Events

CM Seminar - "How Technology-Mediated Food Interactions Support Family Connection and Routine Reconstruction"
2026-01-05 12:30 - 13:30 (America/Los_Angeles) | Silicon Valley Campus, 3175 Bowers Avenue, Santa Clara, CA, 95054, United States
Presented by: Aswati Panicker
Description: “In this talk, I draw on work in human-food interaction (HFI) to examine how food can serve as a rich interaction medium for connection and routine reconstruction in long-distance families. I highlight insights from three of my studies that explore this question across different technological forms. First, I discuss how families navigate shifting meanings of “healthy eating” during life transitions, and the tensions that arise when disclosing new goals and priorities in health-sharing or tracking tools. Second, I show how family members expressed hesitations, value conflicts, and visions for cultural or playful moments when imagining embodied technology such as a social robot within their domestic spaces. Third, through a mobile app probe for teaching food knowledge and recipes, I uncover the subtleties of how family members seek to initiate, learn, guide, or be guided through everyday food practices. Throughout, I outline design implications and close by reflecting on how technology-mediated food interactions might extend to other contexts and routines beyond family life.”
Bio: Aswati Panicker is a postdoctoral scholar at the University of California, Santa Cruz, working with Prof. Christina Chung. Her research is at the intersection of HCI and CSCW, focusing on how technologies are designed and used in social and health-related contexts. She takes a human-centered, participatory approach and draws on theories from family studies and sociology in her work. She earned her PhD in Informatics from Indiana University Bloomington in July 2025. You can read more at aswatipanicker.com

Hosted by: Professor Christina Chung
When: Monday, January 5, 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/98742808551?pwd=[REDACTED]
Meeting ID: 987 4280 8551
Passcode: [REDACTED]

AM Seminar with Dr. Truong Vu
2026-01-05 16:00 - 17:00 | CA
Presenter: Dr. Truong Vu, IPAM and MSU
Description: We present a framework for the gradient flow of sharp-interface surface energies that couple to embedded curvature active agents. We use a penalty method to develop families of locally incompressible gradient flows that couple interface stretching or compression to local flux of interfacial mass. We establish the convergence of the penalty method to an incompressible flow both formally for a broad family of surface energies and rigorously for a more narrow class of surface energies.
Bio: Dr. Vu received a Ph.D. in Applied Mathematics from the Department of Mathematics, Statistics, and Computer Science at University of Illinois at Chicago. Dr. Vu is currently a Postdoctoral Fellow at the Institute for Pure and Applied Mathematics (UCLA) and a visiting faculty in the Department of Mathematics at Michigan State University.
Hosted by: Applied Mathematics

BME 280B Seminar: Nature’s Miniature Masterpieces - Nanobodies as Small but Mighty Antibodies for the next Pandemic
2026-01-08 11:40 - 13:15 | Physical Sciences Building, Physical Sciences Building, Santa Cruz, CA, 95064
Presenter: Katja Hanack, Founder and CEO, New/Era/Mabs
Description: Nanobodies combine remarkable simplicity with surprising power. Their small size allows them to reach targets that remain inaccessible to conventional antibodies, while maintaining high specificity and stability. Their compact architecture allows them to access targets that conventional antibodies cannot reach, yet they preserve the specificity and power that make antibody therapeutics so transformative. In this talk I will introduce the science behind selma, a cell based discovery platform developed over more than a decade to rapidly identify high quality antibodies and nanobodies. I will explore why these tiny binders matter, how they differ from classical antibodies, and what their unique biology enables for diagnostics and therapeutics.
The presentation will conclude with my current project on immune infrastructure and how pre validated nanobody archives can shift the pandemic response from a reactive model to proactive preparedness for future outbreaks.
Bio: Katja Hanack, PhD, MBA, is the Founder and CEO of new/era/mabs and a leading expert in antibody discovery for diagnostic and therapeutic applications, with a particular focus on nanobody technologies. With more than 20 years of experience, she has developed pioneering platforms that enable the efficient generation and selection of next-generation monoclonal antibodies and nanobodies. She holds a Biology degree from Humboldt University of Berlin and a PhD in Biotechnology from the University of Potsdam. As a former Professor of Biochemistry and Biology at the University of Potsdam, she built and led a research group of 25 scientists, secured over €16 million in external funding, and authored more than 40 peer-reviewed publications. Since 2017, Dr. Hanack has contributed to translational innovation as an industrial advisor for SPARK Stanford and SPARK Berlin, supporting academic teams in bringing biomedical discoveries to patients.
Hosted by: Professor Rebecca Dubois, BME Department

Kathleen Schmidt: Sequential Experimental Design for Materials Strength Model Calibration
2026-01-12 16:00 - 17:00 | CA
Presenter: Katie Schmidt, UQ & Optimization Group Leader, Lawrence Livermore National Laboratory
Description: Due to the time and expense associated with physical experiments, there is significant interest in optimal selection of the conditions for future experiments. Selection based on reduction in parameter uncertainty provides a natural path forward. We consider this type of optimal sequential design in the context of Bayesian calibration of materials strength models with the strength model characterizing the evolving resistance of a material to permanent strain. This problem is particularly challenging because different types of experiments and associated diagnostics are employed across strain rate regimes. For lower-strain-rate experiments, stress-strain curves can be measured directly. For higher-strain-rate experiments, strength must be inferred (e.g., from the deformation of a cylinder of material in a Taylor cylinder experiment). We employ data fusion in our sequential design methodology to incorporate these multiple experimental modalities.
LLNL-ABS-835231 This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Bio: Katie Schmidt is the UQ & Optimization Group Leader at Lawrence Livermore National Laboratory. She joined LLNL in 2016 after earning a PhD in Applied Mathematics from North Carolina State University. During her time at the lab, Katie has been involved in a variety of uncertainty quantification problems related to national security as well as outreach and education through LLNL’s Data Science Institute. Her research interests include mixed-effects models, Bayesian inference, sequential design, and sensitivity analysis.
Hosted by: Statistics Department

AM Seminar: Science in the Age of Foundation Models
2026-01-12 16:00 - 17:00 | https://ucsc.zoom.us/j/96136632376?pwd=[REDACTED]
Presenter: Dr. Danielle Robinson, AWS AI
Description: In this talk, I will discuss the large impact of foundation models within the sciences with a particular focus on the importance of physical constraints and uncertainty quantification. First, I will detail our novel ProbConserv framework for enforcing hard constraints within black-box deep learning models. ProbConserv provides uncertainty quantification, and can be used to enforce conservation law constraints as well as other nonlinear constraints. Next, I will discuss its extensions to ensembles of Neural Operators and out-of-distribution (OOD) estimations, as well as how it can be used in constrained generative modeling of PDEs. I will then show applications of our work in computational fluid dynamics (CFD), including weather forecasting, aerodynamics and chaotic systems. Lastly, I will conclude with a forward-looking view of the next steps for designing a physics foundation model that can be applied across various types of flows, geometries and boundary conditions, and what is needed for such a model to be developed.

