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SUMMARY:AM Seminar: The Thinking Eye: AI That Sees\, Reads\, and Reasons in Medicine
DESCRIPTION:Presenter: Yuyin Zhou\, Assistant Professor\, UCSC \nDescription: Medical AI is undergoing a profound transformation\, evolving from simple pattern recognition to systems capable of complex clinical reasoning. This talk will chart this evolution across three dimensions: data\, models\, and evaluation. I will first highlight the shift from limited\, unimodal datasets to massive multimodal resources. In particular\, I will introduce MedTrinity-25M—a novel collection of over 25 million richly annotated medical images that serves as a foundation for multimodal tasks such as visual question answering and report generation. Building on this\, I will describe how grounding decision processes in a structured medical knowledge graph enables the generation of high-fidelity reasoning chains. Using these chains\, we construct a large-scale medical reasoning dataset\, which in turn allows us to develop a new class of reasoning models. These models not only achieve state-of-the-art performance on multiple clinical Q&A benchmarks but also produce reasoning outputs that physicians across seven specialties have independently verified as clinically reliable\, interpretable\, and more factually accurate than existing large language models. Finally\, the talk will offer a deep dive into the critical evaluation of these advanced models\, moving beyond standard benchmarks to expose their current limitations—particularly in interpreting dynamic clinical scenarios such as tracking disease progression from temporal image sequences. To foster a holistic understanding of the mechanisms underlying these reasoning models\, I will introduce a new evaluation framework that examines performance from two complementary perspectives: their grasp of static knowledge versus their capacity for dynamic reasoning. Together\, these advances point toward a future where AI systems can holistically analyze patient information and function as true collaborative partners in complex medical decision-making. \nBio: Yuyin Zhou is an Assistant Professor of Computer Science and Engineering at UC Santa Cruz. Her research interests lie at the intersection of machine learning and computer vision\, with a primary focus on AI for healthcare and scientific discovery. Her work (70+ peered-reviewed publications with18\,000+ citations) has been recognized with honors including 2025 Google Research Scholar Award\, Best Paper Award at KDD 2025 Health Day and at Computerized Medical Imaging and Graphics 2024\, 2023 Hellman Fellowship\, Best Paper Honorable Mention at DART 2022\, and finalist recognition for the MICCAI Young Scientist Publication Impact Award in 2022. Beyond her research\, Yuyin has organized over 20 workshops and tutorials at major conferences including ICML\, MICCAI\, ML4H\, ICCV\, CVPR\, and ECCV\, with coverage in media outlets such as ICCV Daily and Computer Vision News. She serves as a regular Area Chair for CVPR\, ICLR\, MICCAI\, CHIL\, and ISBI\, an associate editor for SPIE medical imaging\, Image and Vision Computing\, and was the Doctoral Consortium Chair for WACV 2025. \nHosted by: Applied Mathematics Department
URL:https://events.ucsc.edu/event/am-seminar-the-thinking-eye-ai-that-sees-reads-and-reasons-in-medicine/
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260403T132000
DTEND;TZID=America/Los_Angeles:20260403T142500
DTSTAMP:20260504T061114
CREATED:20260401T005024Z
LAST-MODIFIED:20260401T005118Z
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SUMMARY:BME 80G Seminar: To Infinity and Beyond? Ethical\, legal\, and social issues of human research in space”
DESCRIPTION:Presenter: Vaso Rahimzadeh\, Assistant Professor\, Baylor College of Medicine \nDescription: As humans venture farther into outer space\, new scientific discovery awaits including in genomics; but so do new ethical dilemmas.  Who bears the risks (and rewards) of space exploration and how should humanity ethically expand beyond our planet? This session will have students think critically about the ethical\, legal\, and social issues of human genomic research in space and offer frameworks for analyzing them. Students will learn about the contemporary challenges and opportunities of genomic research for the upcoming lunar missions\, and in anticipation of future Mars exploration. \nBio: I am Assistant Professor at the Center for Medical Ethics and Health Policy at Baylor College of Medicine. In my National Institutes of Health-funded research\, I investigate the ethical\, legal\, and social issues of health data sharing on earth and in space. I aim to inform policy and practice in ways that maximize the scientific value of data while respecting the rights and interests of individuals and communities. I director the METEORS program (Mission to Enhance eThics Education\, Outreach\, and Research in Space) and serve on the Bioethics Advisory Panel for the National Aeronautics and Space Administration (NASA). I am a proud UC alum\, earning my BS in Microbial Biology from UC Berkeley in 2012\, and hold a PhD from McGill University with a specialization in biomedical ethics. You can read more about my background and read my work here. \nHosted by: Professor Karen Miga\, BME Department
URL:https://events.ucsc.edu/event/bme-80g-seminar-to-infinity-and-beyond-ethical-legal-and-social-issues-of-human-research-in-space/
LOCATION:Jack Baskin Auditorium\, 191 Baskin Cir\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260402T114000
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SUMMARY:BME 280B Seminar: Small changes\, Big consequences: Modulators of Alphavirus Assembly
DESCRIPTION:Presenter: Dr. Suchetana (Tuli) Mukhopadhyay\, Professor\, Indiana University \nDescription: N/A \nBio: Suchetana “Tuli” Mukhopadhyay\, Ph.D.\, is a professor in the Department of Biology at Indiana University\, Bloomington. She received her B.A. in chemistry from DePauw University and her Ph.D. in chemistry from the University of Illinois at Chicago. Following her doctoral studies\, Mukhopadhyay conducted postdoctoral research at the University of Texas Southwestern Medical Center\, focusing on G-protein mediated signaling. She continued her postdoctoral work at Purdue University in structural virology\, where she developed a strong interest in arboviruses. Mukhopadhyay joined Indiana University in 2005\, where she established her research program on the assembly and spread of alphaviruses. \nHosted by: Professor Rebecca Dubois\, BME Department
URL:https://events.ucsc.edu/event/bme-280b-seminar-small-changes-big-consequences-modulators-of-alphavirus-assembly/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260402T090000
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SUMMARY:Learn to use the UCSC Genome Browser
DESCRIPTION:The UCSC Genome Browser is hosting a free workshop for new and advanced users!\n\nBeginner session starts at 9 and will include:\n\nHow to understand genome annotations\nHow to find annotation data\nOverview of training resources\n\nIntermediate + advanced session starts at 10\, covering\n\nCustom tracks\nTrack hubs\nBLAT\nAnd more!!\n\n\nJoin us and be the FIRST to see a brand new feature! At the end we’ll have free stickers and a chance to ask the team questions.\n\nBring your laptop to follow along\, and we encourage questions. See you April 2nd in E2 180.
