AM Seminar: Flexible Filaments and Swimming Cups: Just Go with the Flow
2026-03-30 16:00 - 17:00 (America/Los_Angeles)
Presenter: Lisa Fauci, Professor, Tulane University
Description: The motion of waving or rotating filaments in a fluid environment is a common element in many biological and engineered systems. Examples at the microscale include chains of diatoms moving in the ocean, flagella of individual cells comprising multicellular colonies, as well as engineered nanorobots designed to deliver drugs to tumors. In this talk we will present mathematical and computational insights into these flows at the microscale. Our modeling approaches will vary from detailed models that capture flagellar material properties and wave geometry to minimal force-dipole models that represent a flagellum by a single point. We will investigate a few intriguing systems, including the journey of extremely long insect sperm flagella through tortuous female reproductive tracts, and the hydrodynamic performance of shape-shifting Choanoeca flexa colonies.
Bio: Lisa Fauci received her PhD from the Courant Institute of Mathematical Sciences at New York University, and directly after that joined the Department of Mathematics at Tulane University in New Orleans, Louisiana, USA. Her research focuses on biological fluid dynamics, with an emphasis on using modeling and simulation to study the basic biophysics of organismal locomotion and reproductive mechanics. Lisa served as president of the Society for Industrial and Applied Mathematics (SIAM) in 2019-2020. She is a fellow of SIAM, the American Mathematical Society, the Association for Women in Mathematics, and the American Physical Society. In 2023, she was elected to the US National Academy of Sciences.
Hosted by: Applied Mathematics Department

FINS: Fisheries Insights Narratives and Stories seminar series featuring Dr. Randi Solhjell
2026-04-01 10:30 - 12:00 | Ocean Health Building, McAllister Way, Santa Cruz, CA, 95064
Please join us for the first talk in the FINS: Fisheries Insights Narratives and Stories seminar series featuring Fulbright Fellow Dr. Randi Solhjell. Her talk, “Managing a Fishy Business: Norway’s foreign aid and its impact on global fisheries governance” will discuss how Norwegian actors shape global fisheries policy through diplomacy, regulatory innovation and engagement in international institutions such as the UN FAO. She will also discuss how she studies fisheries from a social/political science lens.
FINS: Fisheries Insights Narratives and Stories Seminar Series
Dr. Randi Solhjell, Fulbright fellow, University of Oslo
Title: Managing a Fishy Business: Norway’s foreign aid and its impact on global fisheries governance
When: Wednesday, April 1st from 11am-12pm
Where: Ocean Health Building Rm 118, 115 McAllister Way, Santa Cruz, CA 95060 and on Zoom
Agenda:

10:30 am – 11:00 am – Professional Networking Session (in person only – light snacks and refreshments provided)
11 am to 12 pm – presentation followed by Q & A
12 pm – 1pm – student lunch with the speaker in OHB courtyard → sign up here

Zoom Meeting Registration: https://ucsc.zoom.us/meeting/register/NwH0_qUbSeuIm3A76DY-Dg
Randi Solhjell is a postdoctoral researcher at the Department of Criminology and Sociology of Law, Faculty of Law, The University of Oslo, and visiting Fulbright scholar at the Science and Justice Research Center, the University of California, Santa Cruz. She holds a PhD in Political Science from the London School of Economics.

CSE Colloquium - Messages from across the event horizon: AI Agentic Design for Computer Architecture (and more generalizable learnings)
2026-04-01 11:00 - 12:15 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Christopher Fletcher, UC Berkeley
Abstract:
It is difficult to escape the hype of agentic coding. Is the hype real? Are we still living in ~Summer 2025 — when AI coding would accomplish little more than upset its human supervisor? Or has a level shift in technology finally arrived?
In this talk I will argue the latter. I will describe a self-imposed experiment to discover modern AI coding tools’ capabilities (starting mid February 2026). I will try (my best) to communicate my utter and sheer surprise at where the state of the art actually is. Then I will do a deep dive and try to relay everything I have learned about this new engineering discipline—based on my attempts to push the technology as hard as I can for the past 1.5 months. I will conclude by pontificating about the future of computer architecture and academic research more generally.
Bio:
Christopher Fletcher is an Associate Professor of EECS at UC Berkeley. He is a computer architect whose research spans architecture, security, and domain-specific acceleration, especially at their intersections from cryptography and hardware attacks to algorithm-to-hardware co-design. His work has received 31 paper recognitions and several other honors, including the NSF CAREER Award, Intel and Google faculty awards, UIUC research and promotion awards, election to DARPA ISAT, and MIT’s George M. Sprowls Award, with related work also recognized by Scientific American as one of ten “World Changing Ideas.”
Hosted by: Professor Alvaro Cardenas
Location: Engineering 2, Room E2-180 (Refreshments such as fruit, pastries, coffee, and tea will be provided.)
Zoom Option: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

BME 280B Seminar: Small changes, Big consequences: Modulators of Alphavirus Assembly
2026-04-02 11:40 - 13:15 | Biomedical Sciences Building, 575 McLaughlin Drive
Presenter: Dr. Suchetana (Tuli) Mukhopadhyay, Professor, Indiana University
Description: N/A
Bio: Suchetana “Tuli” Mukhopadhyay, Ph.D., is a professor in the Department of Biology at Indiana University, Bloomington. She received her B.A. in chemistry from DePauw University and her Ph.D. in chemistry from the University of Illinois at Chicago. Following her doctoral studies, Mukhopadhyay conducted postdoctoral research at the University of Texas Southwestern Medical Center, focusing on G-protein mediated signaling. She continued her postdoctoral work at Purdue University in structural virology, where she developed a strong interest in arboviruses. Mukhopadhyay joined Indiana University in 2005, where she established her research program on the assembly and spread of alphaviruses.
Hosted by: Professor Rebecca Dubois, BME Department

