Will Work for Food
2026-02-26 18:00 - 20:00 (America/Los_Angeles) | Hay Barn, 94 Ranch View Road, Santa Cruz, CA, 95064, United States
Join us for a conversation on labor, food, and justice with Will Work for Food co-author Laura-Anne Minkoff-Zern and regional food production and service workers Veronica Rodriguez (Dole, Soledad), and X (Verve, Santa Cruz). Together, they will explore the often-overlooked role of labor in building a more just food system. Free and open to the public. Translation provided.

Semiconductor Career Summit - From Campus to Silicon Valley
2026-02-27 09:45 - 16:00 | Stevenson Event Center, Stevenson Service Road, Santa Cruz, CA, 95064
A SEMI Professional Development Seminar organized by the SEMI Silicon Valley Chapter – Connecting College Students to the Semiconductor Industry. Learn about career opportunities in high tech and acquire valuable, practical information that will help you choose career directions and plan for your success.
Come learn about careers in the semiconductor industry at the SEMI Professional Development Seminar hosted by UC Santa Cruz.

Listen to professionals in the industry talk about their roles and find out how to prepare for jobs in the Semiconductors Industry.
Discover semiconductor job opportunities you didn’t know existed (internship and entry-level) and how you can prepare for them through job searches, interviews, resumes, and more.
Meet with professionals and executives during our speed mentoring, mock interview, and networking sessions.

All majors are welcome! Students with a background in Engineering, Computer Science, Chemistry, Physics, Math, Data Science, and Business are strongly encouraged to attend.

Enjoy free food, free swag, and giveaways.
Students can come and go.

EVENT is FREE but registration is required. Register by Feb 20th to secure a lunch.
Event is organized by SEMI in collaboration with Career Success, Baskin Engineering and the Innovation & Business Engagement Hub.
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.
Questions? Send to csuccess@ucsc.edu or visit Career Success at Hahn 125 East Entrance
Need accessibility support? Let us know at slugtalent@ucsc.edu at least two weeks prior to the event date.

"Career Success Resources" for UTC Transfer/Continuing Students
2026-02-27 16:00 - 17:00 | CA
Come and find out ALL that Career Success has to offer to UCSC students – every resources is FREE for you! Resume/Cover Letter feedback, Career Coaching Appointments, Graduate School Preparation, Interviewing Skills, Networking Opportunities, Career Fairs, Professional Development Workshops and MORE!
All students are welcome. The presentation will last 30 minutes, followed by a 15-minute Q&A.
We will provide captions for the presentation. If you have disability-related needs, please contact the Career Success office at csuccess@ucsc.edu or (831) 459-4420 as soon as possible.

YOU BELONG HERE
Programs and services 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.

Education Innovation Workshop: Math Atlas
2026-02-28 09:00 - 15:00 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Welcome to The Math Altas!
In this one-day workshop, learning specialist Elizabeth Powell will be sharing her 35 years of experience as an educator in evidence-based strategies from The Math Atlas framework. We’ll talk about instructional effectiveness and learner engagement in one-on-one math settings.
You’ll get to evaluate the real-world implications of visual, verbal, and multi-sensory approaches to math instruction by reflecting on case examples and adapting them to the needs of individual learners and you’ll discover how The Math Atlas methods strengthen conceptual understanding and procedural fluency in mathematics.
This workshop is very welcoming to those who have struggled in math as well as those who love it!
Note: Teachers are welcome to attend, but we’ll be focusing on individualized instruction rather than classroom techniques.
Welcome to our immersive Education Innovation workshop series.
Join master educators to explore the most current, evidence-based best practices to keep your knowledge and skills up to date and to strengthen your ability to support every student. Each session is led by an expert in the field with real-world experience, who will guide you through the material and help you apply it immediately to your work. This workshop is sponsored by the UCSC Silicon Valley Extension Educational Therapy certificate program.
Enroll today.

