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DTSTART;TZID=America/Los_Angeles:20260526T070000
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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/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260526T093000
DTEND;TZID=America/Los_Angeles:20260526T113000
DTSTAMP:20260519T162948Z
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
DTSTAMP:20260518T190031Z
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:20260515T174024Z
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/
CATEGORIES:Ph.D. Presentations
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