BME 80G Seminar: To Infinity and Beyond? Ethical, legal, and social issues of human research in space”
2026-04-03 13:20 - 14:25 | Jack Baskin Auditorium, 191 Baskin Cir, Santa Cruz, CA, 95064
Presenter: Vaso Rahimzadeh, Assistant Professor, Baylor College of Medicine
Description: As humans venture farther into outer space, new scientific discovery awaits including in genomics; but so do new ethical dilemmas. Who bears the risks (and rewards) of space exploration and how should humanity ethically expand beyond our planet? This session will have students think critically about the ethical, legal, and social issues of human genomic research in space and offer frameworks for analyzing them. Students will learn about the contemporary challenges and opportunities of genomic research for the upcoming lunar missions, and in anticipation of future Mars exploration.
Bio: I am Assistant Professor at the Center for Medical Ethics and Health Policy at Baylor College of Medicine. In my National Institutes of Health-funded research, I investigate the ethical, legal, and social issues of health data sharing on earth and in space. I aim to inform policy and practice in ways that maximize the scientific value of data while respecting the rights and interests of individuals and communities. I director the METEORS program (Mission to Enhance eThics Education, Outreach, and Research in Space) and serve on the Bioethics Advisory Panel for the National Aeronautics and Space Administration (NASA). I am a proud UC alum, earning my BS in Microbial Biology from UC Berkeley in 2012, and hold a PhD from McGill University with a specialization in biomedical ethics. You can read more about my background and read my work here.
Hosted by: Professor Karen Miga, BME Department

AM Seminar: The Thinking Eye: AI That Sees, Reads, and Reasons in Medicine
2026-04-06 16:00 - 17:00
Presenter: Yuyin Zhou, Assistant Professor, UCSC
Description: Medical AI is undergoing a profound transformation, evolving from simple pattern recognition to systems capable of complex clinical reasoning. This talk will chart this evolution across three dimensions: data, models, and evaluation. I will first highlight the shift from limited, unimodal datasets to massive multimodal resources. In particular, I will introduce MedTrinity-25M—a novel collection of over 25 million richly annotated medical images that serves as a foundation for multimodal tasks such as visual question answering and report generation. Building on this, I will describe how grounding decision processes in a structured medical knowledge graph enables the generation of high-fidelity reasoning chains. Using these chains, we construct a large-scale medical reasoning dataset, which in turn allows us to develop a new class of reasoning models. These models not only achieve state-of-the-art performance on multiple clinical Q&A benchmarks but also produce reasoning outputs that physicians across seven specialties have independently verified as clinically reliable, interpretable, and more factually accurate than existing large language models. Finally, the talk will offer a deep dive into the critical evaluation of these advanced models, moving beyond standard benchmarks to expose their current limitations—particularly in interpreting dynamic clinical scenarios such as tracking disease progression from temporal image sequences. To foster a holistic understanding of the mechanisms underlying these reasoning models, I will introduce a new evaluation framework that examines performance from two complementary perspectives: their grasp of static knowledge versus their capacity for dynamic reasoning. Together, these advances point toward a future where AI systems can holistically analyze patient information and function as true collaborative partners in complex medical decision-making.
Bio: Yuyin Zhou is an Assistant Professor of Computer Science and Engineering at UC Santa Cruz. Her research interests lie at the intersection of machine learning and computer vision, with a primary focus on AI for healthcare and scientific discovery. Her work (70+ peered-reviewed publications with18,000+ citations) has been recognized with honors including 2025 Google Research Scholar Award, Best Paper Award at KDD 2025 Health Day and at Computerized Medical Imaging and Graphics 2024, 2023 Hellman Fellowship, Best Paper Honorable Mention at DART 2022, and finalist recognition for the MICCAI Young Scientist Publication Impact Award in 2022. Beyond her research, Yuyin has organized over 20 workshops and tutorials at major conferences including ICML, MICCAI, ML4H, ICCV, CVPR, and ECCV, with coverage in media outlets such as ICCV Daily and Computer Vision News. She serves as a regular Area Chair for CVPR, ICLR, MICCAI, CHIL, and ISBI, an associate editor for SPIE medical imaging, Image and Vision Computing, and was the Doctoral Consortium Chair for WACV 2025.
Hosted by: Applied Mathematics Department

Statistics Seminar: Some Recent Results on Transfer Learning
2026-04-06 16:00 - 17:00
Presenter: Oscar Hernan Madrid Padilla, Assistant Professor, University of California, Los Angeles
Description: In the first part of the talk, I will introduce TRansfer leArning via guideD horseshoE prioR (TRADER), a novel approach enabling multi-source transfer through pre-trained models in high-dimensional linear regression. TRADER shrinks target parameters towards a weighted average of source estimates, accommodating sources with different scales. Theoretical investigation shows that TRADER achieves faster posterior contraction rates than standard continuous shrinkage priors when sources align well with the target while preventing negative transfer from heterogeneous sources. Extensive numerical studies and a real-data application demonstrate that TRADER improves estimation and inference accuracy over state-of-the-art transfer learning methods. In the second part of the talk, I will discuss some ongoing work involving transfer learning in nonparametric regression with ReLU networks
Bio: Oscar Madrid Padilla is a tenure-track Assistant Professor in the Department of Statistics at the University of California, Los Angeles. Previously, from July 2017 to June 2019, he was a Neyman Visiting Assistant Professor in the Department of Statistics at the University of California, Berkeley. Before that, he earned his Ph.D. in Statistics from The University of Texas at Austin in May 2017 under the supervision of Professor James Scott. He completed his undergraduate degree, a B.S. in Mathematics, at CIMAT in Mexico in April 2013.
Hosted by: Statistics Department

