Events

The UC Santa Cruz Kraw Lecture Series presents: Unmasking cancer's complete genetic code
2026-03-04 17:30 - 19:30 (America/Los_Angeles) | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
In this Kraw lecture, Angela Brooks will discuss her work on cancer research.
Current 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.

In-Person Reception: 5:30 p.m.
Lecture: 6–7 p.m.

REGISTER

Unexpected Returns: The Historic Entanglements of Fire, Settlement, and Stewardship in the Santa Cruz Mountains
2026-03-04 18:00 - 19:30 | Institute of the Arts and Sciences, 100 Panetta Ave, Santa Cruz, United States
Join UCSC faculty members Miriam Greenberg and Andrew Matthews as they discuss the deep regional histories of fire, from indigenous burning, settler ranching, fire suppression, and much more.

This event is part of Intersections of Climate Change, a series organized with the Friedlaender Lab in conjunction with Weather and the Whale.

ADMISSION
– FREE and open to the public
PARKING
– The entrance to the Institute of the Arts and Sciences Galleries is on Delaware Street and has an accessibility ramp.
– Convenient and free self-parking is available on Panetta Avenue and High Road, immediately adjacent to the galleries.
– Accessible parking is on High Road.
—
This program is open to all members of the public consistent with state and federal law.

BME 280B Seminar: Artificial intelligence systems to advance engineered T cell immunotherapy designs
2026-03-05 11:40 - 13:15 | Biomedical Sciences, Biomedical Sciences Building Red Hill Road, Santa Cruz, CA, 95064
Presenter: Zinaida Good, Assistant Professor of Medicine in the Division of Immunology and Rheumatology and the Division of Computational Medicine, Stanford University
Description: 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.
Bio: 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.
Hosted by: Professor Vanessa Jonsson, BMEbe Department

Xu, Y. (CSE) - Right Place, Right Time: Accelerating Edge Computation on Modern Heterogeneous SoCs
2026-03-05 13:00 - 15:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
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.
This 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.
It 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.
Event Host: Yanwen Xu, Ph.D. Candidate, Computer Science and Engineering
Advisor: Tyler Sorensen
Zoom- https://ucsc.zoom.us/j/5354629158?pwd=[REDACTED]
Passcode- 114514

Science in the Neighborhood: Transforming Pacific salmon recovery, from genes to ecosystems
2026-03-06 18:00 - 19:30 | Coastal Biology Building, 130 McAllister Way, Santa Cruz, CA, 95060
Science In the Neighborhood
A public lecture series hosted quarterly by the UC Santa Cruz Science Division
Transforming Pacific salmon recovery, from genes to ecosystems
Presentation by Eric Palkovacs, Professor, UC Santa Cruz
Q&A with Bryan Gaensler, Dean of Science, UC Santa Cruz
Register here.
An endangered Central California Coast coho salmon from the Scott Creek recovery program that UCSC operates in collaboration with NOAA. Photo credit: Joel Sartore / National Geographic Photo Ark.
For millennia, Pacific salmon have been integral to the health of coastal ecosystems and human communities from California to Alaska. Salmon are ecological and cultural keystone species, connecting marine and freshwater food webs and supporting thriving fisheries. Yet, wild salmon have declined precipitously due to a combination of factors including dams, harvest, hatcheries, water use—and now, climate change.
Professor Palkovacs, who leads UC Santa Cruz’s Institute of Marine Sciences and Fisheries Collaborative Program, will describe transformative approaches to recover wild salmon populations by connecting novel insights from the level of genes to ecosystems. Learn how this integrative research program can provide insights to transform the future for wild Pacific salmon and the ecosystems and fisheries they support.
The event is in-person only. Register here.
March 6, 2026 | 6:00–7:30 p.m.
Coastal Biology Building. Rm. 110
UC Santa Cruz Coastal Campus
130 McAllister Way
Santa Cruz, CA 95060
The screenshot below shows where to find the entrance of the Coastal Biology Building.

First Saturday Tour at the Arboretum
2026-03-07 11:00 - 12:00 | Arboretum, 122 Arboretum Road, Santa Cruz, CA, 95064
First Saturday Tours are a wonderful way to introduce yourself to the Arboretum or to deepen your knowledge of the Arboretum’s plant collections. Each tour is a little different depending on the time of year, the interests of the tour guide, and the people who join in. For example, you might learn about the birds and mammals that make this land their home or about the amazing physical adaptations that plants have evolved to better deal with our extreme weather and climate conditions. Tours are free with paid admission.