URL:https://events.ucsc.edu/event/learn-to-use-the-ucsc-genome-browser/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Training,Workshop
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DTSTART;TZID=America/Los_Angeles:20260401T120000
DTEND;TZID=America/Los_Angeles:20260401T130000
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SUMMARY:Engineering Teaching Community (Faculty)
DESCRIPTION:During the chaos of a quarter\, is it hard to find time to reflect and improve as an instructor? Would you like to be a part of an inclusive\, supportive group of engineering instructors who do this in community? ETC is for sharing teaching experiences\, classroom ideas\, research on learning\, and methods that support instructors and students. All are welcome\, and lunch is provided. Please reach out to Jenny Quynn with questions.
URL:https://events.ucsc.edu/event/engineering-teaching-community-faculty/2026-04-01/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Meetings & Conferences,Training
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260401T110000
DTEND;TZID=America/Los_Angeles:20260401T121500
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CREATED:20260325T164503Z
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SUMMARY:CSE Colloquium - Messages from across the event horizon:  AI Agentic Design for Computer Architecture (and more generalizable learnings)
DESCRIPTION:Presenter: Christopher Fletcher\, UC Berkeley \nAbstract: \nIt is difficult to escape the hype of agentic coding.  Is the hype real?  Are we still living in ~Summer 2025 — when AI coding would accomplish little more than upset its human supervisor?  Or has a level shift in technology finally arrived? \nIn this talk I will argue the latter.  I will describe a self-imposed experiment to discover modern AI coding tools’ capabilities (starting mid February 2026).  I will try (my best) to communicate my utter and sheer surprise at where the state of the art actually is.  Then I will do a deep dive and try to relay everything I have learned about this new engineering discipline—based on my attempts to push the technology as hard as I can for the past 1.5 months.  I will conclude by pontificating about the future of computer architecture and academic research more generally. \nBio: \nChristopher Fletcher is an Associate Professor of EECS at UC Berkeley. He is a computer architect whose research spans architecture\, security\, and domain-specific acceleration\, especially at their intersections from cryptography and hardware attacks to algorithm-to-hardware co-design. His work has received 31 paper recognitions and several other honors\, including the NSF CAREER Award\, Intel and Google faculty awards\, UIUC research and promotion awards\, election to DARPA ISAT\, and MIT’s George M. Sprowls Award\, with related work also recognized by Scientific American as one of ten “World Changing Ideas.” \nHosted by: Professor Alvaro Cardenas \nLocation: Engineering 2\, Room E2-180 (Refreshments such as fruit\, pastries\, coffee\, and tea will be provided.) \nZoom Option: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3 \n 
URL:https://events.ucsc.edu/event/cse-colloquium-messages-from-across-the-event-horizon-ai-agentic-design-for-computer-architecture-and-more-generalizable-learnings/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260330T160000
DTEND;TZID=America/Los_Angeles:20260330T170000
DTSTAMP:20260504T061114
CREATED:20260325T182049Z
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SUMMARY:AM Seminar:  Flexible Filaments and Swimming Cups: Just Go with the Flow
DESCRIPTION:Presenter: Lisa Fauci\, Professor\, Tulane University \nDescription: The motion of waving or rotating filaments in a fluid environment is a common element in many biological and engineered systems. Examples at the microscale include chains of diatoms moving in the ocean\, flagella of individual cells comprising multicellular colonies\, as well as engineered nanorobots designed to deliver drugs to tumors. In this talk we will present mathematical and computational insights into these flows at the microscale. Our modeling approaches will vary from detailed models that capture flagellar material properties and wave geometry to minimal force-dipole models that represent a flagellum by a single point. We will investigate a few intriguing systems\, including the journey of extremely long insect sperm flagella through tortuous female reproductive tracts\, and the hydrodynamic performance of shape-shifting Choanoeca flexa colonies. \nBio: Lisa Fauci received her PhD from the Courant Institute of Mathematical Sciences at New York University\, and directly after that joined the Department of Mathematics at Tulane University in New Orleans\, Louisiana\, USA. Her research focuses on biological fluid dynamics\, with an emphasis on using modeling and simulation to study the basic biophysics of organismal locomotion and reproductive mechanics. Lisa served as president of the Society for Industrial and Applied Mathematics (SIAM) in 2019-2020. She is a fellow of SIAM\, the American Mathematical Society\, the Association for Women in Mathematics\, and the American Physical Society. In 2023\, she was elected to the US National Academy of Sciences. \nHosted by: Applied Mathematics Department
URL:https://events.ucsc.edu/event/am-seminar-flexible-filaments-and-swimming-cups-just-go-with-the-flow/
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260323T183000
DTEND;TZID=America/Los_Angeles:20260323T200000
DTSTAMP:20260504T061114
CREATED:20260226T220528Z
LAST-MODIFIED:20260313T232812Z
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SUMMARY:The Future of Work in the Age of AI: March Slugs and Steins with Dean PK Agarwal