Bio: Danielle Maddix Robinson is a Senior Applied Scientist in the Machine Learning Forecasting Group within AWS AI. She graduated with her PhD in Computational and Mathematical Engineering from the Institute of Computational and Mathematical Engineering (ICME) at Stanford University. She was advised by Professor Margot Gerritsen and developed robust numerical methods to remove spurious temporal oscillations in the degenerate nonlinear Generalized Porous Medium Equation. She is passionate about the underlying numerical analysis, linear algebra and optimization methods behind numerical PDEs and applying these techniques to deep learning. During her PhD, she also did an internship at NVIDIA with Joe Eaton and Alex Fender, and implemented an efficient and load-balanced sparse matrix vector multiplication (spmv) in cuSPARSE and nvGRAPH libraries. She is excited to be back at NVIDIA today. After graduating, Danielle joined AWS in 2018, and has been working on developing statistical and deep learning foundation models for time series forecasting including Chronos. Over the last several years, she has been leading the research initiative on developing models for physics-constrained machine learning for scientific computing on the DeepEarth team. In particular, she has researched how to apply ideas from numerical methods, e.g., finite volume schemes, to improve the accuracy of black-box ML models for PDEs with applications to ocean and climate models, aerodynamics and chaotic systems.

Hosted by: Applied Mathematics

Link: https://ucsc.zoom.us/j/96136632376?pwd=[REDACTED]

Be Inspired: Explore Graduate Studies in STEM
2026-01-12 17:00 - 18:30 | CA
Not sure if graduate school is right for you?
Join us to learn what graduate school is really about and explore whether it’s the right path for you. We’ll cover topics such as qualifying exams, funding options, common misconceptions, and more!
Click the link below to register for the event:
https://ucsc.zoom.us/webinar/register/WN_31OHhwc7QPqJ7nSyiuAUNg

Macroeconomics & International Finance Seminar Series Presents: Dean Corbae
2026-01-13 13:40 - 15:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Macroeconomics and International Finance Seminar
Date: Tuesday, January 13, 2026
Time: 1:40-3:00 p.m.
Location: E2-499

Speaker: Dean Corbae
Title: William Sellery Trukenbrod Chair in Finance
Affiliation: University of Wisconsin – Madison
Host: Grace Gu Steadmon

Seminar title: A Quantitative Model of Bank Merger Dynamics

ABSTRACT:
We develop a simple model of the bank merger process to study the rise in bank concentration following the deregulation of bank branching in the Riegle-Neal Act of 1994. Motivated by the data where currently 10 (dominant) banks have over 55 percent of the U.S. deposit market share while the remaining over 4000 (fringe) banks cover the rest, we apply a dominant-fringe framework with a merger stage to model the rise in concentration following the change in regulation making interstate branching possible. First, we study the effect of the merger wave on competition, efficiency, and stability of the banking industry. Then we use our model to understand the interaction between regulatory and monetary policy. Specifically, how has the bank lending channel of monetary policy been affected by rising concentration; has it amplified or dampened the effectiveness of monetary policy? How might monetary policy itself contribute to mergers and rising concentration?

Applied Microeconomics and Trade Seminar Series Presents: Olivia Bordeu
2026-01-15 01:40 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Applied Microeconomics and Trade Seminar
Date: Thursday, January 15, 2026
Time: 1:40 – 3:00 p.m.
Location: E2-499

Speaker: Olivia Bordeu
Title: Assistant Professor of Economics
Affiliation: University of California, Berkeley
Host: Jeremy West
Seminar title: Bank Branches and the Allocation of Capital across Cities

ABSTRACT: We study how banking market structure and branch networks shape the spatial mobility of capital. Using administrative loan-level data from Chile, we show that bank-level deposit shocks lead receiving banks to increase lending and lower interest rates relative to other banks. Interest rate reductions are concentrated in cities where the bank has a small market share, consistent with local market power. We develop and estimate a quantitative spatial model with multi-city banks, oligopolistic local credit markets, and frictions in interbank lending. These channels lead to spatial dispersion in interest rates and the marginal productivity of physical capital, reducing GDP. Interbank frictions reduce steady-state GDP by 0.04%, while spatial variation in loan markups reduces GDP by 0.5%. Bank mergers improve financial integration between cities but reduce competition, generating heterogeneous welfare effects that depend on the merging banks’ geographic overlap.

Behavioral, Econometrics and Theory Seminar Series Presents: Roberto Corrao
2026-01-20 13:40 - 15:00 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Economics Behavioral, Econometrics, & Theory Seminar
Date: Tuesday, January 20, 2026
Time: 1:40-3:00 p.m.
Location: E2-499

Speaker: Roberto Corrao
Title: Assistant Professor of Economics
Affiliation: Stanford University
Host: Gerelt Tserenjigmid

Seminar title: Contractibility Design

ABSTRACT:
We introduce a model of incentive contracting in which the principal, in addition to
writing contracts, must engage in contractibility design: creating an evidence structure
that allows them to prove when the agent has breached the contract. Designing an
evidence structure entails both (i) front-end costs borne ex ante, such as those of
drafting contracts, and (ii) back-end costs borne ex post, such as those of generating
evidence. We find that, under even small front-end costs, optimal contracts are coarse,
specifying finitely many contingencies out of a continuum of possibilities. In contrast,
under even large back-end costs, optimal contracts are complete. Applied to the design
of procurement contracts, our results rationalize: (i) the discreteness of contracts, (ii)
the presence of similarly vague contracts in low-stakes and high-stakes settings, and
(iii) the discontinuous adjustment of contracts to changes in the economic environment.