BME 80G Seminar: Sara Ackerman - Doing Ethics From The Inside: Collaboration, Critique, and Contradiction in Team Science
2026-04-10 13:20 - 14:25 | Jack Baskin Auditorium, 191 Baskin Cir, Santa Cruz, CA, 95064
Presenter: Sara Ackerman, Medical Anthropologist and Professor of Social and Behavioral Sciences, University of California, San Francisco
Description: Team science has been widely promoted as a collaborative, cross-disciplinary approach to addressing key scientific questions, yet power differences and epistemic hierarchies persist. This talk explores "embedded ethics"—a model in which social scientists and ethicists work directly with scientific research teams. Drawing on findings from an empirical ethics project embedded in a multi-year clinical genomics study, I demonstrate how qualitative methods and participatory design can shift the researcher-participant dynamic toward greater reciprocity and attention to enrolled families’ experiences. At the same time, ethicists and social scientists can find themselves in an uncomfortable and even paradoxical position, expected to facilitate project goals—such as recruitment of historically underrepresented groups—while simultaneously critically assessing the very categories of difference and measures being used. In the future, team science collaborations can result in more just and broadly beneficial science if social science, humanities and community partners are able to meaningfully contribute to the research agenda itself.
Bio: Sara Ackerman, PhD, MPH, is a medical anthropologist and Professor of Social and Behavioral Sciences at the University of California, San Francisco. Her research draws on ethnographic methods and public engagement to examine how genomics, artificial intelligence and other emerging medical technologies affect the lives of patients and caregivers and shape conceptions of health, illness and the public good. Sara teaches courses on community-engaged research, qualitative methods, and research ethics at UCSF. As Director of the Bioethics and Regulatory Support Program for UCSF’s Clinical and Translational Science Institute, she is working to increase patient and public participation in decisions about the use of AI in clinical care and the sharing of patients’ clinical data for research.
Hosted by: Professor Karen Miga, BME Department

2026 Right Livelihood International Conference
2026-04-13 08:00 - 2026-05-15 17:00
The Right Livelihood International Conference is a five-week global conference exploring how education can strengthen democracy, collective intelligence, and just futures. Bringing together Right Livelihood Laureates, students, faculty, and community partners across continents, the conference combines asynchronous learning with participatory dialogue and collaborative action. Rather than advocating specific outcomes, the conference positions education as a democratic practice and the Right Livelihood College as a steward of dialogue, student voice, and long-term institutional learning.
Registration is free and open to the public. Sign up to receive conference updates, session links, and participation opportunities.

ECE 290 Seminar: Speaker Dr. Josh Star-Lack - Photon Counting Detectors for X-Ray Computed Tomography
2026-04-13 10:40 - 11:45 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Dr. Josh Star-Lack, Principal Scientist and Research Manager, Varex Imaging Inc
Description: X-ray computed tomography (CT) provides rapid, detailed 3D imaging of internal organs, bones, and vasculature. By enabling the swift diagnosis of cancer, cardiac disease, neurological disorders, and other pathologies, CT has revolutionized medicine—reducing the need for invasive exploratory surgeries and facilitating precise treatment planning. Despite the technology’s maturity, the clinical demand for higher spatial resolution, increased sensitivity, and lower ionizing radiation doses remains high. This presentation reviews the fundamental principles of CT, traces its evolution since its invention 50 years ago, and describes a new technology, photon-counting x-ray detection, as a transformative solution to current clinical challenges.
Bio: Josh Star-Lack received his B.S. in Applied and Engineering Physics from Cornell University and Ph.D. in Electrical Engineering from U.C. Berkeley. He has worked on the development of medical imaging technologies, including X-ray computed tomography and magnetic resonance imaging, for his entire professional career. He is currently a Principal Scientist and Research Manager at Varex Imaging Inc, the world’s largest manufacturer of X-ray detectors and tubes. He has co-authored over 150 publications and holds over 50 patents.
Hosted by: Professor Soumya Bose, ECE Department
Zoom Link: https://ucsc.zoom.us/j/97975378707?pwd=[REDACTED]

BME 80G Seminar: Speaker Dr. Mohammed Mostajo-Radji - How Close Are We to Consciousness in a Dish?
2026-04-13 13:20 - 14:25 | Jack Baskin Auditorium, 191 Baskin Cir, Santa Cruz, CA, 95064
Presenter: Dr. Mohammed Mostajo-Radji, Associate Research Scientist, Genomics Institute UCSC
Description: We can now grow small pieces of human brain tissue in the lab, known as brain organoids. These models show many features of early brain development, including different types of neurons and coordinated electrical activity. This progress raises a fascinating question: how close are we to consciousness in a dish? In this talk, I will explain what brain organoids are, what we mean by consciousness, and why answering this question is more complex than it may seem. I will end by introducing the idea of neurorights, and why thinking about the rights and protections of minds matters as brain science and technology continue to advance.
Bio: Dr. Mohammed Mostajo-Radji is an Associate Research Scientist at the Genomics Institute UCSC. He is part of the Braingeneers group, a multidisciplinary collective of geneticists, neuroscientists, and engineers focused on the human brain’s specification and function. His research explores neuronal specification and fate plasticity in the cerebral cortex using brain organoid models. Additionally, he leads the Live Cell Biotechnology Discovery Lab, which develops cloud-based experimental science education technologies. Dr. Mostajo-Radji earned his PhD in Molecular and Cellular Biology from Harvard University and completed postdoctoral training at UCSF. He also co-leads the Data Coordination Center for the SSPsyGene Consortium, an NIH initiative to characterize genetic mutations associated with neuropsychiatric disorders.
Hosted by: Professor Karen Miga, BME Department