DESCRIPTION:The Future of Work in the Age of AI: Chaos\, Adaptation\, or Growth? \nArtificial intelligence has reignited an old debate: are we approaching mass technological unemployment\, or simply the next phase of economic evolution? Public discourse swings between dystopian predictions of human obsolescence and confident assurances that “new jobs will replace old ones.” This lecture steps back from speculation and examines evidence. Drawing on economic history and disruptive change\, going back to the wheel and the printing press to electrification\, computers\, and the internet—we explore how societies have repeatedly confronted disruptive change: moments when existing skills\, institutions\, and expectations no longer matched the pace of innovation. The goal is not to predict the future\, but to provide a framework for thinking about it—what AI may automate\, what it may amplify\, and why the real disruption may be the speed of adaptation rather than the disappearance of work itself. The talk concludes with implications for higher education\, workforce development\, and how individuals can remain resilient in an age where intelligence is no longer exclusively human. \nREGISTER \n 
URL:https://events.ucsc.edu/event/march-slugs-and-steins-with-dean-pk-agarwal/
CATEGORIES:Lectures & Presentations
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LOCATION:
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DTSTART;TZID=America/Los_Angeles:20260318T120000
DTEND;TZID=America/Los_Angeles:20260318T130000
DTSTAMP:20260504T061114
CREATED:20250923T070000Z
LAST-MODIFIED:20251001T224502Z
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SUMMARY:Engineering Teaching Community (Faculty)
DESCRIPTION:During the chaos of a quarter\, is it hard to find time to reflect and improve as an instructor? Would you like to be a part of an inclusive\, supportive group of engineering instructors who do this in community? ETC is for sharing teaching experiences\, classroom ideas\, research on learning\, and methods that support instructors and students. All are welcome\, and lunch is provided. Please reach out to Jenny Quynn with questions.
URL:https://events.ucsc.edu/event/engineering-teaching-community-faculty/2026-03-18/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Meetings & Conferences,Training
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260316T080000
DTEND;TZID=America/Los_Angeles:20260316T100000
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SUMMARY:Teng\, Z. (CM) - Visualizing Player Processes: Towards Design Guidelines for Interactive Process Visualization Tools in Game Analytics
DESCRIPTION:Game analysts face a significant challenge in understanding problem-solving and decision-making processes from the vast and complex sequential data generated by modern video games. Existing visualization tools often fail to adequately support the exploration\, suffering from issues of visual clutter\, inflexible cohort construction\, and a lack of interactive depth. To address this gap\, this dissertation adopts a Research through Design (RtD) methodology to investigate how an interactive process visualization system can be designed and developed to better support the needs of game analysts. \nThe research was conducted in three phases. First\, an initial set of five design guidelines was identified through a breakdown analysis of existing tools and semi-structured interviews with professional game analysts. Second\, these guidelines were iteratively refined through long-term\, collaborative case studies with analysts working on diverse commercial games. This process not only validated the initial guidelines and surfaced one additional guideline concerning interactive inspection\, but also resulted in INSPECT\, an interactive process visualization prototyping system that embodies the refined guidelines. Third\, the guidelines were empirically validated through two complementary user studies of the INSPECT system. A controlled experiment demonstrated that features designed according to the guidelines enabled participants to identify player strategies more efficiently than with a baseline system\, while a qualitative study with professional Dota 2 coaches and players demonstrated the system’s practical value for strategic analysis and strong usability. \nThe primary contributions of this dissertation to the fields of game analytics and information visualization is a set of validated design guidelines for process visualization tools. This contribution provides a durable and transferable framework for the design and development of more effective\, analyst-centered tools for understanding player problem-solving and decision-making processes. \nEvent Host: Zhaoqing Teng\, Ph.D. Candidate\, Computational Media  \nAdvsior: Magy Seif El-Nasr \nZoom- https://ucsc.zoom.us/j/97624383966?pwd=NGolaaTbhdytPcDK6aRIBDIv63b8lm.1 \nPasscode-595285
URL:https://events.ucsc.edu/event/teng-z-cm-visualizing-player-processes-towards-design-guidelines-for-interactive-process-visualization-tools-in-game-analytics/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260313T140000
DTEND;TZID=America/Los_Angeles:20260313T150000
DTSTAMP:20260504T061114
CREATED:20260219T170502Z
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SUMMARY:Wang\, H. (CSE) - Accelerating RTL Simulation with Specialized Graph Partitioners
DESCRIPTION:Register transfer level (RTL) simulation is an invaluable tool for developing\, debugging\, verifying\, and validating hardware designs. However\, the performance of RTL simulation has long been a limiting factor in industry. Despite the inherent parallelism of hardware\, current RTL simulators have not achieved practical performance gains due to fundamental challenges in communication\, synchronization\, memory bandwidth\, and architectural mapping. \nThis dissertation addresses the RTL simulation performance problem from three complementary perspectives: optimizing simulation latency through parallelism\, improving aggregate throughput via deduplication\, and enabling efficient GPU acceleration with RTL-native semantics. \nFirst\, we present RepCut\, a parallel RTL simulation methodology that uses replication-aided partitioning to cut circuits into balanced partitions with minimal overlaps. By replicating the overlaps\, RepCut eliminates problematic data dependences