CSE Colloquium - Constraining Chaos: Toward Faithful and Semantic Decoding in Language Models
2026-01-21 11:00 - 12:30 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Loris D’Antoni, UC San Diego
Abstract:
Language models excel at producing fluent text, but in domains like code and math, fluency isn’t enough — outputs must obey strict syntactic and semantic rules. A new wave of research is rethinking decoding itself: not as a process of sampling words, but as a negotiation between probability, structure, and meaning. In this talk, I’ll explore how grammar and semantics can be embedded into the decoding loop, how we can sample from the true model conditional distribution under constraints, and how programmable abstractions make it possible to enforce properties like type safety or program invariants. The result is a vision of decoding that is faithful to the model yet governed by rules, pointing toward a future where LLMs generate not just plausible text, but reliably correct output.
Bio:
Loris D’Antoni is a Jacobs Faculty Scholar and Associate Professor in the Department of Computer Science and Engineering at the University of California San Diego. His research helps people build trustworthy software. His work has introduced new frameworks for verifying and synthesizing programs—ranging from resilient network configurations to robust decision-making systems—and, more recently, methods for aligning language models with user intent.
He is the recipient of an NSF CAREER Award and a Microsoft Research Faculty Fellowship, and was selected as a Vilas Associate at the University of Wisconsin-Madison. He has also received Google, Amazon, and Meta Faculty Awards, and the Morris and Dorothy Rubinoff Dissertation Award. His papers have earned several best paper awards and nominations, including at TACAS, ESOP, ICDCN, and SBES.
Loris received his B.S. and M.S. in Computer Science from the University of Torino, and his Ph.D. in Computer Science from the University of Pennsylvania. Before joining UC San Diego, he was a faculty member at the University of Wisconsin–Madison.
Hosted by: Professor Nikos Tziavelis
Location: Engineering 2, Room E2-180
*Light refreshments such as coffee, pastries, and fruit will be available.
Zoom: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

Applied Microeconomics and Trade Seminar Series Presents: Guo Xu
2026-01-22 01:40 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Applied Microeconomics and Trade Seminar
Date: Thursday, January 22, 2026
Time: 1:40 – 3:00 p.m.
Location: E2-499

Speaker: Guo Xu
Title: Associate Professor of Economics
Affiliation: University of California, Berkeley
Host: Ajay Shenoy

Seminar title: Personnel is Policy: Delegation and Political Misalignment in the Rulemaking Process

ABSTRACT: We combine comprehensive data on the U.S. federal rulemaking process with individuallevel personnel and voter registration records to study the consequences of partisan misalignment between regulators and the president. We present three main results. First, even important pieces of new regulation are frequently delegated to bureaucrats who are politically misaligned. Second, rules that are overseen by misaligned regulators take systematically longer to complete, are more verbose, generate more negative feedback from the public, and are more likely to be challenged in court. Third, in assigning regulators to rules, agency leaders often face a sharp tradeoff between political alignment and expertise. Agency frictions notwithstanding, they tend to resolve this tradeoff in favor of expertise.

BME Seminar: Rotation Talks
2026-01-22 11:40 - 13:15 | Physical Sciences Building, Physical Sciences Building, Santa Cruz, CA, 95064
Presenter: Grad Students
Description: Rotation Talks
Bio: N/A
Hosted by: Professor Rebecca DuBois, BME Department

Statistics Seminar: Heterogeneous Statistical Transfer Learning
2026-01-23 12:00 - 13:00 | https://ucsc.zoom.us/j/94465292273?pwd=[REDACTED]
Presenter: Subhadeep Paul, Associate Professor, Ohio State University
Description: In the first part of the talk, we consider the problem of Transfer Learning (TL) under heterogeneity from a source to a new target domain for high-dimensional regression with differing feature sets. Most homogeneous TL methods assume that target and source domains share the same feature space, which limits their practical applicability. In applications, the target and source features are frequently different due to the inability to measure certain variables in data-poor target environments. Conversely, existing heterogeneous TL methods do not provide statistical error guarantees, limiting their utility for scientific discovery. Our method first learns a feature map between the missing and observed features, leveraging the vast source data, and then imputes the missing features in the target. Using the combined matched and imputed features, we then perform a two-step transfer learning for penalized regression. We develop upper bounds on estimation and prediction errors, assuming that the source and target parameters differ sparsely but without assuming sparsity in the target model. We obtain results for both when the feature map is linear and when it is nonparametrically specified as unknown functions. Our results elucidate how estimation and prediction errors of HTL depend on the model’s complexity, sample size, the quality and differences in feature maps, and differences in the models across domains. In the second part of the talk, going beyond linear models, I will discuss a transfer learning method for nonparametric regression using a random forest. The unknown source and target regression functions are assumed to differ for a small number of features. Our method obtains residuals from a source domain-trained Centered RF (CRF) in the target domain, then fits another CRF to these residuals with feature splitting probabilities proportional to feature-residual distance covariance. We derive an upper bound on the mean square error rate of the procedure that theoretically brings out the benefits of transfer learning in random forests. Our results explain why shallower trees in the residual random forest in the target domain provide implicit regularization.
Bio:Subhadeep Paul is an Associate Professor in the Department of Statistics at The Ohio State University. He is also a faculty fellow and previously served as a co-director of the foundations of data science and AI community at the Translational Data Analytics Institute at Ohio State. He received his PhD in Statistics from the University of Illinois at Urbana-Champaign in 2017. His research focuses on statistical analysis of complex network-linked data and transfer and federated statistical learning. His research has been funded by two NSF grants from the algorithms of threat detection and mathematics of digital twins programs.
Hosted by: Statistics Department
Zoom link: https://ucsc.zoom.us/j/94465292273?pwd=[REDACTED]