Statistics Seminar: Calibration Weighting-Style Diagnostics for Nonlinear Bayesian Hierarchical Models
2026-04-13 16:00 - 17:00
Presenter: Dr. Ryan Giordano, UC Berkeley Statistics
Description: Multilevel Regression with Post-stratification (MrP) has become a workhorse method for estimating population quantities using non-probability surveys, and is the primary alternative to traditional survey calibration weights, e.g.~ as computed by raking. For simple linear regression models, MrP methods admit “equivalent weights”, allowing for direct comparisons between MrP and traditional calibration weights (Gelman 2006). In the present work, we develop a more general framework for computing and interpreting “MrP local equivalent weights” (MrPlew), which admit direct comparison with calibration weights in terms of important diagnostic quantities such as covariate balance, frequentist sampling variability, and partial pooling. MrPlew is based on a local approximation, which we show in theory and practice to be accurate and meaningful for the target diagnostics. Importantly, MrPlew can be easily computed based on existing MCMC samples and conveniently wraps standard MrP software implementations.
Bio: Dr. Ryan Giordano is currently an assistant professor of statistics at UC Berkeley. Dr. Ryan Giordano earned a PhD in Statistics from UC Berkeley advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc with distinction in econometrics and mathematical economics from the London School of Economics, and undergraduate degrees in mathematics and engineering mechanics from the University of Illinois in Urbana-Champaign. Dr. Ryan Giordano has worked as a postdoctoral researcher at MIT under Tamara Broderick, as an engineer for Google and HP, and served for two years as an education volunteer in the US Peace Corps in Kazakhstan. Dr. Ryan Giordano’s research interests include machine learning, variational inference, Bayesian methods, robustness quantification, and what it even means to do statistics at all.
Hosted by: Statistics Department

AM Seminar: Genetically Admixed Groups as a Laboratory for Mathematical Modeling and Discovery
2026-04-13 16:00 - 17:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
Presenter: Dr. Aw, Department of Genetics, University of Pennsylvania
Description: Admixed groups consist of individuals descended from two or more historically separated ancestral populations, and they are underrepresented in biomedical studies. Admixed individuals are unique in that they carry mosaics of ancestral segments within their genome, so their genetic information is typically summarized as a pair of genotype and local ancestry data matrices. I will present two research projects on admixed groups: one applying statistical models to study genetic architecture and polygenic risk, and another using biomedical data analysis to motivate combinatorial and probabilistic questions. In the “math to genetics” direction, we describe structural causal models that show local and global genetic ancestry are instruments for epistasis. These models of genetic architecture imply that certain polygenic scores can differentiate between cis and trans epistases, and highly similar cross-ancestry genetic effects do not rule out pervasive gene-gene or gene-environment interactions. In the “genetics to math” direction, we study the enumeration of genotype and local ancestry data matrices — which we call admixed arrays — subject to constraints that arise naturally in biomedical applications. Using saddle-point approximation and complex martingale techniques, we show that admixed arrays admit a different independence heuristic than the closely related binary contingency tables (e^(–1/4) vs e^(–1/2) correction factor). If time permits, we will discuss ongoing work on designing algorithms for performing exact and approximate enumeration.
About the speaker: Alan Aw is a mathematical scientist specializing in human statistical and population genomics. He is currently a postdoctoral researcher with the Department of Genetics at the University of Pennsylvania, having obtained a PhD in Statistics at UC Berkeley and studied Applied Mathematics as an undergraduate. His research centers on the genetics and mathematical modeling of underrepresented groups. This includes statistical modeling and analyses of Biobank-scale admixed cohorts to better understand the genetic architecture of biomedical traits and improve genetic risk prediction, developing non-parametric hypothesis testing methods for genomics, and interdisciplinary approaches to studying European demographic history inclusive of indigenous Siberians. He is a member of the PRIMED Consortium and a trainee under a National Institutes of Health T32 Grant in Genomic Medicine.
Hosted By: Applied Mathematics

Nursing School Info Session with UPENN and Johns Hopkins
2026-04-14 12:00 - 13:00 | Humanities 1 Building, 257 Cowell-Stevenson Road, Santa Cruz, CA, 95064, United States
University of Pennsylvania and Johns Hopkins Schools of Nursing each offer accelerated nursing programs for individuals looking to transition to the field from non-nursing degrees.
We will cover program overview, admissions and career outcomes.
Humanities 1 – Room 202
Register now!

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.

Prepare for the Fair with COOP Careers
2026-04-14 16:00 - 17:00 | Career Success Student Lounge (125 Hahn), 1156 High Street, Santa Cruz, CA, 95064, United States
Join us for an in-person workshop with COOP Careers about elevating your professional brand – just in time to prepare for the Spring Career & Internship Fair.
This session will help students craft a compelling professional pitch, tailor their resume to stand out to employers, and network meaningfully with industry professionals. Don’t miss this chance to get fair-ready and set yourself up for success!
Register now!
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.

FINS: Fisheries Insights Narratives and Stories seminar series featuring Dr. George Leonard
2026-04-15 10:30 - 12:00 | Ocean Health Building, McAllister Way, Santa Cruz, CA, 95064
Please join us for the second talk in the FINS: Fisheries Insights Narratives and Stories seminar series featuring Adjunct Professor Dr. George Leonard. His talk, “Lessons learned from my time at the science-policy interface” will discuss his 35 years of experience researching, communicating, and advocating for a vibrant and healthy ocean upon which all of us depend. He has been at the forefront of ocean science-policy interface at major nonprofits (Monterey Bay Aquarium and Ocean Conservancy), working on practical ocean solutions to some of the ocean’s greatest environmental challenges. He initiated, developed, and led a host of conservation programs during his time at Ocean Conservancy including offshore aquaculture, plastics pollution, ocean acidification, climate change, mesopelagic fisheries, and deep-sea mining. During his early career at Monterey Bay Aquarium, he developed the scientific foundation for the nascent sustainable seafood movement
FINS: Fisheries Insights Narratives and Stories Seminar Series
Dr. George Leonard, Adjunct Professor, Coastal Science and Policy UCSC
Title: Lessons learned from my time at the science-policy interface
When: Wednesday, April 15th from 10:30am-12pm
Where: Ocean Health Building Rm 118, 115 McAllister Way, Santa Cruz, CA 95060 and on Zoom
Agenda:

