BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Events - ECPv6.15.20//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:20260301T000000
DTEND;TZID=America/Los_Angeles:20260331T235959
DTSTAMP:20260421T165633
CREATED:20260223T210337Z
LAST-MODIFIED:20260223T210337Z
UID:10009248-1772323200-1775001599@events.ucsc.edu
SUMMARY:March is Hummingbird Month at the UCSC Arboretum & Botanic Garden
DESCRIPTION:March is Hummingbird Month at the UCSC Arboretum & Botanic Garden \nThis time of year\, the Arboretum hosts both Anna’s and Allen’s hummingbirds\, the two most common species in Northern California. “The density of hummingbirds—the number per area in the Arboretum—is ridiculously high\,” says Bruce Lyon\, Professor Emeriti of Ecology and Evolutionary Biology at UCSC. “You can watch them feeding on flowers\, you can watch their courtship\, you can watch them chasing different species. It’s a great opportunity to see some pretty amazing hummingbird biology.” \nIn celebration of this special time of year\, we invite you to visit the garden as much as possible! We will have presentations\, workshops\, and tours throughout the month. See our webpage for a schedule of activities and more information about hummingbirds and the abundance of plants at the Arboretum that attract them. \nWe will also feature hummingbird merchandise and hummingbird-attracting plants at our gift shop and nursery. Visit Norrie’s Gift & Garden Shop\, Tuesdays thru Sundays from 10 – 4. For more information visit: https://arboretum.ucsc.edu/garden-shop/ \nAll events are free with paid admission: Adults: $10\, Seniors $8 and Youth 4-17 $5. Current UCSC students are free. Rain cancels outdoor activities. \nCurrent Arboretum members are always free and enjoy other great benefits year-round!  Join Today at https://arboretum.ucsc.edu/get-involved/join-us/    \n  \n 
URL:https://events.ucsc.edu/event/march-is-hummingbird-month-at-the-ucsc-arboretum-botanic-garden/
LOCATION:Arboretum\, 122 Arboretum Road\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/Lohman_Kevin_Spotlight-WATERMARK.jpeg
GEO:36.9838652;-122.0609079
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Arboretum 122 Arboretum Road Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=122 Arboretum Road:geo:-122.0609079,36.9838652
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T070000
DTEND;TZID=America/Los_Angeles:20260309T080000
DTSTAMP:20260421T165633
CREATED:20260303T175533Z
LAST-MODIFIED:20260303T175533Z
UID:10009386-1773039600-1773043200@events.ucsc.edu
SUMMARY:Hendawy\, M. (CM) - Autonoming Child Online Safety in the Age of AI: From Control to Digital Co-Agency Across Cultures
DESCRIPTION:Children’s lives are now inextricably linked with AI-driven digital systems that shape learning\, social interaction\, and development. This has elevated child online safety to a central concern for families\, policymakers\, and educators. This makes Child online safety a wicked socio-technical problem\, emerging from the complex interplay of social norms\, platform incentives\, cultural expectations\, and rapidly evolving technologies. Dominant control-based paradigms—monitoring\, blocking\, and surveillance—undermine children’s developmental capacity\, erode family trust\, and foreclose the iterative cycles of self regulated learning necessary for digital resilience. This proposal advances digital co-agency as a new paradigm for child online safety. It reframes safety from an outcome of unilateral control to a shared\, relational practice distributed across children\, caregivers\, technologies\, and governance structures. To be effective\, digital co-agency must be grounded in a clear normative standard. I define this standard as ethical safety: protection is legitimate only when it is rights-respecting and developmentally supportive. Within this boundary\, the dissertation proposes autonoming as a design stance for AI-mediated safety systems. Autonoming systems act as developmental mentors that support children’s judgment over time through explanation\, negotiation\, and graduated support that can fade as competence grows. Autonoming is grounded in Self-Regulated Learning (SRL) as the developmental mechanism for durable safety capacity. SRL models learning as cyclical forethought (planning)\, performance (in-the-moment regulation)\, and reflection (evaluating outcomes). The dissertation adopts a socio-technical interpretivist stance and a Design Science Research orientation to produce actionable artifacts that are theoretically grounded and evaluable.. Its core methodological contribution is localization-first comparative design across Cairo and Berlin. This comparative structure helps distinguish between: localized variables (culturally specific norms regarding authority\, privacy\, risk\, norms\, expectations\, and legitimacy conditions that must be adapted to) from ethical invariants (accountability\, contestability\, proportionality that should hold across contexts). \nEvent Host: Mennatullah Hendawy\, Ph.D. Student\, Computational Media  \nAdvisor: Magy Seif El-Nasr \nZoom- https://ucsc.zoom.us/j/93831600031?pwd=hsnX574bcXVQRZa16sKbX0u7OuaMlu.1 \nPasscode-459844
URL:https://events.ucsc.edu/event/hendawy-m-cm-autonoming-child-online-safety-in-the-age-of-ai-from-control-to-digital-co-agency-across-cultures/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T080000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260421T165633