between partitions and significantly reduces synchronization overhead. RepCut achieves superlinear speedups of up to 27.10x using 24 threads with only a 3.81% replication cost. \nSecond\, we introduce Simulation Deduplication\, a technique that exploits the extensive reuse of building blocks in modern hardware designs. By generating shared code for duplicated instances and carefully co-scheduling their execution\, we reduce the instruction cache footprint and memory bandwidth pressure. This approach achieves up to 1.95x speedup for single simulations and 2.09x improvement in overall batch simulation throughput. \nThird\, we present Toucan\, a GPU-accelerated RTL simulation framework that preserves RTL semantics rather than flattening designs to gate-level netlists. By leveraging native GPU arithmetic operations and introducing warp-level micro-partitioning with shuffle-based communication\, Toucan achieves efficient mapping of irregular circuit topologies to GPU SIMT architectures while maintaining fast compilation times. Toucan achieves up to 4.73x speedup over the state-of-the-art GPU RTL simulator on large multi-core designs. \nTogether\, these three approaches provide a comprehensive solution to RTL simulation performance optimization\, demonstrating significant improvements over state-of-the-art commercial and open-source simulators across multiple hardware platforms and design scales. \nEvent Host: Haoyuan Wang\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Jose Renau \nZoom- https://ucsc.zoom.us/j/94044618343?pwd=xZkK8GmD28P2Vf8pbyl6aoOaNxxhya.1 \nPasscode- 574772
URL:https://events.ucsc.edu/event/wang-h-cse-accelerating-rtl-simulation-with-specialized-graph-partitioners/
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:20260313T100000
DTEND;TZID=America/Los_Angeles:20260313T120000
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SUMMARY:Moghadam\, M. (CE) - Constraint-Aware Scene Understanding and Trajectory Generation Using Deep Reinforcement Learning for Autonomous Vehicles
DESCRIPTION:Advanced driver-assistance systems (ADAS) are commonly organized as modular pipelines that transform raw sensor measurements into low-level actuation commands through perception\, planning\, and control. While learning-based methods have achieved state-of-the-art performance in perception and environment modeling\, the planning layer remains a key bottleneck for reliable autonomy. Highway driving in particular requires long-horizon reasoning and socially aware interaction with multiple actors\, while also producing smooth and dynamically feasible motion that can be tracked by classical controllers. \nThis thesis focuses on scene understanding and planning for highway driving. We study the problem through two complementary simulation environments: the high-fidelity CARLA simulator for motion planning and continuous trajectory generation under realistic vehicle dynamics and road geometry\, and the lightweight HighwayEnv simulator for interaction-rich behavior planning at high episode throughput. \nWe present three planning contributions that increase autonomy. First\, we introduce a modular hierarchical planning framework in Frenet space that combines long-term decision-making with short-term trajectory optimization. The approach includes a corridor-based dynamic obstacle avoidance strategy that generates spatiotemporal polynomial trajectories and supports diverse driving styles through interpretable parameter tuning. Second\, we propose an end-to-end continuous deep reinforcement learning approach that unifies decision-making and motion planning into a single policy that outputs continuous polynomial trajectories in the Frenet frame. A spatiotemporal observation tensor and a temporal convolutional backbone enable the learned planner to exploit interaction history and outperform optimization-based and discrete RL baselines in CARLA. Third\, we develop an interaction-aware behavior planning neural network architecture that couples trajectory prediction with high-level decision-making via a social pooling scene encoder built on actor histories and an ego-centered BEV representation. This unified design improves RL social awareness\, safety\, and overall driving performance in multi-agent highway scenarios in HighwayEnv. \nAcross extensive simulation studies\, the results show that constraint-aware representations and learning-based policies can improve planning quality beyond hand-crafted objectives\, especially when the policy is equipped with spatiotemporal social context while retaining classical feedback control for stable trajectory tracking. Finally\, we provide supporting simulation and evaluation infrastructure\, including observation tensor and neural network designs\, BEV utilities\, and scalable training and testing pipelines\, to enable reproducible research on learning-based planning in interactive traffic. \nEvent Host: Majid Moghadam\, Ph.D. Candidate\, Computer Engineering  \nAdvisor: Gabriel Elkaim \nZoom- https://ucsc.zoom.us/j/95848602314?pwd=2jlktZ6BChlXcyqT3anX4ZuKrYV4wE.1 \nPasscode- 325939
URL:https://events.ucsc.edu/event/moghadam-m-ce-constraint-aware-scene-understanding-and-trajectory-generation-using-deep-reinforcement-learning-for-autonomous-vehicles/
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:20260313T093000
DTEND;TZID=America/Los_Angeles:20260313T110000
DTSTAMP:20260504T061114
CREATED:20260217T203948Z
LAST-MODIFIED:20260217T203948Z
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SUMMARY:Fan\, Y. (CSE) - Building Human-Centered Multimodal AI Agents