10:30 am – 11:00 am – Professional Networking Session (in person only – light snacks and refreshments provided)
11 am to 12 pm – presentation followed by Q & A
12 pm – 1pm – student lunch with the speaker in OHB courtyard → sign up here

Zoom Meeting Registration: https://ucsc.zoom.us/meeting/register/NwH0_qUbSeuIm3A76DY-Dg

CSE Colloquium - Algorithmic Problems in Discrete Choice by Ravi Kumar
2026-04-15 11:00 - 12:15 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Ravi Kumar, Google
Abstract:
In discrete choice, a user selects one option from a finite set of available alternatives, a process that is crucial for recommendation systems applications in e-commerce, social media, search engines, etc. A popular way to model discrete choice is through Random Utility Models (RUMs). RUMs assume that users assign values to options and choose the one with the highest value from among the available alternatives. RUMs have become increasingly important in the Web era; they offer an elegant mathematical framework for researchers to model user choices and predict user behavior based on (possibly limited) observations. While RUMs have been extensively studied in behavioral economics and social sciences, many basic algorithmic tasks remain poorly understood. In this talk, we will discuss various algorithmic and learning questions concerning RUMs.
Bio:
Ravi Kumar has been a research scientist at Google since 2012. Prior to this, he was at the IBM Almaden Research Center and at Yahoo! Research. His interests include algorithms for massive data, ML/privacy, and the theory of computation. He maintains an extensive publication record that includes Test-of-Time Awards from STOC and WWW, as well as Best Paper Awards from KDD and WWW, to mention a few. He is an ACM fellow.

Hosted by: Professor Sungjin Im

Date and Time: Wednesday, April 15, 2026 from 11:00 am – 12: 15 pm
Location: Engineering 2, Room E2-180 (Refreshments such as fruit, pastries, coffee, and tea will be provided.)

Zoom Option: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

AM Seminar: Variational Inference and Density Estimation with Non-Negative Tensor Train
2026-04-20 16:00 - 17:00
Presenter: Dr. Xun Tang, Stanford University
Description: This talk covers an efficient numerical approach for compressing a high-dimensional discrete distribution function into a non-negative tensor train (NTT) format. The two settings we consider are variational inference and density estimation, whereby one has access to either the unnormalized analytic formula of the distribution or the samples generated from the distribution. In particular, the compression is done through a two-stage approach. In the first stage, we use existing subroutines to encode the distribution function in a tensor train format. In the second stage, we use an NTT ansatz to fit the obtained tensor train. For the NTT fitting procedure, we use a log barrier term to ensure the positivity of each tensor component, and then utilize a second-order alternating minimization scheme to accelerate convergence. In practice, we observe that the proposed NTT fitting procedure exhibits drastically faster convergence than an alternative multiplicative update method that has been previously proposed. Through challenging numerical experiments, we show that our approach can accurately compress target distribution functions.
Bio: Xun Tang is a postdoc in Stanford University, department of mathematics, hosted by Prof. Lexing Ying. Xun works on tensor network methods for scientific computing and data science, and Xun also works on optimal transport algorithms. Xun will join HKUST department of mathematics in August 2026 as an incoming assistant professor.
Hosted by: Applied Mathematics Department

Statistics Seminar: Hierarchical Clustering with Confidence
2026-04-20 16:00 - 17:00
Presenter: Snigdha Panigrahi, Associate Professor, Department of Statistics, University of Michigan
Description:Agglomerative hierarchical clustering is one of the most widely used approaches for exploring how observations in a dataset relate to each other. However, its greedy nature makes it highly sensitive to small perturbations in the data, often producing different clustering results and making it difficult to separate genuine structure from spurious patterns. In this talk, I will show how randomizing hierarchical clustering can be useful not just for measuring stability but also for designing valid hypothesis testing procedures based on the clustering results. We propose a simple randomization scheme to construct valid p-values at each node of a hierarchical clustering dendrogram, quantifying evidence against greedy merges while controlling the Type I error rate. Our method applies to any linkage without case-specific derivations, is substantially more powerful than existing selective inference approaches, and provides an estimate of the number of clusters with a probabilistic guarantee on overestimation.
Bio:Snigdha Panigrahi is an Associate Professor of Statistics at the University of Michigan, where she also holds a courtesy appointment in the Department of Biostatistics. She received her PhD in Statistics from Stanford University in 2018 and has been a faculty member at Michigan since then. Her research focuses on converting purely predictive machine learning algorithms into principled inferential methods. She is an elected member of the International Statistical Institute, and her work has been recognized with an NSF CAREER Award and the Bernoulli New Researcher’s Award. Her editorial service, past and present, includes Journal of Computational and Graphical Statistics, Bernoulli, and Journal of the Royal Statistical Society: Series B.
Hosted by: Statistics Department