ECE Seminar: Tactile sensing: At the boundary between mechanical and computational intelligence in robotic grippers
2026-01-26 10:40 - 11:45 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Dr. Hannah Stuart, Associate Professor in the Department of Mechanical Engineering, University of California at Berkeley
Description: Robot grippers typically include mechanical intelligence (e.g., underactuation, compliance) or computational intelligence (e.g., fully actuated with a wide array of sensors). Next generation grippers and hands will require both intelligences to work in concert across applications with resilience and dexterity. This talk will introduce the concept of mechanical and computational intelligence co-design through example case studies that focus on the particular importance of embodied sensitivity as a feature of the co-design process. For example, the most recent work on the Smart Suction Cup, conducted largely by Dr. Jungpyo Lee, demonstrates how design decisions like the number of sensitive chambers influences the resultant robot arm controller as well as physical compliance and manufacturing feasibility and cost.
Bio: Dr. Hannah Stuart is an Associate Professor in the Department of Mechanical Engineering at the University of California at Berkeley. She received her BS in Mechanical Engineering at the George Washington University in 2011, and her MS and PhD in Mechanical Engineering at Stanford University in 2013 and 2018, respectively. Her research focuses on understanding the mechanics of physical interaction in order to better design systems for dexterous manipulation. Applications range from remote robotics to assistive orthotics. Recent awards include the NSF CAREER grant, NASA Early Career Faculty grant, Hellman Fellows Fund grant, and Johnson & Johnson Women in STEM2D grant. She is a Senior Member of IEEE.
Hosted by: Professor Soumya Bose, ECE Department
Zoom Link: https://ucsc.zoom.us/j/97975378707?pwd=[REDACTED]

Statistics Seminar: Boosting Biomedical Imaging Analysis via Distributed Functional Regression and Synthetic Surrogates
2026-01-26 12:00 - 13:00 | https://ucsc.zoom.us/j/92479478035?pwd=[REDACTED]
Presenter: Guannan Wang, Associate Professor, The College of William & Mary
Description: Generative AI has emerged as a powerful tool for synthesizing biomedical images, offering new solutions to challenges such as data scarcity, privacy constraints, and modality imbalance. However, the reliable use of synthetic images in scientific analysis requires principled statistical frameworks that can assess fidelity and rigorously quantify uncertainty. In this talk, I present a distributed functional data analysis approach for comparing original and AI- generated biomedical images through their mean and covariance structures. Using spline-based representations on complex imaging domains, we construct simultaneous confidence regions, enabling formal inference on original-synthetic differences and providing statistical safeguards for downstream analyses. Building on this foundation, I demonstrate how synthetic images can
be safely incorporated into functional regression models to learn spatially varying covariate effects when key imaging modalities are partially observed. Applications to large-scale neuroimaging studies illustrate how integrating generative AI with rigorous statistical inference enhances the reliability, interpretability, and scientific value of modern biomedical imaging analyses.
Bio: Guannan Wang is a Diamond Term Distinguished Associate Professor in the Department of Mathematics at William & Mary. She received a Ph.D. in Statistics and an M.S. in Computer Science from the University of Georgia in 2015. Her research focuses on the statistical foundations of generative AI, distributed and federated learning, and spatial and functional data analysis, with applications to neuroimaging, public health, and environmental and social sciences. She has published over 30 peer-reviewed articles in leading statistical journals, including JASA, JCGS, Statistica Sinica, Biometrics, and JMLR, and her work has been supported by the NIH, NSF, and the Simons Foundation.
Hosted by: Statistics Department
Zoom link: https://ucsc.zoom.us/j/92479478035?pwd=[REDACTED]

CM Seminar - "Revealing Hidden Stories: Co-Designing the Thámien Ohlone Augmented Reality Tour"
2026-01-26 12:30 - 13:30 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presented by: Kai Lukoff
Description:
The Santa Clara University campus is adorned with symbols and monuments, including a Spanish Mission Church, that highlight its Catholic heritage. However, the presence and history of the Ohlone Native Americans, who have inhabited this land for thousands of years and continue to live in the region, receive little to no recognition. How can we utilize augmented reality (AR) to share these hidden stories?
In collaboration with the Muwekma Ohlone Tribe, our interdisciplinary team developed the Thámien Ohlone AR tour. This tour reveals hidden stories, encourages visitors to engage in critical reflection, and inspires visions of a more just future and received the Best Movie Award at CHI 2024, the leading conference in the field of human-computer interaction. This talk will share insights on co-designing location-based AR experiences for social impact and explore the potential of AR in preserving cultural heritage.
Bio: Kai Lukoff is an assistant professor in the Department of Computer Science & Engineering at Santa Clara University. He leads the Human-Computer Interaction Lab, focusing on technologies with social impact. His recent work focuses on co-design methods for location-based augmented reality. His research has been featured in prominent conferences such as CHI, CSCW, IMWUT, and DIS, and he was honored with the 2023 Outstanding Dissertation Award from ACM SIGCHI.

Hosted by: Professor Sri Kurniawan
When: Monday, January 26, 2026 from 12:30PM to 1:30PM
Location:
IN-PERSON @ UCSC Main Campus, E2-280.
Viewing room @ SVC 3212.
LUNCH WILL BE PROVIDED AT BOTH LOCATIONS! Faculty and students are highly encouraged to attend.
Zoom info:
https://ucsc.zoom.us/j/95105219890?pwd=[REDACTED]
Meeting ID: 951 0521 9890
Passcode: [REDACTED]

AM Seminar: Probing Forced Responses and Causality in Data-Driven Climate Emulators: Conceptual Limitations and the Role of Reduced-Order Models
2026-01-26 16:00 - 17:00 | https://ucsc.zoom.us/j/97450297092?pwd=[REDACTED]
Presenter: Fabrizio Falasca, New York University
Description: A central challenge in climate science and applied mathematics is developing data-driven models of multiscale systems that capture both stationary statistics and responses to external perturbations. Current neural climate emulators aim to resolve the atmosphere–ocean system in all its complexity but often struggle to reproduce forced responses, limiting their use in causal studies such as Green’s function experiments. To explore the origin of these limitations, we first examine a simplified dynamical system that retains key features of climate variability. We argue that the ability of emulators of multiscale systems to reproduce perturbed statistics depends critically on (i) the choice of an appropriate coarse-grained representation and (ii) careful parameterizations of unresolved processes. These insights highlight reduced-order models, tailored to specific goals, processes, and scales, as valid alternatives to general-purpose emulators. We next consider a real-world application, developing a neural model to investigate the joint variability of the surface temperature field and radiative fluxes. The model infers a multiplicative noise process directly from data, largely reproduces the system’s probability distribution, and enables causal studies through forced responses. We discuss its limitations and outline directions for future work. These results expose key challenges in data-driven modeling of multiscale physical systems and underscore the value of coarse-grained, stochastic approaches.Throughout, we propose linear response theory as a rigorous framework for evaluating neural models beyond stationary statistics, probing causal mechanisms, and guiding model design.
Bio: Fabrizio Falasca is physicist working at the intersection of statistical physics, applied mathematics and climate science. He acquired his master degree in Physics of Complex Systems in the University of Turin in Italy. He then moved to Atlanta to pursue a PhD in Climate Science under the supervision of Annalisa Bracco. In the last 5 years he has been working in the Courant Institute of Mathematical Science in the group of Laure Zanna. His work span response theory, causal inference, data-driven modeling, and their applications to climate dynamics and change.