DESCRIPTION:As multimodal artificial intelligence systems become increasingly embedded in everyday technology\, there is a growing need to design human-centered AI agents that support and amplify human capabilities rather than replace them. This dissertation investigates how to build human-centered multimodal AI agents\, framing human-centeredness as an agent-level objective that requires both accessible\, assistive interaction and reliable\, trustworthy behavior across physical and digital environments. This dissertation explores two complementary dimensions of human-centered agent design. The first focuses on enhancing accessibility through conversational and interactive agents that assist users in everyday tasks. We study both embodied and digital settings in which agents reduce physical and cognitive burdens via natural language interaction\, including hands-free drone control\, navigation assistance in unfamiliar environments\, and interactive access to complex graphical user interfaces. The second dimension focuses on strengthening agent capability to improve reliability and trust. We investigate how agents can acquire environment-specific knowledge through autonomous exploration and how they can reason about visual information in a grounded and transparent manner\, drawing inspiration from human learning and reasoning behaviors. \nEvent Host: Yue Fan\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Xin Eric Wang \nZoom- https://ucsc.zoom.us/j/99619642071?pwd=dwWOlkJxjbamgpB4IbRxYDXbngqXOE.1 \nPasscode- 467959
URL:https://events.ucsc.edu/event/fan-y-cse-building-human-centered-multimodal-ai-agents/
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260312T114000
DTEND;TZID=America/Los_Angeles:20260312T131500
DTSTAMP:20260504T061114
CREATED:20260303T000204Z
LAST-MODIFIED:20260303T000204Z
UID:10009382-1773315600-1773321300@events.ucsc.edu
SUMMARY:BME 280B Seminar: Modulating Insulin Receptor Through New Ligands
DESCRIPTION:Presenter: Danny Chou\, Associate Professor of Pediatrics\, Stanford University \nDescription: Since its discovery in 1921\, insulin has been at the forefront of scientific breakthroughs. From its amino acid sequencing to the revelation of its three‐dimensional structure\, the progress in insulin research has spurred significant therapeutic breakthroughs. In recent years\, protein engineering has introduced innovative chemical and enzymatic methods for insulin modification\, fostering the development of therapeutics with tailored pharmacological profiles. In this seminar\, I will highlight the use of new ligands to modulate insulin receptors and discuss how they continue to shape the future trajectory of insulin research. \nBio: Danny Chou is an Associate Professor of Pediatrics and by courtesy\, of Chemical & Systems Biology at Stanford University. He is an affinity group leader at Stanford Diabetes Research Center. His research interests lie in the intersection of peptide therapeutics and metabolic diseases. He started his independent career as an assistant professor of biochemistry at University of Utah in 2014. He moved his lab to Stanford University in 2020 and continued their pursuit of using peptide and protein chemistry to develop therapeutics to address unmet needs.  \nHosted by: Professor Andy Yeh\, BME Department
URL:https://events.ucsc.edu/event/bme-280b-seminar-modulating-insulin-receptor-through-new-ligands/
LOCATION:Physical Sciences Building\, Physical Sciences Building\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/02/BElogoWHITE.png
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X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Physical Sciences Building Physical Sciences Building Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Physical Sciences Building:geo:-122.0618552,36.9996638
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260311T110000
DTEND;TZID=America/Los_Angeles:20260311T130000
DTSTAMP:20260504T061114
CREATED:20260311T160620Z
LAST-MODIFIED:20260311T160620Z
UID:10011304-1773226800-1773234000@events.ucsc.edu
SUMMARY:Yang\, S. (CSE) - Beyond Image Editing: Building Generalized Image Customization Systems
DESCRIPTION:Current generative vision models struggle with image customization that requires multi-step reasoning or real-world knowledge. This proposal introduces generalized image customization\, enabling systems to execute complex\, inferential modifications rather than just simple edits. The research focuses on the foundational framework required for this generalization\, specifically high-quality training data\, scalable evaluation benchmarks\, self-improving training paradigms that reduce reliance on paired annotations\, and unified multi-modal architectures. Building on two completed studies in data quality and evaluation\, this proposal outlines two future research directions to develop capable\, annotation-efficient\, and reasoning-native image customization systems. \nEvent Host: Siwei Yang\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Cihang Xie \nZoom- https://ucsc.zoom.us/j/3852138080?pwd=Z0MyTVM2WjdCbEM4OXVxWUhhei84dz09 \n 
URL:https://events.ucsc.edu/event/yang-s-cse-beyond-image-editing-building-generalized-image-customization-systems/
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option-1.jpg
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260311T110000
DTEND;TZID=America/Los_Angeles:20260311T121500
DTSTAMP:20260504T061114
CREATED:20260303T181914Z
LAST-MODIFIED:20260303T181914Z
UID:10009389-1773226800-1773231300@events.ucsc.edu
SUMMARY:CSE Colloquium: Co-Active AI-Assisted Programming
DESCRIPTION:Presenter: Nadia Polikarpova\, UCSD \nAbstract: \nAI-assisted programming has rapidly moved from novelty to default. Today\, most developers use AI coding tools\, and increasingly rely on agentic systems capable of making multi-step design and implementation decisions with minimal human guidance. While these systems boost productivity\, they also introduce new risks: developers may disengage from the reasoning behind generated code\, leading to shallow understanding\, loss of ownership\, and what is increasingly described as cognitive debt. \nIn this talk\, I argue that AI-driven software development must be co-active: humans and AI should remain continuously engaged in a shared process of understanding and decision-making. I will present two complementary research directions toward this goal. The first focuses on observability—helping developers understand\, validate\, and trace the behavior of AI-generated code. The second focuses on controllability—making AI decisions explicit\, persistent\, and steerable. Together\, these ideas restore programmer agency while maintaining the productivity gains of AI-assisted development. \nBio: \nNadia Polikarpova is an associate professor at UC San Diego\, and a member of the Programming Systems group. She received her Ph.D. in Computer Science from ETH Zurich in 2014\, and then spent some time as a postdoctoral researcher at MIT. Nadia’s research interests are at the intersection of programming languages\, AI\, human-computer interaction\, and social computing. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, Room E2-180 (*Refreshments such as coffee\, tea\, pastries\, and fresh fruit will be available.) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-co-active-ai-assisted-programming/