March is Hummingbird Month at the UCSC Arboretum & Botanic Garden
2026-03-01 00:00 - 2026-03-31 23:59 | Arboretum, 122 Arboretum Road, Santa Cruz, CA, 95064
March is Hummingbird Month at the UCSC Arboretum & Botanic Garden
This time of year, the Arboretum hosts both Anna’s and Allen’s hummingbirds, the two most common species in Northern California. “The density of hummingbirds—the number per area in the Arboretum—is ridiculously high,” says Bruce Lyon, Professor Emeriti of Ecology and Evolutionary Biology at UCSC. “You can watch them feeding on ﬂowers, you can watch their courtship, you can watch them chasing different species. It’s a great opportunity to see some pretty amazing hummingbird biology.”
In celebration of this special time of year, we invite you to visit the garden as much as possible! We will have presentations, workshops, and tours throughout the month. See our webpage for a schedule of activities and more information about hummingbirds and the abundance of plants at the Arboretum that attract them.
We will also feature hummingbird merchandise and hummingbird-attracting plants at our gift shop and nursery. Visit Norrie’s Gift & Garden Shop, Tuesdays thru Sundays from 10 – 4. For more information visit: https://arboretum.ucsc.edu/garden-shop/
All events are free with paid admission: Adults: $10, Seniors $8 and Youth 4-17 $5. Current UCSC students are free. Rain cancels outdoor activities.
Current Arboretum members are always free and enjoy other great benefits year-round! Join Today at https://arboretum.ucsc.edu/get-involved/join-us/

Optional Practical Training (OPT) Awareness Week
2026-03-02 00:00 - 2026-03-06 23:59 | CA
Join us for OPT Awareness Week at UC Santa Cruz, a supportive and informative series designed to help international students learn about and confidently prepare for Optional Practical Training. You’ll learn application steps, key timelines, and employment rules while connecting with advisors and peers who are here to help you succeed. Whether you’re just starting to explore OPT or ready to apply, this week offers the tools and clarity you need to move forward with confidence.
Visit our website to explore the different workshops and sessions.

ECE 290 Seminar: Precision Nuclear Medicine: Engineering Solutions from Acquisition to Analysis
2026-03-02 10:40 - 11:45 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Spencer L. Bowen, Assistant Professor in the Departments of Radiology and Biomedical Engineering, UT Southwestern Medical Center
Description: The Bowen Lab focuses on the development of tools for positron emission tomography (PET) and hybrid systems (e.g. PET/CT), to advance precision imaging for the care and study of oncology, neurology, and cardiology patients. Quantitative metrics from PET are integral to both patient workup and clinical research. However, current approaches to enable quantitative imaging have substantial performance limitations that can compromise study conclusions, fail to generalize across exams and scanners, expose patients to additional ionizing radiation, or necessitate invasive procedures. To address these key barriers, Dr. Bowen and his team investigate advanced acquisition techniques, image reconstruction algorithms, and post-processing methods. Their studies span from digital simulations to human subjects research. This lecture will cover recent developments by the Bowen Lab, including 1) advanced PET data correction methods for low-dose and standalone exams, 2) non-invasive fully quantitative imaging, and 3) leveraging topical sensors to detect faulty radiotracer injections.
Bio: Spencer L. Bowen, Ph.D., is an Assistant Professor in the Departments of Radiology and Biomedical Engineering at UT Southwestern Medical Center. He earned his doctorate in biomedical engineering from University of California, Davis, where he developed hardware and algorithmic solutions to enable quantification with a breast PET/CT scanner. Dr. Bowen then worked as a research fellow at Massachusetts General Hospital on precision PET imaging methods for combined PET/MR. Prior to joining the UT Southwestern faculty in 2020, he served as a research assistant professor at the Fralin Biomedical Research Institute at Virginia Tech-Carilion. Dr. Bowen’s research program is funded by both industry and the NIH. His work has been featured on the cover of the Journal of Nuclear Medicine and detailed by the press.
Hosted by: Professor Soumya Bose, ECE Department
Zoom Link: https://ucsc.zoom.us/j/97975378707?pwd=[REDACTED]

