academic divisions. Enrolled graduate students will present either a poster\, talk\, or mixed media presentation. Judges will select and award a top prize for each academic division. This event is free and open to the public. \nLocation : Science Hill\nResearch talks will be scheduled in BioMed 200\, BioMed 300 and PSB 240 from 1:00 – 2:30 PM\nThe poster session will be outside on the Plaza between PSB and the Science & Engineering Library\, 2:30 – 4:00 PM
URL:https://events.ucsc.edu/event/22nd-annual-graduate-research-symposium/
LOCATION:Physical Sciences Building\, Physical Sciences Building\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260604T093000
DTEND;TZID=America/Los_Angeles:20260604T113000
DTSTAMP:20260601T192714
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/
LOCATION:
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260604T100000
DTEND;TZID=America/Los_Angeles:20260604T120000
DTSTAMP:20260601T192714
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260604T100000
DTEND;TZID=America/Los_Angeles:20260604T120000
DTSTAMP:20260601T192714
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
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DTSTART;TZID=America/Los_Angeles:20260604T130000
DTEND;TZID=America/Los_Angeles:20260604T150000
DTSTAMP:20260601T192714
CREATED:20260526T193652Z
LAST-MODIFIED:20260526T193652Z
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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
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DTSTART;TZID=America/Los_Angeles:20260604T140000
DTEND;TZID=America/Los_Angeles:20260604T153000
DTSTAMP:20260601T192714
CREATED:20260527T164116Z
LAST-MODIFIED:20260527T164116Z
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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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260605T080000
DTEND;TZID=America/Los_Angeles:20260605T100000
DTSTAMP:20260601T192714
CREATED:20260527T160819Z
LAST-MODIFIED:20260527T160819Z
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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
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260609T103000
DTEND;TZID=America/Los_Angeles:20260609T130000
DTSTAMP:20260601T192714
CREATED:20260526T194326Z
LAST-MODIFIED:20260526T194445Z
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SUMMARY:Shen\, G. (CSE) - Library-Level Choreographic Programming
DESCRIPTION:Modern software increasingly relies on distributed systems to provide accessible\, scalable\,\nand reliable services. Choreographic programming brings a global perspective to distributed\nsystem development: programmers write a single program that describes the behavior of a\nwhole system\, and a compiler projects that global description into local programs run by each\nnode. By making distributed control flow explicit\, choreographic programming can rule out\nimportant classes of errors\, including deadlocks. This dissertation investigates library-level\nchoreographic programming\, an approach that embeds choreographic abstractions in existing\nhost languages rather than implementing them as standalone languages. The central claim\nis that the library approach can retain the safety and global reasoning principles of chore-\nographic programming while taking advantage of the host language’s features\, tools\, and\necosystem. First\, we present HasChor\, a first-of-its-kind library-level choreographic program-\nming language in Haskell\, built using freer monads. Next\, we generalize the design underlying\nHasChor to algebraic effects\, giving library-level implementations in Agda and OCaml. Fi-\nnally\, we present Parkour\, a backward-compatible extension to HasChor that adds a construct\nfor expressing parallel behavior in choreographies. Together\, these systems show that chore-\nographic programming can be implemented\, generalized\, and extended at the library level\,\nmaking global programming techniques available within practical host-language settings. \nEvent Host: Gan Shen\, Ph.D. Candidate\, Computer Science & Engineering  \nAdvisor: Lindsey Kuper  \nZoom: https://ucsc.zoom.us/j/93790633483?pwd=Jg8JlISsrwjLBaQIi1KdHk36bNMIv7.1 \nPasscode: 902041 \n 
URL:https://events.ucsc.edu/event/shen-g-cse-library-level-choreographic-programming/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260609T120000
DTEND;TZID=America/Los_Angeles:20260609T130000
DTSTAMP:20260601T192714
CREATED:20260526T161617Z
LAST-MODIFIED:20260526T161617Z
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SUMMARY:Kim\, C. (CSE)- Toward Adaptive Graph Processing and Fault-Tolerant Agentic Inference on Heterogeneous Distributed Systems