Hosted by: Applied Mathematics
Zoom Link: https://ucsc.zoom.us/j/97450297092?pwd=[REDACTED]

CSE Colloquium - Towards Relational Foundation Models: Zero-Shot Forecasting over Relational Databases
2026-01-28 11:00 - 12:15 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Charilaos I. Kanatsoulis, Stanford University
Abstract: Foundation models have transformed unstructured domains such as language and vision, yet relational datasets, where most enterprise knowledge lives, still rely on brittle, task-specific ML pipelines. I will begin by introducing Relational Deep Learning (RDL), a general framework for learning directly from heterogeneous multi-table data, capturing structure across entities, attributes, and relationships without handcrafted schemas or features.
Building on this paradigm, I will present the Relational Transformer (RT), a schema-invariant model pretrained across diverse relational databases that performs structural learning with in-context information and transfers zero-shot to new databases and predictive tasks. By modeling both inter- and intra-table dependencies and reframing prediction as pattern recognition inside a unified latent relational space, RT represents a concrete step toward relational foundation models that can be prompted, reused, and generalized for new problems.
Bio: Charilaos I. Kanatsoulis is a Research Scientist in the Department of Computer Science at Stanford University. He previously was a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania and received his Ph.D. in Electrical and Computer Engineering from the University of Minnesota, Twin Cities. His research lies at the intersection of machine learning and signal processing, with a focus on Transformer and foundation model design for structured data, graph representation learning, tensor analysis, and explainable AI. His work has been recognized with the Best Paper Award at the KDD Temporal Graph Learning Workshop (2025) and the Best Student Paper Award at IEEE CAMSAP (2023). He co-instructs CS246 and CS224W at Stanford and previously taught ESE 5140 at Penn. He has organized several community events, including the Graph Signal Processing short course at IEEE ICASSP 2023, the Stanford Graph Learning Workshop (2024–2025), the Relational Deep Learning tutorial at ACM KDD 2025, and the New Perspectives in Advancing Graph Machine Learning Workshop at NeurIPS 2025.
Hosted by: Professor Nikos Tziavelis
Location: Engineering 2, Room E2-180 (Refreshments such as coffee, pastries, and fruit will be provided.)
Zoom: https://ucsc.zoom.us/j/93445911992?pwd=[REDACTED]

Statistics Seminar: Inferring Unobserved Trajectories from Multiple Temporal Snapshots
2026-01-28 12:00 - 13:00 | https://ucsc.zoom.us/j/93769232971?pwd=[REDACTED]
Presenter: Yunyi Shen, Ph.D. Candidate, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Description: Practitioners often aim to infer an unobserved population trajectory using sample snapshots at multiple time points. E.g. given single-cell sequencing data, scientists would like to learn how gene expression changes over a cell’s life cycle. But sequencing any cell destroys that cell. So we can access data for any particular cell only at a single time point, but we have data across many cells. The deep learning community has recently explored using Schrödinger bridges (SBs) and their extensions in similar settings. However, existing methods either (1) interpolate between just two time points or (2) require a single fixed reference dynamic (often set to Brownian motion within SBs). But learning piecewise from adjacent time points can fail to capture long-term dependencies. And practitioners are typically able to specify a model family for the reference dynamic but not the exact values of the parameters within it. So I propose a new method that (1) learns the unobserved trajectories from sample snapshots across multiple time points and (2) requires specification only of a family of reference dynamics, not a single fixed one. I demonstrate the advantages of my method on simulated and real data, across applications in biology and oceanography.
Bio: Yunyi Shen is currently a Ph.D. candidate in the Department of Electrical Engineering and Computer Science at MIT. He works in probabilistic machine learning and statistics on problems where data are scarce or noisy, and as a result require adaptive data collection, incorporation of domain-specific structure, and careful downstream evaluation. Drawing on a background in the physical and life sciences, his work is shaped by close interdisciplinary collaborations and motivated by scientific problems in biology and physics, such as gene regulation, fluid dynamics in cells, wildlife monitoring, and time-domain astronomy.
Hosted by: Statistics Department
Zoom link: https://ucsc.zoom.us/j/93769232971?pwd=[REDACTED]

BME 280B Seminar: Satellite repeats encode megabase-scale transcription factor hubs
2026-01-29 11:40 - 13:15 | Physical Sciences Building, Physical Sciences Building, Santa Cruz, CA, 95064
Presenter: Matt Franklin, Postdoctoral Researcher, Stanford University
Description: Eukaryotic genomes contain large stretches of repetitive DNA called satellite DNA, often found near centromeres and ribosomal DNA regions. In humans, alpha satellite has well-established roles in centromere biology, however the functions of other human satellite DNAs remain largely unexplored.
We recently identified the Hippo pathway effector TEAD as a novel Human Satellite 3 (HSat3) binding TF. Because HSat3 is highly enriched near ribosomal DNA (rDNA) genes, we examined whether the Hippo pathway regulates rDNA via HSat3. Our work demonstrates that HSat3 localizes the Hippo factors YAP and TEAD inside the nucleolus, where YAP directly activates ribosomal RNA (rRNA) transcription. These findings present the first evidence that the Hippo pathway factor YAP directly regulates RNA Polymerase I activity.
Disparate studies have identified examples of transcription factors that bind repetitive DNA elements through motif recognition. However, a systematic search for such factors has not been conducted. Using Telomere-to-telomere genome assemblies, we predicted and validated dozens of new satellite-binding TFs, many of which are part of highly conserved signaling pathways. Beyond revealing a direct relationship between the Hippo pathway and ribosomal DNA regulation, this work demonstrates that satellite DNA can encode a broad range of functional motifs, hinting at new roles for these enormous genomic elements.
Bio: Following his undergraduate studies, Matt conducted a 1-year research fellowship at EMBL Hamburg, where he worked on X-ray scattering methods for structural biology. He then earned his PhD in chemical engineering at Stanford University, where he investigated mechanotransduction and Hippo pathway signaling. Matt continued this research as a postdoc under Kun-Liang Guan at UC San Diego, where he discovered that Hippo pathway effectors bind repetitive DNA elements. To expand on his newfound interest in repetitive DNA, Matt returned to Stanford as a postdoctoral researcher under Nicolas Altemose, where he is studying the functions of satellite repeats as hubs for transcription factor binding.
Hosted by: Professor Karen Miga, BME Department