Hendawy, M. (CM) - Autonoming Child Online Safety in the Age of AI: From Control to Digital Co-Agency Across Cultures
2026-03-09 07:00 - 08:00
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).
Event Host: Mennatullah Hendawy, Ph.D. Student, Computational Media
Advisor: Magy Seif El-Nasr
Zoom- https://ucsc.zoom.us/j/93831600031?pwd=[REDACTED]
Passcode-459844

Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
2026-03-09 08:00 - 17:00, 2026-03-09 16:00 - 17:00
Presenter: Amanda Coston, Assistant Professor, University of California Berkeley
Description: 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.
Bio: 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.
Hosted by: Statistics Department

Robbins, A. (ECE) - How to train your organoid: goal-directed learning in biological neural networks
2026-03-09 10:00 - 11:00
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.
Event Host: Ash Robbins, Ph.D. Candidate, Electrical and Computer Engineering
Advisor: Mircea Teodorescu
Zoom- https://ucsc.zoom.us/j/95839863615?pwd=[REDACTED]
Passcode- 069118

ECE 290 Seminar: Dynamical Signatures: Harnessing the Hidden Language of In-Space Electric Propulsion
2026-03-09 10:40 - 11:45 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Dr. Christine Greve, Research Engineer, Edwards AFB
Description: 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.
Bio: 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.
Hosted by: Professor Soumya Bose, ECE Department
Zoom: https://ucsc.zoom.us/j/97975378707?pwd=[REDACTED]

Harrison, D. (CS) - Multi-Level Control in Neural Dialogue Generation: Style, Semantics, and Selection through Over-Generation and Ranking
2026-03-09 14:00 - 16:00
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.
First, 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.
A 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.

Event Host: Davan Harrison, Ph.D. Candidate, Computer Science
Advisor: Marilyn Walker

AM Seminar: Solution Discovery in Fluids with High Precision Using Neural Networks
2026-03-09 16:00 - 17:00
Presenter: Ching-Yao Lai, Assistant Professor, Stanford University
Description: 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.
Bio: 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.
Hosted by: Applied Mathematics

Mashhadi, N. (CSE) - Compositional, Clinically Conditioned, and Confound-Aware Deep Learning for Alzheimer’s Disease Neuroimaging
2026-03-10 16:30 - 17:30
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.
This 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.
Together, 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.
Event Host: Najmeh Mashhadi, Ph.D. Candidate, Computer Science and Engineering
Advisor: Razvan Marinescu
Zoom- https://ucsc.zoom.us/j/98195204428?pwd=[REDACTED]
Passcode- 688069

CSE Colloquium: Co-Active AI-Assisted Programming
2026-03-11 11:00 - 12:15 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Nadia Polikarpova, UCSD
Abstract:
AI-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.
In 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.
Bio:
Nadia 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.
Hosted by: Professor Nikos Tziavelis
Location: Engineering 2, Room E2-180 (*Refreshments such as coffee, tea, pastries, and fresh fruit will be available.)
Zoom: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

Yang, S. (CSE) - Beyond Image Editing: Building Generalized Image Customization Systems
2026-03-11 11:00 - 13:00
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.
Event Host: Siwei Yang, Ph.D. Student, Computer Science and Engineering
Advisor: Cihang Xie
Zoom- https://ucsc.zoom.us/j/3852138080?pwd=[REDACTED]

What's new in AI?
2026-03-11 18:00 - 19:00 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Chat with the AI chair
Lead innovation as a machine learning engineer
Want to learn what’s new in AI? Join Praveen Krishna, chair of the Artificial Intelligence Application Development certificate program, in an informal discussion about the AI topic of the month and an open Q&A. You’ll get an insider’s look at what you need to know for where you want to go. Bring your questions. Share your experience. Plan your learning.
Every 2nd Wednesday | Online
Visit our program page for a closer look at our AI App Development courses and offerings.
Register now.

BME 280B Seminar: Modulating Insulin Receptor Through New Ligands
2026-03-12 11:40 - 13:15 | Physical Sciences Building, Physical Sciences Building, Santa Cruz, CA, 95064
Presenter: Danny Chou, Associate Professor of Pediatrics, Stanford University
Description: 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.
Bio: 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.
Hosted by: Professor Andy Yeh, BME Department

[HSI Equity Talk] Beyond Invisibility: Reimagining Servingness to Support Undocumented Transfer Students
2026-03-12 15:30 - 17:00 | https://events.ucsc.edu/event/hsi-equity-talk-beyond-invisibility-reimagining-servingness-to-support-undocumented-transfer-students/
Presenters: Valeria Alonso Blanco, Dr. Saskias Casanova, and Jesus Morales
This interactive talk draws on a qualitative study conducted with undocumented Latinx transfer students at a California four-year HSI. Using focus groups and reflexive thematic analysis, we examined how these students perceive and experience institutional support, belonging, and barriers related to their intersectional identities. This session is designed around three goals: (1) assessing what staff and administrators currently know about undocumented students on campus, particularly undocumented transfer students; (2) building shared knowledge through our research findings and student voices; and (3) connecting participants to existing campus resources and identifying concrete opportunities for cross-departmental collaboration. We will share key resources and actionable strategies for holistically serving undocumented transfer students.
Please register online. The Zoom information and a calendar invitation will be sent to those who RSVP.
You Belong Here: The programs and services described here are open to all, consistent with state and federal law, as well as the University of California’s nondiscrimination policies. Every initiative—whether a student service, faculty program, or community event—is designed to be accessible, inclusive, and respectful of all identities.
To learn more, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.