CSE Colloquium - Robust Machine Learning for Biomedical Data: Efficiency, Reliability, and Generalizability
2026-04-22 11:00 - 12:15 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter
Chenyu You, Stony Brook University
Abstract
In the rapidly growing area of machine learning, there is profound promise in crafting intelligent, data-driven methods for diverse real-world applications. Yet, in safety-critical domains like healthcare, some fundamental challenges remain: (1) The insufficiency of raw biomedical data emphasizes the need for data-efficient and robust learning approaches. (2) The imperative of safety and stability necessitates a cohesive framework that unifies learning with theoretical guarantees. (3) The inherent heterogeneity and distribution shifts in real-world clinical data call for robust and generalizable learning methods. To address these challenges, there are several major directions I have explored: (i) (Robust) Machine Learning for Imperfect Medical Data: The development of machine learning models, particularly in the context of label scarcity, increasingly necessitates the collection of substantial annotated medical data. Moreover, medical data often display a long-tailed class distribution, which consequently results in notable imbalance issues. To this end, there are several growing interests in training machine learning models jointly across imbalanced class distributions and limited annotations. I have developed novel, efficient, statistically consistent algorithms to improve empirical performance for biomedical image analysis. (ii) Learning with Theoretical Guarantees: As machine learning methods have become ubiquitous in clinical decision-making, their reliability and interpretability have become important. This is particularly crucial in the field of biomedical image analysis, where decision outcomes can have profound implications. I have developed novel machine learning algorithms that enable provably accurate anatomical modeling with theoretical guarantees. (iii) Generalize across Diverse Biomedical Data: The development of medical foundation models often requires massive and diverse biomedical data. To this end, I have developed various foundation models for biomedical imaging data and explored novel applications of these models. I have also developed novel medical AI Agents that lead to the scalable and accurate predictive modeling, particularly for distribution shift problems.
Speaker Bio
Chenyu You is an Assistant Professor in the Department of Applied Mathematics & Statistics and Department of Computer Science at Stony Brook University. He is also the core faculty member of the CVLab, AI institute, and affiliated with the Institute for Advanced Computational Science. His research focuses on both fundamental and applied problems in computer vision and machine learning, often with a focus on generalization, and making machine learning more reliable. Our applied research includes applications to healthcare, biomedical imaging, and cognitive neuroscience. He received his Ph.D. in 2024 from Yale University under the advisement of James S. Duncan, his M.S. in 2019 from Stanford University under the advisement of Daniel Rubin, and his B.S. in 2017 from Rensselaer Polytechnic Institute under the advisement of Ge Wang, all in electrical engineering. He has also spent wonderful time at Facebook AI Research (FAIR), as well as Google Research. He serves on the Medical Image Computing and Computer-Assisted Intervention Society (MICCAI), and the SUNY AI Symposium Planning Committee, and as associate editors for IEEE Transactions on Medical Imaging, Medical Image Analysis, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, and Transactions on Machine Learning Research. He has received AAAI’26 New Faculty Highlights, CPAL’26 Rising Stars Award, Tinker Research Grant Award, Lambda Research Grant Award, ICML’25 Oral Presentation Award, EMBC’25 Top Paper Award, MICCAI’25 NIH Registration Grant Award, IEEE TMI’25 Distinguished Associate Editor Certificate of Excellence Award, and Yale George P. O’Leary Graduate Fellowship, and has been ranked as the World’s Top 2% most-cited scientists by Stanford University since 2024, is a member of the Sigma Xi scientific research society, and received the Excellence in Teaching Award for Spring and Fall 2025. For more information, please check his website: https://chenyuyou.me/.
Hosted by: Professor Yuyin Zhou
Location: Engineering 2, Room E2-180 (Refreshments such as fruit, pastries, coffee, and tea will be provided.)
Zoom Option: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

CM Seminar: Edward Wang, "Inventing a New Blood Pressure Monitor"
2026-04-27 12:30 - 13:30 | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Presented by: Edward Wang
Description: “What does it actually look like to invent something? In this talk, I trace the decade-long journey of turning a smartphone into a blood pressure monitor, from Seismo, which used smartphone accelerometers to measure pulse transit time, to BPClip, a dollar clip that brought calibration-free oscillometry to the fingertip, to VibroBP, which eliminated the attachment entirely using the phone’s vibration motor. Each project was born from the limitations of the last. And each time we thought we’d solved the problem, new layers of unknowns appeared around usability, manufacturing, and FDA classification. This is a talk about what inventing looks like when you zoom in past the papers and patents. Less about creating something new, and more about finding the unknowns between a need and its solution, and creatively working through them, one by one.”
Bio: Dr. Edward J. Wang is the Jacobs Faculty Chair in Entrepreneurship Associate Professor of Design and Electrical & Computer Engineering at UC San Diego, where he directs the Digital Health Technologies Lab. His research explores practical solutions to address real-world medical needs drawn from collaborations with clinicians and world health organizations, but solved using new and creative insights that leverage state-of-the-art applied machine learning, embedded systems, and mobile sensors. He has been named an NAI Senior Member, NIH Trailblazer, Norman Design Laureate, and Google Research Scholar. He publishes in premier computer science and health science venues including ACM IMWUT, CHI, UIST, Nature Publishing, Frontiers in Digital Health, and JMIR, having been awarded 9 best paper awards. He actively engages in the translation of research through faculty entrepreneurship. He earned his Ph.D. from the University of Washington and his B.S. from Harvey Mudd College.
Hosted by: Professor Christina Chung
When: Monday, April 27, 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/91516487260?pwd=[REDACTED]
Meeting ID: 915 1648 7260
Passcode: [REDACTED]

Statistics Seminar: Active Learning for Fair and Stable Allocations
2026-04-27 16:00 - 17:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
Presenter: Riddhiman Bhattacharya, Postdoc, UCSC
Description: We propose an active learning approach for dynamic fair resource allocation problems. In contrast to prior work that assumes full feedback from all agents on their allocations, we focus on scenarios where feedback is available only from a carefully select subset of agents at each epoch of the online resource allocation process. Despite this limitation, our algorithms achieve sub-linear regret in the number of time-periods for multiple fairness metrics commonly used in resource allocation problems and stability constraints inherent to matching mechanisms. The core innovation of our approach lies in the adaptive identification of the most informative feedback through dueling upper and lower confidence bounds. This strategy enables efficient decision-making with limited feedback, achieving favorable outcomes across various problem classes.
About the speaker: I am Riddhiman Bhattacharya, currently a postdoc at UCSC, Statistics Department, working with Justin (Sangwon Hyun). I have previously been a postdoc at Purdue and have obtained my PhD from the University of Minnesota in Statistics. I am interested in methodological development in statistics with varied applications including oceanography, biology and economics. I am also interested in theoretical development of statistics particularly in the fields of Markov Chain Monte Carlo, Optimization and Fast Sampling.