Allen van Gelder Memorial
2026-01-29 13:00 - 16:30 | University Center, University Center, Santa Cruz, CA, 95064
You are cordially invited to an event celebrating the life and research legacy of Allen van Gelder, who passed away in April 2025 after 37 years of dedicated service to the Computer Science and Engineering Department at UC Santa Cruz.
Thursday, January 29, 2025
Reception begins 1pm, Program begins 1:30pm
Alumni Room, University Center, UC Santa Cruz
Please RSVP via the following link:
https://forms.gle/iyFTL2aAxLMWdRMU9
The gathering will include three presentations reflecting on Allen’s contributions to computer science, followed by an opportunity for attendees to share remembrances and stories
On Allen’s contributions to databases and logic programming
Jeff Ullman, Stanford W. Ascherman Professor of Computer Science (Emeritus), Computer Science Department, Stanford University
On Allen’s contributions to computer graphics and visualization
Claudio Silva, Institute Professor of Computer Science and Engineering, NYU Tandon School of Engineering, New York University
On Allen’s contributions to satisfiability
Olaf Beyersdorff, Professor of Theoretical Computer Science, Institute of Computer Science, Friedrich Schiller University Jena
Whether you were a colleague, student, or friend, we hope you’ll join us in celebrating Allen’s career and its impact.

ECE Seminar: Advanced Packaging as the Engine of the AI Systems Era
2026-02-02 10:40 - 11:45 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Tolga Acikalin, System and Package Architect, Lumilens
Description: The rapid rise of artificial intelligence and machine learning—most notably recent breakthroughs in large language models—is reshaping the trajectory of the semiconductor industry and ushering in a new era of system innovation. As performance scaling at the device level slows, heterogeneous integration (HI) has emerged as a foundational technology to sustain advances in computing and communication. By integrating separately manufactured components with diverse functions into a single system, HI enables new levels of functionality, performance, and efficiency that are no longer achievable through traditional scaling alone.
Realizing the full potential of heterogeneous systems demands a shift toward holistic system-level co-design, with advanced packaging assuming a central and strategic role. This talk will briefly review the evolution of packaging technologies and then focus on advanced packaging architectures that enable heterogeneous integration.Topics will include advances in 2D and 3D interconnect technologies, the introduction of novel packaging materials such as glass substrates, and the growing role of photonic links, including co-packaged optics enabled by silicon photonics. The talk will conclude with a discussion of power delivery and thermal management as system-level challenges and opportunities that will shape the next generation of high-performance, energy-efficient systems.
Bio: Tolga Acikalin received his Bachelor of Science degree in Mechanical Engineering from Middle East Technical University in Ankara, Turkey, and his Master of Science and Ph.D. degrees from Purdue University in West Lafayette, Indiana.
He joined Intel in 2007 as a Research and Development Engineer, working on assembly and test pathfinding projects within the Technology and Manufacturing Group in Chandler, Arizona. From 2013 to 2025, he was a Principal Engineer at Intel Labs in Santa Clara, California, where he led and influenced innovative strategies for heterogeneous system integration, spanning package- to wafer-scale solutions, with a strong emphasis on next-generation interconnect technologies. Tolga is currently a System and Package Architect at Lumilens, where he focuses on next-generation photonic interconnect solutions, ranging from near-packaged optics to co-packaged optics.
His technical interests include co-packaged optics and silicon photonics, optical and sub-THz to THz RF high-speed interconnects and the associated advanced package architectures, novel advanced packaging solutions such as glass substrates, and optical computing. Tolga has authored or co-authored more than 15 peer-reviewed journal and conference publications in leading APS, ASME, and IEEE venues, including best paper awards at IEEE RFIC and JSCC. He holds nine issued patents and more than 27 additional patent filings.
Hosted by: Professor Soumya Bose, ECE Department
Zoom Link: https://ucsc.zoom.us/j/97975378707?pwd=[REDACTED]

Statistics Seminar: Mathematical Foundations for Machine Learning from a Nonlinear Time Series Perspective
2026-02-02 12:00 - 13:00 | https://ucsc.zoom.us/j/96647674332?pwd=[REDACTED]
Presenter: Jiaqi Li, William H. Kruskal Instructor, University of Chicago
Description:Modern machine learning (ML) algorithms achieve remarkable empirical success, yet providing rigorous statistical guarantees remains a major challenge, particularly in distributional theory and online inference methods. In this talk, we will introduce a novel framework to provide mathematical foundations for ML by bringing powerful tools in nonlinear time series. First, we focus on the stochastic gradient descent (SGD) with constant learning rates. By interpreting the SGD sequence as a nonlinear AR(1) process, we can establish the geometric moment contraction (GMC) for SGD regardless of initializations. By this GMC property, we can derive refined asymptotic theory of SGD and its averaging variant, including general moment convergence, quenched central limit theorems, quenched invariance principles, and sharp Berry- Esseen bounds. Then, we extend this theoretical framework to SGD with dropout regularization, a widely used but theoretically underexplored technique in deep learning. By establishing GMC under explicit learning-rate and dimensional scaling regimes, we obtain asymptotic normality and invariance principles for dropout SGD and its averaged version. These results enable online inference, for which we introduce a fully recursive estimator of the long-run covariance matrix appearing in the limiting distributions. The proposed online confidence intervals with asymptotically correct coverage can be generalized to many other ML algorithms. Overall, viewing online learning algorithms as nonlinear time series provides a powerful toolkit for deriving statistical guarantees in modern ML, with implications for high-dimensional stochastic optimization and real-time uncertainty quantification.
Bio:Jiaqi Li is a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago. She obtained her PhD in Statistics from Washington University in St. Louis in 2024. Her research focuses on developing theoretical guarantees and statistical inference methods for machine learning algorithms. She also works on time series data, especially in the high- dimensional settings with complex temporal and cross-sectional dependency structures. She also
collaborates with neuroscientists on applications in fMRI and EEG data.
Hosted by: Statistics Department
Zoom link: https://ucsc.zoom.us/j/96647674332?pwd=[REDACTED]