Your path to medical school starts here.
2026-03-12 18:00 - 19:00 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Are you considering medical school and seeking a clear, supportive path forward?
Join the University of California, Santa Cruz, for a free online informational session to learn more about our Summer 2026 Premed Postbacc Cohort Programs. This session is designed for students and graduates who are serious about pursuing a career in health care and want expert guidance through the medical school preparation and application process.
During this session, you’ll learn how the UCSC Premed Postbacc Program provides a structured, cohort-based experience—offered in Silicon Valley and Marina on the Central Coast—to help students strengthen their academic foundation, prepare for medical school, and apply with confidence.
Claim your seat.

Fan, Y. (CSE) - Building Human-Centered Multimodal AI Agents
2026-03-13 09:30 - 11:00
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.
Event Host: Yue Fan, Ph.D. Candidate, Computer Science and Engineering
Advisor: Xin Eric Wang
Zoom- https://ucsc.zoom.us/j/99619642071?pwd=[REDACTED]
Passcode- 467959

Moghadam, M. (CE) - Constraint-Aware Scene Understanding and Trajectory Generation Using Deep Reinforcement Learning for Autonomous Vehicles
2026-03-13 10:00 - 12:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
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.
This 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.
We 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.
Across 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.
Event Host: Majid Moghadam, Ph.D. Candidate, Computer Engineering
Advisor: Gabriel Elkaim
Zoom- https://ucsc.zoom.us/j/95848602314?pwd=[REDACTED]
Passcode- 325939

Wang, H. (CSE) - Accelerating RTL Simulation with Specialized Graph Partitioners
2026-03-13 14:00 - 15:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
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.
This 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.
First, 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.
Second, 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.
Third, 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.
Together, 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.
Event Host: Haoyuan Wang, Ph.D. Candidate, Computer Science and Engineering
Advisor: Jose Renau
Zoom- https://ucsc.zoom.us/j/94044618343?pwd=[REDACTED]
Passcode- 574772

Wildlife Photography from Hummingbirds to Elephants - a photo presentation by Kevin Lohman
2026-03-14 13:00 - 14:30
Join Kevin for “Wildlife Photography from Hummingbirds to Elephants” at the UC Santa Cruz Arboretum & Botanic Garden in the Horticulture 2 Meeting Hall. He will offer tips for photographing hummingbirds, share beautiful images taken at the arboretum, and present highlights from his wildlife photography across Monterey Bay and from his travels around the globe.
Kevin Lohman is an award-winning wildlife photographer based in Santa Cruz, California. One of his hummingbird photographs taken at the UC Santa Cruz Arboretum & Botanic Garden is among several of his images featured locally on a Santa Cruz METRO wildlife bus as part of the “One Ride at a Time” conservation campaign. His work has been published by National Geographic (online) and Outdoor Photographer, and recognized in major competitions including the Audubon Photography Awards and Nature’s Best International Photography Awards. Lohman actively supports conservation organizations through the donation and use of his photography.
The event is free with paid admission. UCSC students and faculty receive free admission to the Arboretum. Public admission costs are: Adults $10, Seniors $8, and Youth (4-17) $5.

OLLI at UCSC Sunday Speaker Meeting
2026-03-15 10:00 - 12:00 | Colleges Nine and John R. Lewis College Multi-purpose Room, 615 College Nine Road, Santa Cruz, CA, 95064
OLLI at UCSC Hosts Bruce Lyon, Professor, Ecology & Evolutionary Biology
The Osher Lifelong Learning Institute (OLLI) at UC Santa Cruz invites the public to an in-person presentation from 10:00 to noon on Sunday, March 15th, in the Colleges Nine/Lewis Multipurpose Room at UCSC.
Join us for a social hour and a presentation titled, Sparrows in the Mist: Complex Winter Social Behavior in a Little Brown Bird.
Ornithologists often study breeding birds, and as a result, the winter ecology and social behavior of migratory birds is relatively understudied. A 22-year study of a population of golden-crowned sparrows that winter in the Arboretum of the University of California, Santa Cruz was conducted. Each sparrow was given a unique combination of color bands, which allowed us to identify individual birds in the wild and monitor their behavior and survival. Given that they survive migration and breeding, individual sparrows return winter after winter to the arboretum and flock together with the same individuals as in previous years. We used innovative social network methods to understand the patterns of social organization and discovered remarkable social complexity in these birds that is similar in some ways to the societies of mammals, including primates.
Professor Lyon seeks to understand the ecological and evolutionary basis of reproductive strategies and social behavior in animals, particularly reproductive parasitism, parental care, and mating systems. One focus is to understand patterns of cooperation and reproductive parasitism in birds and insects. His passion for photography has also been very useful in his research.
OLLI at UCSC is a community of adults from diverse educational, occupational, and geographic backgrounds who are devoted to the pursuit of learning. This event is free and open to the public. Bring a friend. Coffee and nibbles will be served.
For directions and free parking information, visit: https://olli.ucsc.edu/monthly-gatherings/location-directions-and-parking/
Free and open to the public