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009358-1773043200-1773075600@events.ucsc.edu
SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/1/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T100000
DTEND;TZID=America/Los_Angeles:20260309T110000
DTSTAMP:20260421T165633
CREATED:20260303T174856Z
LAST-MODIFIED:20260303T174856Z
UID:10009385-1773050400-1773054000@events.ucsc.edu
SUMMARY:Robbins\, A. (ECE) - How to train your organoid: goal-directed learning in biological neural networks
DESCRIPTION:Artificial neural networks can now learn to play games\, control robots\, generate language\, and solve complicated reasoning tasks\, yet we still lack a clear understanding of how to directly guide learning in biological neural networks. We show that brain organoids can learn to solve a fundamental control task\, balancing an inverted pendulum\, through closed-loop electrophysiology. Cortical organoids interfaced with high-density microelectrode arrays received sensory input about the pole’s angle and produced motor output through their neural activity. Training signals selected by a reinforcement learning algorithm significantly outperformed random stimulation and no-stimulation controls. Blocking glutamatergic transmission abolished the learning and washout restored it\, confirming the adaptation depends on synaptic plasticity. To support this work and future experiments\, we developed BrainDance\, an open-source framework for running reproducible biological learning experiments\, and contributed to RT-Sort\, a real-time spike sorting algorithm. This dissertation presents the tools\, experiments\, and findings from pursuing goal-directed learning in biological neural networks. BrainDance makes these experiments easy-to-create\, reproducible and shareable\, letting any lab with compatible hardware start training their own organoids. \nEvent Host: Ash Robbins\, Ph.D. Candidate\, Electrical and Computer Engineering  \nAdvisor: Mircea Teodorescu \nZoom- https://ucsc.zoom.us/j/95839863615?pwd=EmqTWPN9RRBYZRW7rcpoaT9kqacfRP.1 \nPasscode- 069118
URL:https://events.ucsc.edu/event/robbins-a-ece-how-to-train-your-organoid-goal-directed-learning-in-biological-neural-networks/
LOCATION:
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T104000
DTEND;TZID=America/Los_Angeles:20260309T114500
DTSTAMP:20260421T165633
CREATED:20260305T230039Z
LAST-MODIFIED:20260305T230039Z
UID:10009404-1773052800-1773056700@events.ucsc.edu
SUMMARY:ECE 290 Seminar: Dynamical Signatures: Harnessing the Hidden Language of In-Space Electric Propulsion
DESCRIPTION:Presenter: Dr. Christine Greve\, Research Engineer\,  Edwards AFB \nDescription: Low-thrust space electric propulsion systems offer long propulsion system lifetimes for satellite maintenance maneuvers. These thrusters operate by generating and accelerating plasmas\, making the thrusters throttleable\, propellant-efficient\, and scalable from low-to-high power operations. This talk will focus on efforts to leverage the underlying time-dependent dynamics of plasma to investigate and influence thruster research and development. Prior years of study have developed techniques to uniquely represent the dynamics of such systems that have since been used to open a new way to test and operate plasma systems. Additional work has investigated the correlations between time-dependent measurements of these dynamics to develop digital twins\, automate test processes with machine learning\, inform design of experiments\, and develop on-orbit system diagnostics. The talk will conclude with a look to the future as these tools are further applied both within the lab and potentially transitioned to on-orbit applications. \nBio: Dr. Christine Greve is a research engineer for the Air Force Research Laboratory at Edwards AFB. She received her Ph.D. in Aerospace Engineering from Texas A&M University under an NDSEG fellowship for her work in data-driven modeling of plasma-based systems. She now serves as the Electric Propulsion group lead with interests in high-power electric propulsion\, machine learning\, data-driven modeling\, and novel plasma diagnostic techniques. \nHosted by: Professor Soumya Bose\, ECE Department \nZoom: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-290-seminar-dynamical-signatures-harnessing-the-hidden-language-of-in-space-electric-propulsion/
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/2026/02/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:20260309T140000
DTEND;TZID=America/Los_Angeles:20260309T160000
DTSTAMP:20260421T165633
CREATED:20260303T174216Z
LAST-MODIFIED:20260303T174216Z
UID:10009384-1773064800-1773072000@events.ucsc.edu
SUMMARY:Harrison\, D. (CS) - Multi-Level Control in Neural Dialogue Generation: Style\, Semantics\, and Selection through Over-Generation and Ranking