AM Seminar: Are Graph Learning Methods Actually Learning?
2026-02-02 16:00 - 17:00 | CA
Presenter: Seshadhri Comandur, Professor of Computer Science, UCSC
Description: There has been a lot of literature on graph machine learning over the past few years, and a bewildering array of new methods. This talk is based on a series of results making a provocative argument. Maybe many graph machine learning methods are not really that effective, and the progress we are seeing is an artifact of experimental design and measurement. I will talk about some results showing that low-dimensional embeddings with dot product similarity (arguably the most common graph ML technique) cannot capture salient aspects of real-world graphs. Follow-up work demonstrates that simple benchmarks seem to outperform fancier methods, and that there are significant shortcomings in existing accuracy measurement.
Bio: C. Seshadhri (Sesh) is a professor of Computer Science at the University of California, Santa Cruz and an Amazon scholar. Prior to joining UCSC, he was a researcher at Sandia National Labs, Livermore in the Information Security Sciences department, during 2010-2014. His primary interest is the theoretical study of algorithms, especially those with a mix of graphs and randomization. By and large, Sesh works at the boundary of theoretical computer science (TCS) and data mining. His work spans many areas: sublinear algorithms, graph algorithms, graph modeling, scalable computation, and data mining. In the theory world, his work has resolved numerous open problems in monotonicity testing and graph property testing. A number of his papers in the interface of TCS and applied algorithms have received paper awards at KDD, WWW, ICDM, SDM, and WSDM. He received the 2019 SDM/IBM Early Career Award for Excellence in Data Analytics. Sesh got his Ph.D from Princeton University and spent two years as a postdoc in IBM Almaden Labs.
Hosted by: Ashesh Chattopadhyay, Applied Mathematics Department

Statistics Seminar: Statistical Inference for Multi-Modality Data in the AI Era
2026-02-04 12:00 - 13:00 | https://ucsc.zoom.us/j/91740050783?pwd=[REDACTED]
Presenter: Qi Xu, Postdoctoral Researcher, Department of Statistics & Data Science, Carnegie Mellon University
Description: Multi-modality data are increasingly common across science medicine and technology, such as imaging, text, sensors, and genomics. These modalities are often high dimensional or unstructured and naturally exhibit blockwise (nonmonotone) missingness where different samples observe different subsets of modalities. Such missingness creates a major obstacle for statistical analyses since classical methods either discard large portions of data or rely on strong modeling assumptions. Recent advances in AI make it possible to generate or predict unobserved modalities from observed ones, opening new opportunities for data integration. In this talk, I will focus on statistical inference for blockwise-missing multi-modality data, while rigorously incorporating modern AI tools. Rooted in semiparametric theory, there is a long-term open problem that theoretically optimal estimating function under non-monotone missingness is computationally intractable, even under the missing completely at random mechanism. I introduce a tractable approximation to the optimal estimating equation through a novel Restricted ANOVA hierarchY or RAY decomposition and its almost-eigen-operator property. This leads to a new class of estimators that leverage predictive or generative AI models to borrow information across datasets while remaining unbiased and asymptotically normal. Motivated by the property of the RAY estimator, we extend the RAY estimator to a class of unbiased, consistent, and computationally tractable estimators. The most efficient estimator in this class is then derived, named as Adaptive RAY estimator, which optimally integrating all available data and prediction from AI. Simulation studies and a single cell multi-omics application demonstrate that the proposed framework enables stable and efficient inference for complex multi modality data in the AI era. This is a joint work with Lorenzo Testa, Jing Lei and Kathryn Roeder, and the paper is available on arXiv: https://arxiv.org/abs/2509.24158
Bio: Qi Xu is a postdoctoral researcher in the Department of Statistics & Data Science at Carnegie Mellon University. His research interests lie broadly in statistics and machine learning, especially in data integration and AI for statistics, with their applications in genomics and mobile health. He received his Ph.D. from the Department of Statistics at University of California, Irvine, and the Master degree from University of Illinois Urbana Champaign, and the Bachelor degree (with honors) from Tongji University.
Hosted by: Statistics Department
Zoom link: https://ucsc.zoom.us/j/91740050783?pwd=[REDACTED]

GDAC Portfolio Workshop
2026-02-06 12:15 - 13:00 | Cultural Center – Merrill College, 641 Merrill Rd, Santa Cruz, 95064, United States
Workshop

Part of the GDA Conference on campus – come and learn best practices for creating a portfolio to use in the gaming industry!