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:20260310T163000
DTEND;TZID=America/Los_Angeles:20260310T173000
DTSTAMP:20260504T061114
CREATED:20260217T184921Z
LAST-MODIFIED:20260217T184921Z
UID:10009240-1773160200-1773163800@events.ucsc.edu
SUMMARY:Mashhadi\, N. (CSE) - Compositional\, Clinically Conditioned\, and Confound-Aware Deep Learning for Alzheimer’s Disease Neuroimaging
DESCRIPTION:Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia. Neuroimaging and clinical biomarkers can reveal early disease changes\, but building reliable machine learning models is difficult because data come from different scanners and sites\, some modalities are missing\, labeled cohorts are limited\, and factors such as age and scanner/site effects can bias results. \nThis dissertation develops machine learning methods for robust\, interpretable\, and controllable analysis of AD-related neuroimaging data. First\, I introduce a modular\, graph-based framework for multimodal AD detection that treats datasets and models as nodes and directed edges that can be combined to build more complex predictors. Second\, I propose a clinically conditioned 3D VAE-GAN to synthesize brain MRI\, enhanced with diffusion-driven sampling in clinical feature space to improve realism and control\, supporting data augmentation. Third\, I present a disentangled 3D masked autoencoder (MAE) that learns separated representations for age\, pathology\, and scanner effects\, making it possible to isolate and adjust age\, pathology\, or scanner effects\, while remaining reliable across sites. \nTogether\, these contributions advance practical methods for modular prediction\, controllable image generation\, and confound-aware representation learning in neuroimaging\, with an emphasis on generalization and interpretability for clinically relevant applications. \nEvent Host: Najmeh Mashhadi\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Razvan Marinescu \nZoom- https://ucsc.zoom.us/j/98195204428?pwd=nyfvbmd9t81Xj5Z3yPPVtu4R58CXHq.1 \nPasscode- 688069
URL:https://events.ucsc.edu/event/mashhadi-n-cse-compositional-clinically-conditioned-and-confound-aware-deep-learning-for-alzheimers-disease-neuroimaging/
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260504T061114
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/
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:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260504T061114
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/
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:20260309T140000
DTEND;TZID=America/Los_Angeles:20260309T160000
DTSTAMP:20260504T061114
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/
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:20260309T104000
DTEND;TZID=America/Los_Angeles:20260309T114500
DTSTAMP:20260504T061114
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:20260309T100000
DTEND;TZID=America/Los_Angeles:20260309T110000
DTSTAMP:20260504T061114
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/
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option-1.jpg
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T080000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260504T061114
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/
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:20260309T070000
DTEND;TZID=America/Los_Angeles:20260309T080000
DTSTAMP:20260504T061114
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/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260305T130000
DTEND;TZID=America/Los_Angeles:20260305T150000
DTSTAMP:20260504T061114
CREATED:20260217T182432Z
LAST-MODIFIED:20260217T182432Z
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SUMMARY:Xu\, Y. (CSE) - Right Place\, Right Time: Accelerating Edge Computation on Modern Heterogeneous SoCs
DESCRIPTION:Modern edge computing increasingly relies on heterogeneous System-on-Chip (SoC) architectures. These chips tightly integrate general-purpose CPUs with various specialized accelerators\, including GPUs\, FPGAs\, and AI accelerators\, all under a shared memory architecture. Although these shared-memory SoCs enable more efficient communication and data sharing between different processing units\, they are notoriously difficult to program and tune due to architectural diversity across vendors and asymmetric compute capabilities within each SoC. \nThis dissertation introduces Redwood and BetterTogether\, two frameworks that rethink CPU-accelerator collaboration on heterogeneous SoCs. Redwood targets a class of algorithms termed traverse–compute\, that combine irregular tree traversals with dense leaf-level computation\, e.g.\, Nearest-Neighbor Search and Barnes–Hut algorithm. \nIt addresses the efficient mapping of these algorithms onto heterogeneous systems by exploiting the architectural strengths of CPUs\, GPUs\, and FPGAs. BetterTogether extends this methodology to a different class of edge workloads\, specifically multi-stage pipelines and neural networks commonly used in computer vision tasks. Furthermore\, it introduces interference-aware analysis and scheduling techniques tailored for mobile SoCs. Finally\, to broaden the scope of heterogeneous acceleration\, we evaluated emerging domain-specific accelerators. We provide a preliminary analysis of Tensor Processing Units and Tensor Cores within the context of modern programming abstractions. \nEvent Host: Yanwen Xu\, Ph.D. Candidate\, Computer Science and Engineering \nAdvisor: Tyler Sorensen \nZoom- https://ucsc.zoom.us/j/5354629158?pwd=0CVhbwLuXDMX5fAGZd63tcfNqDWp0t.1 \nPasscode- 114514