DESCRIPTION:Edge computing and distributed AI systems increasingly operate under heterogeneous resources\, dynamic workloads\, and frequent failures\, requiring both adaptivity and fault tolerance for efficient execution. In heterogeneous edge clusters\, nodes differ significantly in CPU throughput\, memory capacity\, and network bandwidth\, while modern distributed GPU clusters supporting agentic LLM inference must recover large amounts of runtime state under routine failures. This dissertation addresses these challenges through two systems: Zsiga\, an adaptive distributed graph processing system for heterogeneous edge clusters\, and Forte\, a fault-tolerant KV cache recovery system for distributed agentic LLM inference. \nZsiga improves connected component computation through capacity-aware graph partitioning and runtime-adaptive boundary migration\, reducing execution time by up to 90.9% while eliminating out-of-memory failures under heterogeneous resource constraints. Forte addresses KV cache recovery for long-running agentic inference workloads\, where failures can erase accumulated reasoning trajectories and tool interaction histories. Forte exploits the observation that not all KV blocks are equally critical\, introducing criticality-aware erasure coding\, domain-diverse placement\, and prioritized foreground recovery to enable efficient recovery under correlated failures. Experimental results show that Forte is the only evaluated scheme that successfully resumes execution under correlated domain failures\, reducing foreground stall by 89.7% and end-to-end recovery latency by 50.6–58.9% at 2.0$\times$ memory overhead. Together\, these systems demonstrate how adaptivity and fault tolerance can improve the efficiency and resilience of distributed systems in heterogeneous and failure-prone environments. \nEvent Host: Chaeeun Kim\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Chen Qian & Liting Hu \nZoom: https://ucsc.zoom.us/j/9863615188?pwd=kTka0aZXJ070tor1EKvrt3X6AveBRp.1 \nPasscode:  cG5SL8 \n  \n 
URL:https://events.ucsc.edu/event/kim-c-cse-toward-adaptive-graph-processing-and-fault-tolerant-agentic-inference-on-heterogeneous-distributed-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260618T100000
DTEND;TZID=America/Los_Angeles:20260618T120000
DTSTAMP:20260601T192714
CREATED:20260526T162714Z
LAST-MODIFIED:20260526T162714Z
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SUMMARY:Carrión\, H. (CSE) - Deep Learning Algorithms for Medical Image Representation Learning and Understanding
DESCRIPTION:AI-assisted clinical decisions in medicine\, and particularly in dermatology\, demand fine-grained understanding across diverse skin tones\, body sites\, and disease types\, yet expert-annotated datasets are scarce\, demographically imbalanced\, and almost devoid of rare presentations. This dissertation develops four deep learning systems for this low-label\, low-coverage regime. We introduce HealNet\, which learns wound healing stages from longitudinal photographs without any human labels\, reaching 90.6% downstream stage-classification accuracy on a small longitudinal cohort. The Fair\, Efficient\, and Diverse Diffusion (FEDD) model then leverages powerful diffusion-model embeddings to build a skin-tone-fair\, data-efficient classifier for skin lesions\, matching or exceeding state-of-the-art performance while using only 5-20% of available labels and contributing explicit skin-tone-stratified fairness evaluation of the work. Next\, Controllable Generation of Diverse Dermatological Imagery (cgDDI) re-tasks this diffusion model to controllably synthesize skin-tone-balanced dermatological imagery\, growing a small biopsy-confirmed dataset by over 400x and reaching state-of-the-art 90.9% accuracy and improved fairness in malignancy classification\, with a +13.9% cross-dataset gain on the Fitzpatrick17k benchmark. Finally\, we introduce D-Synth and DermDepth: a synthetic dermoscopic dataset with pixel-perfect 3D ground truth and a metric-scale foundation model that closes the loop into 3D dermatology\, correcting metric scale error from over 16x to under 1.1x on real dermoscopic data and enabling single-photograph measurement of lesion reconstruction: size\, area\, and volume without specialized hardware. All data\, code\, and models are released openly to support reproducibility and ongoing fairness research. \nEvent Host:  Héctor Carrión\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Narges Norouzi \nZoom: https://ucsc.zoom.us/j/96678782408?pwd=71f0ObEnUMNgkZ9NYnpbFLMlg1Pdm0.1 \nPasscode: 0FMVtz
URL:https://events.ucsc.edu/event/carrion-h-cse-deep-learning-algorithms-for-medical-image-representation-learning-and-understanding/
LOCATION:
CATEGORIES:Ph.D. Presentations
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