Presented by the Osher Lifelong Learning Institute (OLLI) at UC Santa Cruz.

Teng, Z. (CM) - Visualizing Player Processes: Towards Design Guidelines for Interactive Process Visualization Tools in Game Analytics
2026-03-16 08:00 - 10:00
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.
The 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.
The 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.
Event Host: Zhaoqing Teng, Ph.D. Candidate, Computational Media
Advsior: Magy Seif El-Nasr
Zoom- https://ucsc.zoom.us/j/97624383966?pwd=[REDACTED]
Passcode-595285

AI-Enhanced Project Management Workshop
2026-03-21 09:00 - 15:00 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Transform the way you manage projects
Join Sri Srinivasa, program management leader at Tesla, for a one-day workshop focused on leveraging generative AI tools for project management.
Workshop goals

Describe and discuss AI technology available to Project Managers
Explain the transformative potential of Generative AI in improving project planning, resource allocation, and stakeholder communication
Demonstrate an ability to properly and effectively use generative AI for project management tasks and processes, reducing errors, optimizing workflows, and saving time and resources

Topics

Benefits of adopting generative AI for project management
Effective prompt engineering to optimize generative ai usage along with case examples
Identify use cases for generative AI in project management
AI-driven insights for KPI & project performance measurement

Here’s the workshop syllabus.
Students are required to bring laptops for class exercises.
About the AI Technology Workshop Series
The UCSC Silicon Valley Extension AI Technology Workshop Series provides an immersive AI experience. Students are introduced to new and established AI tools create and manipulate content in new and powerful ways. Each session is led by an industry expert who will guide you through the material and share its real-world implications.

Explore lots of AI courses
Project management courses
Workshops

Enroll today.

Santa Cruz Pickwick Club presents: Bleak House
2026-03-22 13:00 - 15:00
Spontaneous human combustion! Evil lawyers! Detectives! Family intrigue! These all come together in Charles Dickens’s masterwork, Bleak House. This year, we will spend the year reading the 2026 Dickens Universe novel. Join Dickens enthusiasts and Pickwick Club members on Zoom for a series of discussions about this beloved book.
Register via Zoom
Reading Schedule:

OCT 26: Chapters 8-13
NOV 23: Chapters 14-19
DEC 28: No meeting
JAN 25: Chapters 20-25
FEB 22: Chpaters 26-32
MAR 22: Chapters 33-38
APR 26: Chapters 39-46
MAY 24: Chapters 47-53
JUN 28: Chapters 54-67 (End)

Recommended Edition: We recommend the Penguin Classics edition of the novel for its appendices and notes, but other versions are fine. First-time readers should avoid the Introduction if they don’t want spoilers. Download the novel to read at Gutenburg.org or listen to it at LibriVox.org.
The Santa Cruz Pickwick (Book) Club, a branch of the Dickens Fellowship, is a community of local bookworms, students, and teachers who meet monthly to discuss a nineteenth-century novel. The Santa Cruz Public Libraries provide support for the reading group.

Community Conversation with author Randy Ribay
2026-03-23 16:30 - 18:00
Please join us on Monday, March 23, 2026 for a community conversation with Randy Ribay, young adult fiction writer and National Book Award Finalist. During the event, Randy will discuss his recent novel, Everything We Never Had (2024), about four-generations of Filipino American men grappling with identity, masculinity, and father-son relationships. The characters’ stories traverse histories of Filipino America, including those of Watsonville and the Pajaro Valley. Randy conducted archival research using the Watsonville is in the Heart Community Digital Archive and other repositories to create intergenerational narratives of Filipino American migration, labor, and family.
Our conversation will take place in the Community Room located on the Fourth Floor of the Watsonville Civic Plaza. This event is free and open to the public. It is co-sponsored by Watsonville Public Library, UCSC’s Center for Labor and Community, Watsonville is in the Heart, The Tobera Project, and Pajaro Valley for Ethnic Studies and Justice.

The Future of Work in the Age of AI: March Slugs and Steins with Dean PK Agarwal
2026-03-23 18:30 - 20:00
The Future of Work in the Age of AI: Chaos, Adaptation, or Growth?
Artificial 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.
REGISTER