URL:https://events.ucsc.edu/event/xu-y-cse-right-place-right-time-accelerating-edge-computation-on-modern-heterogeneous-socs/
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:20260305T114000
DTEND;TZID=America/Los_Angeles:20260305T131500
DTSTAMP:20260504T061114
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SUMMARY:BME 280B Seminar: Artificial intelligence systems to advance engineered T cell immunotherapy designs
DESCRIPTION:Presenter: Zinaida Good\, Assistant Professor of Medicine in the Division of Immunology and Rheumatology and the Division of Computational Medicine\, Stanford University \nDescription: T cell immunotherapies have reshaped the treatment landscape for hematologic malignancies and are rapidly extending to solid tumors\, autoimmune diseases\, and transplant tolerance. Yet durable benefit remains inconsistent\, and toxicities remain clinically significant. The current discovery proceeds one edit at a time\, and existing preclinical models do not represent patient biology\, which often results in failure upon clinical translation. Overcoming these challenges to improve patient outcomes and reduce toxicities requires a systems-level understanding of the multiscale factors governing T cell function and toxicity in patients. Artificial intelligence (AI) approaches offer an exciting opportunity to tackle this problem by learning unified representations from diverse data types spanning molecular\, cellular\, and clinical modalities. I will provide an overview on our team’s approaches building AI systems that harness primary patient datasets to directly inform advanced T cell designs optimized for clinical outcomes\, with validation in preclinical models. \nBio: Zinaida Good\, Ph.D.\, is an Assistant Professor of Medicine in the Division of Immunology and Rheumatology and the Division of Computational Medicine at Stanford University. She also serves as the Director of the Stanford Center for Cancer Cell Therapy Data Hub. The goal of her research program is to understand and enhance engineered T cell immunotherapies for cancer and immune-mediated diseases through innovative computational approaches and systems immunology. Her lab leverages innovation in machine learning and clinical multiomic datasets to build artificial intelligence systems for advanced T cell therapy design. Dr. Good earned her Ph.D. in Computational & Systems Immunology from Stanford University. Her work includes 4 first-author papers (Nature Medicine 2018 & 2022\, Nature Biotechnology 2019\, Trends in Immunology 2019)\, 18+ co-authored papers (including Nature 2019\, 2022\, 2024\, Science 2021\, Nature Methods 2016\, 2022\, and NEJM 2024)\, and an initial senior author papers (ICML 2025\, NeurIPS 2025\, Frontiers in Immunology 2025). Her research is supported by the NIH/NCI Pathway to Independence Award\, NIH/OD Multimodal AI Initiative Award\, NIH/NCI Program Project Grant\, and the Weill Cancer Hub West. Dr. Good has been named an Arthur & Sandra Irving Cancer Immunology Fellow in 2022\, Parker Bridge Fellow in 2023\, and an AACR-Woman in Cancer Research Scholar in 2024. \nHosted by: Professor Vanessa Jonsson\, BMEbe Department
URL:https://events.ucsc.edu/event/bme-280b-seminar-artificial-intelligence-systems-to-advance-engineered-t-cell-immunotherapy-designs/
LOCATION:Biomedical Sciences\, Biomedical Sciences Building Red Hill Road\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260304T173000
DTEND;TZID=America/Los_Angeles:20260304T193000
DTSTAMP:20260504T061114
CREATED:20260203T172611Z
LAST-MODIFIED:20260227T223629Z
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SUMMARY:The UC Santa Cruz Kraw Lecture Series presents: Unmasking cancer's complete genetic code
DESCRIPTION:In this Kraw lecture\, Angela Brooks will discuss her work on cancer research. \nCurrent cancer research focuses almost entirely on finding errors—mutations—in DNA. This has given us incredible tools like precision oncology\, matching patients with targeted drugs. But cancer cells almost always develop drug resistance\, causing treatments to fail and limiting patient survival. An often-overlooked aspect of cancer genes is the messenger RNA\, which is copied from DNA\, then translated into protein to do the work of the cell. Over 95% of human genes have isoforms\, which are different versions of the RNA message created through a process called RNA splicing. These different messages lead to slightly different proteins\, and we believe our lack of knowledge of different isoforms is a missing cause of treatment failure. \n\nIn-Person Reception: 5:30 p.m.\nLecture: 6–7 p.m.\n\nREGISTER
URL:https://events.ucsc.edu/event/the-uc-santa-cruz-kraw-lecture-series-presents-unmasking-cancers-complete-genetic-code/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Lectures & Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260304T130000
DTEND;TZID=America/Los_Angeles:20260304T170000
DTSTAMP:20260504T061114
CREATED:20260217T222743Z
LAST-MODIFIED:20260219T210101Z
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SUMMARY:Shields\, S. (CM) - Procedural\, Player-Centric Game Balancing