AM Seminar: Machine Learning in Molecular Simulations: From Free Energy to Vibrational Spectroscopy
2026-04-27 16:00 - 17:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
Presenter: Marcos Calegari Andrade, Assistant Professor, Chemistry and Biochemistry, UC Santa Cruz
Description: In this talk, I will demonstrate how neural networks can represent the high-dimensional potential energy surfaces of many-body systems. By achieving the accuracy of first-principles quantum calculations at a fraction of the computational cost, these models enable atomistic simulations of condensed matter at unprecedented scales. I will discuss how this approach provides a detailed lens into chemical reaction dynamics under experimentally relevant conditions and facilitates the direct calculation of observables, such as vibrational spectra, effectively bridging the gap between theoretical simulation and experimental observation.
About the speaker: Marcos Calegari Andrade is an Assistant Professor in the Department of Chemistry and Biochemistry at the University of California, Santa Cruz. He earned his PhD from Princeton University, where he developed machine learning models to simulate the chemistry and vibrational spectroscopy of condensed-phase systems. He later joined the Quantum Simulations Group at Lawrence Livermore National Laboratory, applying deep neural network models to fundamental challenges in climate and energy security. His current research at UCSC focuses on the application of machine learning to molecular simulations, with a particular emphasis on chemical reaction mechanisms, vibrational spectroscopy, and the development of automated simulation frameworks.
This seminar is hosted by Applied Mathematics

CSE Colloquium - Towards Safe and Resilient Large-scale Distributed Programming
2026-04-29 11:00 - 12:15 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Philipp Haller, KTH Royal Institute of Technology
Abstract:
Distributed programming is notoriously difficult. Not only are distributed systems concurrent, they pose additional challenges including data consistency and fault tolerance. At the same time, the share of software systems that are necessarily distributed systems is growing rapidly. As a result, too many software developers are asked to become distributed systems experts. Thus, tools and techniques for ensuring the correctness of distributed systems are urgently needed in order to leave this unsustainable trajectory. This talk presents research results towards the design and implementation of programming systems that support emerging applications and workloads; provide reliability and trust; and embrace simplicity and accessibility. Concretely, the presented work focuses on two directions.
The first direction explores a distributed programming model that provides consistency while enabling high availability for workloads operating on join-semilattices without sacrificing partition tolerance. We propose a new consistency protocol, called observable atomic consistency protocol (OACP), which leverages on-demand coordination to support both coordination-free operations as well as totally-ordered operations on replicated data types. We present a formal, mechanized model of OACP in rewriting logic and verify key correctness properties using the model checking tool Maude. Furthermore, we present the evaluation of a prototype implementation of OACP based on Akka, a widely-used actor-based middleware. The second direction explores a programming system that aims to reconcile the scalability and fault tolerance of stream processing systems with the flexibility of the actor concurrency model. The programming system ensures a failure-transparency property, effectively masking failures through transparent recovery. Our work is the first to formalize failure transparency using a small-step operational semantics, and to provide proofs of failure transparency for stateful dataflow streaming and a fault-tolerant actor-based programming model.
Bio:
Philipp Haller is an Associate Professor in the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology in Stockholm, Sweden. His main research interests are in the design and implementation of programming languages, type systems, concurrency, and distributed programming. He was part of the team that received the 2019 ACM SIGPLAN Programming Languages Software Award for the development of the Scala programming language. Prior to KTH, he was an early employee at Akka (previously Lightbend, Inc.), a start-up company developing and supporting Scala as well as frameworks for large-scale distributed programming. Prior to Akka, he was a post-doctoral fellow at Stanford University, USA, and at EPFL, Switzerland. In 2010 he received his PhD in computer science from EPFL, including a nomination for the 2010 EPFL Doctorate Award. In 2006 he received his Dipl.-Inform. degree from Karlsruhe Institute of Technology (previously University of Karlsruhe), Germany.
Hosted by: Professor Mohsen Lesani
Location: Engineering 2, Room E2-180 (Refreshments such as fruit, pastries, coffee, and tea will be provided.)
Zoom Option: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

Statistics Seminar: Advancing Statistical Rigor in Single-Cell and Spatial Omics Using In Silico Control Data
2026-05-04 16:00 - 17:00
Presenter: Guan’ao Yan, Assistant Professor, Michigan State University
Description: Single-cell and spatial transcriptomics technologies now let us map cellular diversity and tissue organization at high resolution, but the computational methods built to analyze these data are difficult to evaluate in a rigorous, reproducible way. Two key barriers are the lack of realistic synthetic data with known ground truth and the ambiguity in how we define biologically meaningful spatial patterns. This talk will introduce two simulation frameworks—scReadSim for single-cell RNA-seq and ATAC-seq data, and scIsoSim for isoform-level expression and splicing—that generate realistic sequencing reads while preserving user-specified truth. These tools enable fair, controlled benchmarking of quantification and splicing methods across experimental protocols. The talk will also present a systematic review of 34 methods for detecting spatially variable genes (SVGs) in spatial transcriptomics data, proposing a new categorization of SVGs and outlining how future benchmarks should be designed. Overall, the goal is to improve statistical rigor, interpretability, and comparability in single-cell and spatial omics analysis.
Bio: Guan’ao Yan is an Assistant Professor of Computational Mathematics, Science & Engineering at Michigan State University. He received his Ph.D. in Statistics from UCLA. His research focuses on statistical and computational methods for modern statistical genomics, particularly single-cell and spatial omics, with an emphasis on rigorous benchmarking, interpretability, and biomedical discovery.
Hosted by: Statistics Department

International Research Opportunities Forum: focus on Latin America
2026-05-07 12:00 - 14:00 | Engineering Building 2, E2-180
Please register by Monday, May 4, 2026. If attending virtually, a Zoom link will be shared after you register.