CM Seminar - "From Sibelius to Game: Crafting Adaptive Music for 'Kingdom Come: Deliverance'"
2026-03-02 12:30 - 13:30 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Presented by: Adam Sporka
Description: “This talk explores the technical and creative processes behind the music of Kingdom Come: Deliverance II, where I served as a music programmer, and soundtrack contributor. Using our proprietary Sequence Music Engine and music logic module, we authentically scored the game’s 1400s Bohemia setting with segment-based adaptive music driven by in-game variables. Our workflow centers around the notation program Sibelius and our custom tool Converdi, which streamlines the production by converting the score symbols to preliminary MIDI streams per individual VSTs and by extracting the precise timing data necessary for the segment transitions. This enabled us to spend more time on the creative aspects of music and less time on production and implementation.”
The key takeaways from the talk are as follows:
* Production should start with a complete score, and not just the MIDI exports therefrom
* Custom automation tools can streamline the music creation workflow and reduce the production time
* Resequencing is more suitable for classical and medievalesque music than layering
* Rapid music prototyping allows for early testing of adaptive music directly in the game
Bio: Adam Sporka is a software developer by trade, a musician by domain, and a scientist by approach. As a researcher, developer, and educator in game audio, he places a special focus on interactive music. He has served as the technical music director for Kingdom Come: Deliverance II (Warhorse Studios) and is the author of the Sequence Music Engine, a proprietary adaptive music middleware used in both games. As a composer, he contributed to the soundtrack of both Kingdom Come: Deliverance games, writing some of the most memorable medievalesque and early renaissance pieces on the soundtrack. Currently, he teaches game audio at the University of California, Santa Cruz. He received his Ph.D. and habilitation in human-computer interaction from the Czech Technical University (Czech Republic) and was a postdoctoral researcher at the University of Trento (Italy). He has published more than 50 articles in the proceedings of international conferences and academic journals. Adam is currently appointed as a principal engineer at Embody, a Sunnyvale-based game audio software company focused on the commercial applications of the head-related transfer function.
Hosted by: Professor Christina Chung
When: March 2, 2026 from 12:30PM to 1:30PM
Location:
IN-PERSON @ SVC 3212.
Viewing room @ UCSC Main Campus, E2-280.
LUNCH WILL BE PROVIDED AT BOTH LOCATIONS! Faculty and students are highly encouraged to attend.
Zoom info:
https://ucsc.zoom.us/j/98763397019?pwd=[REDACTED]
Meeting ID: 987 6339 7019
Passcode: [REDACTED]

Statistics Seminar: Decoding Phytoplankton Responses to a Changing Ocean
2026-03-02 16:00 - 17:00 | CA
Presenter: Francois Ribalet, Research Associate Professor, School of Oceanography, University of Washington
Description: François Ribalet will present new observational technologies and computational approaches for studying phytoplankton responses to ocean warming. Using SeaFlow, a custom-built automated flow cytometer deployed on over 100 research cruises, his team has collected nearly 850 billion cell measurements across global oceans. Matrix population models applied to these data reveal how temperature affects phytoplankton division rates and biomass. The research shows that Prochlorococcus, the ocean’s most abundant photosynthetic organism, experiences sharp declines in growth above 28°C. Climate projections incorporating these metabolic constraints predict a 40-60% decrease in Prochlorococcus production in tropical regions by 2100, with Synechococcus partially compensating through a 20-40% increase. These shifts between dominant phytoplankton groups will likely disrupt ocean food webs and carbon cycling, raising questions about whether tropical ecosystems can adapt to warming oceans.

Bio: François Ribalet is a research associate professor at the University of Washington studying phytoplankton and their role in ocean food webs and carbon cycling. He combines field observations with statistical models to understand how environmental changes affect the growth and community dynamics of these microscopic organisms.
Hosted by: Statistics Department

AM Seminar: The Evolving Landscape of AI for Science and Engineering: Bridging Simulation, Experiment, and Multi-scale Dynamics
2026-03-02 16:00 - 17:00 | CA
Presenter: Aditi Krishnapriyan, Assistant Professor, UC Berkeley
Description: Recent advances in large-scale scientific datasets are creating new opportunities for machine learning (ML) methods to more effectively capture scientific phenomena with greater accuracy and reach. In this talk, I will discuss how these advances are both shifting ML design paradigms and enabling new scientific inquiries. This includes investigations into understanding if neural networks can autonomously discover fundamental physical relationships from data, and demonstrating how more flexible machine learning modeling design choices enable capturing physical dynamics across multiple scales. I will also explore how generative modeling approaches rooted in statistical physics can be applied to accelerate the sampling of dynamic pathways, and as a framework to align and bridge the gap between simulated data and experimental observations.
Bio: Aditi Krishnapriyan is an Assistant Professor at UC Berkeley where she is part of Chemical and Biomolecular Engineering, Electrical Engineering and Computer Sciences, and Berkeley AI Research; as well as a faculty scientist in the Applied Mathematics division at Lawrence Berkeley National Laboratory. She holds a PhD from Stanford University, supported by the DOE Computational Science Graduate Fellowship, was the Luis W. Alvarez Fellow in Computing Sciences at Lawrence Berkeley National Laboratory, and is a recipient of the Department of Energy Early Career Award and RCSA Scialog. Her research focuses on developing physics-inspired machine learning methods that bridge machine learning with physical science applications to capture phenomena across multiple length and timescales.
Hosted by: Applied Mathematics