DESCRIPTION:End-to-end neural generation models have largely displaced the modular architectures that once gave dialogue system designers explicit control over what is said and how it is said. While these models produce fluent text\, they collapse content planning\, sentence planning\, and surface realization into a single undifferentiated decoding step\, sacrificing the controllable structure that earlier systems provided. This dissertation investigates how that structure can be recovered through the over-generate-and-rank (OGR) paradigm: generating multiple candidate outputs and selecting among them using learned or prompt-based ranking functions that jointly optimize semantic fidelity\, stylistic appropriateness\, and conversational coherence. We instantiate OGR at three levels of natural language generation for dialogue: utterance-level stylistic control\, cross-domain semantic evaluation\, and dialogue-level response selection. \nFirst\, we show that explicit conditioning mechanisms\, specifically decoder-level side constraints for personality variation and discourse contrast\, re-introduce stylistic control into neural sequence-to-sequence models without compromising semantic accuracy. Second\, we demonstrate that prompt-based learning with structured linguistic profiles achieves near-perfect personality accuracy and effectively zero slot error rate when combined with ranking\, establishing that LLM prompting with explicit pragmatic specifications can match or exceed fine-tuning for personality-conditioned generation. Third\, we develop a cross-domain semantic error rate evaluation framework that frames slot error computation as an extraction task\, using a LoRA-adapted language model to extract meaning representations from generated text and a trained ranker to select among candidate extractions\, achieving reliable evaluation across 23 topic domains without domain-specific rules. Fourth\, we build and evaluate a speaker-aware transformer response ranker for Athena\, our Alexa Prize socialbot\, demonstrating that learned ranking over heterogeneous generator pools produces significantly longer conversations and higher user ratings than heuristic rule-based selection in a live A/B study with over 6\,000 conversations. \nA unifying finding emerges across all four contributions: the pragmatic features that control personality style in generation—acknowledgements\, engagement questions\, hedges\, exclamations—are the same features that distinguish high-quality from mediocre responses in open-domain dialogue. This parallel reveals that stylistic control and response ranking are complementary mechanisms for achieving the same goal: making dialogue systems sound more natural and engaging. Together\, these results support the dissertation’s central hypothesis that over-generate-and-rank provides a general\, extensible mechanism for controllable neural language generation\, restoring explicit decision points where competing communicative objectives can be weighed. The ranking function serves a role analogous to the sentence planner in classical NLG architectures\, but operates on the outputs of modern neural and LLM-based generators. \n  \nEvent Host: Davan Harrison\, Ph.D. Candidate\, Computer Science \nAdvisor: Marilyn Walker \n 
URL:https://events.ucsc.edu/event/harrison-d-cs-multi-level-control-in-neural-dialogue-generation-style-semantics-and-selection-through-over-generation-and-ranking/
LOCATION:CA
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260421T165633
CREATED:20260217T230434Z
LAST-MODIFIED:20260217T230434Z
UID:10009244-1773072000-1773075600@events.ucsc.edu
SUMMARY:AM Seminar: Solution Discovery in Fluids with High Precision Using Neural Networks
DESCRIPTION:Presenter: Ching-Yao Lai\, Assistant Professor\, Stanford University \nDescription: I will discuss examples utilizing neural networks (NNs) to find solutions to partial differential equations (PDEs) that facilitate new discoveries. Despite being deemed universal function approximators\, neural networks\, in practice\, struggle to fit functions with sufficient accuracy for rigorous analysis. Here\, we developed multi-stage neural networks (Wang and Lai\, J. Comput. Phys. 2024) that can reduce the prediction error to nearly the machine precision of double-precision floating points within a finite number of iterations. We use accurate NNs to tackle the challenge of searching for singularities in fluid equations (Wang-Lai-Gómez-Serrano-Buckmaster\, Phys. Rev. Lett. 2023). Unstable singularities\, especially in dimensions greater than one\, are exceptionally elusive. With NNs we demonstrate the first discovery of smooth unstable self-similar singularities to unforced incompressible fluid equations (Wang et al.\, arXiv:2509.14185). The example illustrates how deep learning can be used to discover new and highly accurate numerical solutions to PDEs. \nBio: Ching-Yao Lai (Yao) is an Assistant Professor in the Department of Geophysics and an Affiliated Faculty of the Institute for Computational and Mathematical Engineering (ICME) at Stanford. Before joining Stanford\, she was an Assistant Professor at Princeton University. She received an undergraduate degree (2013) in Physics from National Taiwan University and a PhD (2018) in Mechanical and Aerospace Engineering from Princeton University. She completed her postdoctoral research at Columbia University where she received the Lamont Postdoctoral Fellowship. Her current research focuses on enhancing the representation of machine-learning models to tackle multiscale problems. She was the recipient of the 2023 Google Research Scholar Award\, the 2024 Sloan Research Fellowship\, and the 2025 NSF CAREER Award. \nHosted by: Applied Mathematics
URL:https://events.ucsc.edu/event/am-seminar-solution-discovery-in-fluids-with-high-precision-using-neural-networks/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260421T165633
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009357-1773072000-1773075600@events.ucsc.edu
SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/2/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
END:VCALENDAR