Join us for the International Research Opportunities Forum: focus on Latin America, a collaborative hybrid event hosted by the Division of Global Engagement and the Division of Graduate Studies. This forum is intended for faculty and graduate students seeking to learn more about funding available for international research collaborations in Latin America.
Attendees will hear from panelists who have successfully received funding, followed by an interactive Q&A and networking session.
Location: Engineering Building 2, E2-180 (map)
Agenda Highlights:

12:00 p.m. Check-in/Lunch
12:10 p.m. Opening Remarks, Peter Biehl, Vice Provost and Dean of Graduate Studies
12:15–12:45 p.m. Panel Discussion
12:45–1:15 p.m. Audience Q&A
1:15–2:00 p.m. Networking

Panelists:

SECTEC – Science, Technology & Innovation Sector, Consulate General of Brazil in San Francisco
Aaron Melaas – Associate Director for Research and Innovation, UC Alianza MX

Katia Obrączka – Professor, Computer Science and Engineering, CAPES Brazil funding recipient

Malin Pinsky – Associate Professor, Ecology and Evolutionary Biology, Fulbright Scholar Grantee to Chile 2026-27

Gerardo Urritua Sánchez – UC Alianza-MX Postdoctoral Fellow & Vera Rubin Postdoctoral Fellow from Mexico

AM Seminar: Column Subset Selection: Theory, Structure, and Algorithms
2026-05-11 16:00 - 17:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
Presenter: Anil Damle, Associate Professor, Cornell University
Description: The column subset selection problem is a classical topic in numerical linear algebra, with renewed interest driven by applications in computational quantum chemistry, integral equations, model reduction, and model compression in machine learning. This talk surveys recent advances that clarify how structural properties of a matrix influence the performance of column selection algorithms. We focus on structure-aware and randomized methods, highlighting both theoretical guarantees and practical algorithmic consequences.
About the speaker: Anil Damle is an associate professor of computer science at Cornell University. His research focuses on the development and analysis of robust and efficient algorithms in applied and computational mathematics that exploit structure coming from underlying physical or statisical models. He interfaces with a broad range of application areas, and his work is inherently interdisciplinary—with the ultimate goal of developing algorithms that are usable for practitioners. He received his PhD from Stanford University in computational and mathematical engineering (ICME), and his MS in applied mathematics and BS in applied mathematics and computer engineering from the University of Colorado, Boulder.
This seminar is hosted by Applied Mathematics.

CSE Colloquium - The EU’s Cybersecurity Framework: what it is, what it means
2026-05-13 11:00 - 12:15 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Chris Jay Hoofnagle, Frederik Zuiderveen Borgesius, Lothar Determann, Pieter T.J. Wolters
Abstract:
The European Union has enacted a comprehensive cybersecurity framework (the “Framework”) that imposes far-reaching obligations on developers of standalone software and connected products. This Article describes the European legislative approach before turning to a description of the Framework. Anchored by the Cyber Resilience Act and the Cybersecurity Act, and reinforced by a constellation of sector-specific measures, the Framework effectively creates a California-like-products-liability regime for software. It mandates extensive security-by-design obligations, imposes stringent conformity assessment and incident-reporting duties, and shifts substantial compliance burdens onto manufacturers, importers, and distributors. It even treats emotional wrongs caused by software as injurious. The Framework will take full effect in December 2027, meaning that companies must integrate its requirements into their current product cycles.
Bio: Chris Hoofnagle is professor of law in residence at the University of California, Berkeley, where he teaches tort law and cybersecurity.
Hosted by: Professor Alvaro Cardenas
Location: Engineering 2, Room E2-180 (Refreshments such as fruit, pastries, coffee, and tea will be provided.)
Zoom Option: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

UCSC Graduate Research Symposium
2026-05-15 13:00 - 16:00 | McHenry Library, 1156 High St, Santa Cruz, CA, 95064
Friday, May 15, 1:00-4:00 PM (PDT)
McHenry Library | Information Commons South on the Main Floor
We are delighted to invite you to the 22nd Annual Graduate Research Symposium!
This event celebrates and highlights the work of UCSC graduate students in all academic divisions. Any enrolled graduate student is welcome to present either a poster, talk, or mixed media presentation. The event is free and open to the public. Please see the Graduate Division website for more information.

Statistics Seminar: Unifying Regression-Based and Design-Based Causal Inference in Time-Series Experiments and Crossover Experiments
2026-05-18 16:00 - 17:00 | Jack Baskin Engineering, Baskin Engineering 1156 High Street, Santa Cruz, CA, 95064
Presenter: Peng Ding, Associate Professor, UC Berkeley
Description: I will present some recent results on unifying regression-based and design-based causal inference in time-series experiments and crossover experiments. Part I: Time-series experiments, also called switchback experiments or N-of-1 trials, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework, recent research has studied time-series experiments from the design-based perspective, relying solely on the randomness in the design to drive the statistical inference. Focusing on simpler statistical methods, we examine the design-based properties of regression- based methods for estimating treatment effects in time-series experiments. We demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors. Additionally, we show that asymptotically, the heteroskedasticity and autocorrelation consistent variance estimators provide conservative estimates of the true, design-based variances. This part is based on https://arxiv.org/pdf/2510.22864
Part II: Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit, these designs can effectively eliminate between-unit variation and facilitate the identification of both instantaneous effects of current treatments and carryover effects from past treatments. They are widely used in traditional biomedical studies and are increasingly adopted in modern digital platforms. However, standard analyses of crossover designs often rely on strong parametric models, making inference vulnerable to model misspecification. We unify the analysis of crossover designs using least squares, with restrictions on the coefficients and weights on the units. Based on the theory, we recommend specifying the regression function, weighting scheme, and coefficient restrictions to assess identifiability, construct efficient estimators, and estimate variances in a unified manner. This part is based on https://arxiv.org/pdf/2511.09215
About the speaker: Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. He obtained his Ph.D. from the Department of Statistics, Harvard University in May 2015 and worked as a postdoctoral researcher in the Department of Epidemiology, Harvard T. H. Chan School of Public Health until December 2015. Previously, he received his B.S. in Mathematics, B.A. in Economics, and M.S. in Statistics from Peking University.
This seminar is hosted by Professor Allen Kei.