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ECE Seminar: Integrated Micro- and Nanosystems for Biosensing, Neural Therapy, and Nanotoxicity
2026-02-09 10:40 - 23:45 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presenter: Dr. Ke Du, Associate Professor of Chemical and Environmental Engineering, University of California, Riverside
Description: Miniaturized micro- and nanofluidic systems, integrated with biochemistry, microscopy, nanomaterials, and computer vision algorithms, provide powerful platforms for diverse biomedical applications, including molecular diagnostics, biophysics, and optogenetics. In this presentation, we introduce a pneumatically controlled nano-sieve device with nanolithography-defined microstructures designed to enhance target capture efficiency in bodily fluids. This system incorporates sheath flow configurations, surface-enhanced Raman probes, and CRISPR reactions for the sensitive and multiplexed detection of drug-resistant bacteria in nanoconfined environments. We also highlight our recent advancements in implantable devices for adeno-associated virus (AAV) delivery and the treatment of neurological disorders in mouse models. These devices, fabricated via high-resolution 3D printing, utilize total internal reflection at the liquid–air–microstructure interface to efficiently stimulate neurons. Finally, we integrate experimental approaches with molecular dynamics simulations to study the interactions between arbitrary nanoparticles and living cells—advancing our understanding of nanotoxicity and guiding the design of next-generation drug delivery systems.
Bio: Dr. Ke Du is an Associate Professor of Chemical and Environmental Engineering at the University of California, Riverside. He established his independent research lab in 2018 following postdoctoral training with Richard Mathies at the University of California, Berkeley, and Holger Schmidt at the University of California, Santa Cruz. His research team focuses on molecular diagnostics for infectious diseases such as sepsis, in vivo bioimaging, and nanotoxicology. Dr. Du has received numerous honors, including the EIPBN Inaugural Early Career Award (2024) and the NIH Maximizing Investigators’ Research Award (2021). He was recognized as an Emerging Investigator by Lab on a Chip (2024) and Nanoscale (2025), and named a Global Rising Star in Sensing by ACS Sensors. His research is supported by federal agencies and industry partners, including NIH NIGMS, NIH NIAID, NSF CBET, NSF CMMI, USDA, DOE, the Burroughs Wellcome Fund, Mammoth Biosciences, and Biological Mimetics. Beyond his research activities, Dr. Du serves as an Early Career Editorial Advisory Board member for Biomicrofluidics (AIP Publishing) and Sensors and Actuators Reports (Elsevier).
Hosted by: Professor Soumya Bose, ECE Department
Zoom Link: https://ucsc.zoom.us/j/97975378707?pwd=[REDACTED]

CM Seminar - “The ‘Social’ Side of Social Virtual Reality”
2026-02-09 12:30 - 13:30 | Engineering 2, Engineering 2 1156 High Street, Santa Cruz, CA, 95064
Presented by: Bree McEwan
Description: One of the potential use cases of virtual reality is to create spaces where humans can interact with each other or virtual agents across distances. However, despite many of the technological challenges of social VR being solved, social VR does not see poised for widespread adoption. Multi-user social VR needs to be perceived not just as a technology to be solved but an emerging communication channel. Social science approaches, particularly from communication scholars, are needed to truly understand the way that humans engage with VR and each other in these new environments. McEwan’s talk will outline a program of research using qualitative and quantitative approaches to understand communication processes, effects, and user perceptions of VR design to deepen our understanding of how people engage with environments and each other in social VR.
Bio: Bree McEwan is a Professor in the Institute of Communication, Culture, Information and Technology, an associate director of the Data Sciences Institute, and a faculty affiliate of the Schwartz Reisman Institute for Technology and Society at the University of Toronto. She is a co- organizer and founder of the Questioning Reality conference, a social VR research incubator. McEwan authored Navigating New Media Networks and co-authored Interpersonal Encounters. She directs the McEwan Mediated Communication Lab which researches the intersection of technology and social interaction. McEwan has published on relational maintenance on social network sites, perceived social affordances of communication channels, linguistic patterns in online communities, and the diffusion of information through social media. In addition, McEwan has metascience interests focused on transparency and replication in the social sciences. Current studies of the McMC Lab focus on affordances of social virtual environments, cognition and heuristics related to learning in VR spaces, and nonverbal communication patterns of avatars and agents.
Hosted by: Professor Katherine Isbister
When: Monday, February 9, 2026 from 12:30PM to 1:30PM
Location:
IN-PERSON @ UCSC Main Campus, E2-280.
Viewing room @ SVC 3212.
LUNCH WILL BE PROVIDED AT BOTH LOCATIONS! Faculty and students are highly encouraged to attend.
Zoom info:
https://ucsc.zoom.us/j/91469785121?pwd=[REDACTED]
Meeting ID: 914 6978 5121
Passcode: [REDACTED]

AM Seminar: Data Driven Modeling for Scientific Discovery and Digital Twins
2026-02-09 16:00 - 17:00 | CA
Presenter: Dongbin Xiu, Professor, Ohio State University
Description:We present a data-driven modeling framework for scientific discovery, termed Flow Map Learning (FML). This framework enables the construction of accurate predictive models for complex systems that are not amenable to traditional modeling approaches. By leveraging data and the expressiveness of deep neural networks (DNNs), FML facilitates long-term system modeling and prediction even when governing equations are unavailable. FML is particularly powerful in the context of Digital Twins, an emerging concept in digital transformation. With sufficient offline learning, FML enables the construction of simulation models for key quantities of interest (QoIs) in complex Digital Twins, when direct mathematical modeling of the QoIs is infeasible. During the online execution of a Digital Twin, the learned FML model can simulate the QoIs without reverting to the computationally intensive Digital Twin simulation model. As a result, FML serves as an enabling methodology for real-time control and optimization for complex systems.
Bio: Dongbin Xiu received his Ph.D degree from the Division of Applied Mathematics of Brown University in 2004. He joined the Department of Mathematics of Purdue University in 2005 and moved to the University of Utah in 2013. In 2016, He joined The Ohio State University as Professor of Mathematics and Ohio Eminent Scholar. He received NSF CAREER award in 2007 and was elected to SIAM Fellow in 2023. He is currently the Editor-in-Chief of the Journal of Computational Physics and the founding Editor-in-Chief of Journal of Machine Learning for Modeling and Computing (JMLMC). His current research focuses on developing efficient numerical methods for scientific machine learning, data driven discovery and digital twins.
Hosted by: Daniele Venturi, Applied Mathematics

EOP Students - "Words That Impress: Creating a Great Résumé & Cover Letter"
2026-02-11 15:00 - 16:00 | Career Success Student Lounge (125 Hahn), 1156 High Street, Santa Cruz, CA, 95064, United States
Crafting a fantastic Resume and Cover Letter are the key to getting an interview and landing a job! Join us for this informative workshop that will cover best practices for resume and cover letter development. You’ll gain understanding about the perfect format, navigating AI filters, and how to write captivating resume bullet points and engaging cover letter paragraphs. The presentation will be 30 minutes, followed by 30 minutes of optional worktime here in our office with coaches to give you brief input.
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.