DESCRIPTION:Game balance is a term widely used among players\, researchers\, and designers of games. It is a concept that feels vitally important to how we make and play games – but when we try to define it or implement it\, we seldom get the same definition twice. Balance appears differently to whoever is judging it\, but as researchers and designers we still must translate this element of game design into technical practice. It also is an expensive and time-consuming subject\, one that requires a constant loop of playtesting and design iteration through nearly the entirety of the game development process. \nThis work seeks to focus our understanding of balance while offering procedural methods to either increase speed or improve quality when performing balancing tasks in game design and research. It accomplishes this by offering a taxonomy of balance alongside a generic design framework that can be used to apply balancing strategies to any game context. It additionally provides a catalog of balancing methods\, allowing designers to use common patterns to apply procedural balancing to their games. Finally\, I offer three technical examples using the taxonomy and framework\, putting theoretical knowledge of balance into concrete technical systems. \nBalance ultimately helps us design games that make us feel fairness in our play. By sharpening and optimizing our understanding of the term\, we improve the games we make and open new doors in game systems design. \nEvent Host: Sam Shields\, Ph.D. Candidate\, Computational Media  \nAdvisor: Edward F. Melcer \nZoom- https://ucsc.zoom.us/j/98956788669?pwd=ao7DzYQebCeS3SJ4PsGaZeGYhYMVNI.1 \nPasscode- 713173
URL:https://events.ucsc.edu/event/shields-s-cm-procedural-player-centric-game-balancing/
LOCATION:Merrill College\, College Office\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260304T120000
DTEND;TZID=America/Los_Angeles:20260304T130000
DTSTAMP:20260504T061114
CREATED:20250923T070000Z
LAST-MODIFIED:20251001T224502Z
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SUMMARY:Engineering Teaching Community (Faculty)
DESCRIPTION:During the chaos of a quarter\, is it hard to find time to reflect and improve as an instructor? Would you like to be a part of an inclusive\, supportive group of engineering instructors who do this in community? ETC is for sharing teaching experiences\, classroom ideas\, research on learning\, and methods that support instructors and students. All are welcome\, and lunch is provided. Please reach out to Jenny Quynn with questions.
URL:https://events.ucsc.edu/event/engineering-teaching-community-faculty/2026-03-04/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Meetings & Conferences,Training
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260304T110000
DTEND;TZID=America/Los_Angeles:20260304T121500
DTSTAMP:20260504T061114
CREATED:20260217T182353Z
LAST-MODIFIED:20260217T182353Z
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SUMMARY:CSE Colloquium - Improving Efficiency and Reliability of Foundation Models in Clinical AI
DESCRIPTION:Presenter: Vasiliki “Vicky” Bikia\, PhD\, Stanford Department of Biomedical Data Science and Institute for Human-Centered AI (HAI) \nAbstract: \nDeploying foundation models in health requires both computational efficiency and reliable generation. In this talk\, I present two studies that address these dimensions separately but with a shared goal of real-world clinical deployment. The first study focuses on reduced-resolution distillation for multimodal clinical data\, particularly medical imaging. As model and input sizes increase\, inference cost and memory constraints become major barriers to deployment. We investigate how high-capacity teacher models can transfer structured knowledge to compact student models trained on downsampled images\, using embedding-space supervision to preserve clinically meaningful representations while reducing computational footprint. The second study examines the reliability of AI-generated clinical text. Foundation models are increasingly used to produce discharge summaries and patient-facing explanations\, yet fluency does not guarantee safety. We develop a structured evaluation framework grounded in clinical error taxonomies and clinician-calibrated metrics to quantify hallucinations\, omissions\, and semantic misalignment. Together\, these studies emphasize that scalable clinical AI requires not only smaller and faster models\, but also rigorous evaluation of generative reliability before deployment. \nBio: \nVasiliki Bikia is a Postdoctoral Researcher at Stanford University\, affiliated with the Department of Biomedical Data Science and the Stanford Institute for Human-Centered Artificial Intelligence (HAI). She received an Advanced Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki\, and a Ph.D. in Bioengineering from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her research focuses on medical foundation models\, structured representations of health data\, and the evaluation of generative systems in clinical settings. Previously\, she was a Machine Learning Scientist at the Mussallem Center for Biodesign at Stanford University\, where she developed software pipelines to improve data accessibility and interoperability in digital health applications. Vasiliki was selected as an MIT Rising Star in EECS (2025) and as an Emerson Consequential Scholar (2025)\, and is actively engaged with the Silicon Valley entrepreneurial ecosystem through collaborations at the intersection of research\, industry\, and healthcare. She is an organizing member of the Conference on Health\, Inference\, and Learning (CHIL) and serves as Unconference Chair for the 2025 and 2026 editions\, where she leads the design and execution of the entrepreneurship-focused track bridging academic research and real-world deployment. Her work has appeared in venues including IEEE journals\, npj Digital Medicine\, Nature Communications\, and leading AI conferences\, and she has contributed to multiple funded research proposals and clinical studies at the intersection of AI\, medicine\, and translational impact. \nHosted by: Professor Nikos Tziavelis \nLocation: Engineering 2\, E2-180 (*Refreshments such as coffee\, tea\, fresh fruit\, and pastries will be provided) \nZoom: https://ucsc.zoom.us/j/93445911992?pwd=YkJ2TQtF79h0PcNXbEcpZLbpK0coiY.1&jst=3
URL:https://events.ucsc.edu/event/cse-colloquium-improving-efficiency-and-reliability-of-foundation-models-in-clinical-ai/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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