CSE Colloquium - Improving Efficiency and Reliability of Foundation Models in Clinical AI
2026-03-04 11:00 - 12:15 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Vasiliki “Vicky” Bikia, PhD, Stanford Department of Biomedical Data Science and Institute for Human-Centered AI (HAI)
Abstract:
Deploying 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.
Bio:
Vasiliki 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.
Hosted by: Professor Nikos Tziavelis
Location: Engineering 2, E2-180 (*Refreshments such as coffee, tea, fresh fruit, and pastries will be provided)
Zoom: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

Shields, S. (CM) - Procedural, Player-Centric Game Balancing
2026-03-04 13:00 - 17:00 | Merrill College, College Office, Santa Cruz, CA, 95064
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.
This 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.
Balance 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.
Event Host: Sam Shields, Ph.D. Candidate, Computational Media
Advisor: Edward F. Melcer
Zoom- https://ucsc.zoom.us/j/98956788669?pwd=[REDACTED]
Passcode- 713173

Sonic Icons: Relation, Recognition, and Revival in a Syriac World
2026-03-04 15:30 - 17:00 | Social Sciences 1, Social Sciences 1, Santa Cruz, CA, 95064
Talk Abstract: To the extent that Middle Eastern Christians register in Euro-American political imaginaries at all, they are usually invoked to make a political point about the need for Western military intervention in places like Iraq or Syria, or they are cited as an exemption to anti-Islamic immigration policies because of an assumption that their Christianity makes them easily assimilable in the so-called “Judeo-Christian” West. Sonic Icons argues that these views work against the very communities they are meant to benefit by tracking a diasporic network of Syriac Orthodox Christians—also known as Assyrians, Aramaeans, and Syriacs—in the Netherlands who intertwine religious practice with political activism to “save” Syriac Christianity from the twin threats of political violence in the Middle East and cultural assimilation in Europe.
Coming of age in a historical moment when much of their tradition has been destroyed or forgotten by war, dispossession, displacement, and genocide—their story of self-discovery is a story of survival, revival, and reinvention. Their activism is oriented toward seeking a complex form of recognition for what they understand to be the ethical core of Christian kinship in an ethnic as well as in a religious sense, despite living in societies that do not recognize this unhyphenated form of ethnoreligious identity as a politically legitimate mode of public identity. Drawing on both theological and linguistic theories of the icon, Sonic Icons rethinks foundational theoretical accounts of ethnicization, racialization, and secularization by examining how kinship gets made, claimed, and named in the global politics of minority recognition.

Speaker Bio: A cultural anthropologist by training (UCSC ’13), Dr. Sarah Bakker Kellogg is a sensory ethnographer whose research and writing documents minor traditions of knowledge, care, and relational world-making. Known for her methodological creativity, her work centers questions of reproduction, ethics, and justice at the intersection of religious studies, gender studies, migration studies, and political economy. The author of Sonic Icons: Relation, Recognition, and Revival in a Syriac World published by Fordham University Press (2025), she has also won numerous awards, research grants, and fellowships, including the SSRC’s International Dissertation Research Fellowship, the Charlotte W. Newcombe Doctoral Dissertation Fellowship, and the Wenner Gren Hunt Postdoctoral Fellowship. Her new research project is an ethnographic and historical investigation of interfaith activist traditions organized around immigration, racial reparations, and economic justice.

The UC Santa Cruz Kraw Lecture Series presents: Unmasking cancer's complete genetic code
2026-03-04 17:30 - 19:30 | 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 | CA
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 | CA
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 | CA
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
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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.
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