BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Events - ECPv6.16.5.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Events
X-ORIGINAL-URL:https://live-events-ucsc.pantheonsite.io
X-WR-CALDESC:Events for Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20270314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20271107T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251023T134000
DTEND;TZID=America/Los_Angeles:20251023T150000
DTSTAMP:20251105T190727Z
CREATED:20251022T204629Z
LAST-MODIFIED:20251105T190727Z
UID:10004986-1761226800-1761231600@live-events-ucsc.pantheonsite.io
SUMMARY:Behavioral\, Econometrics and Theory Seminar Series Presents: Kevin Chen
DESCRIPTION:Economics Behavioral\, Econometrics\, & Theory Seminar\nDate: Thursday\, October 23\, 2025\nTime: 1:40-3:00 p.m.\nLocation: Engineering 2\, Rm 499\n\n \n\nSpeaker: Kevin Chen \nTitle:  Assistant Professor of Economics \nAffiliation: Stanford University\nHost: Michael Leung\n \nSeminar title: Compound Selection Decisions: An Almost SURE Approach \n \nABSTRACT:  This paper proposes methods for producing compound selection decisions in a Gaussian sequence model. Given unknown\, fixed parameters µ_{1:n} and known σ_{1:n} with observations Yᵢ ∼ 𝒩(μᵢ\, σᵢ²)\, the aim is to select a subset of units S to maximize utility Σ_{i∈S}(μᵢ − Kᵢ) for known costs Kᵢ. Inspired by Stein’s unbiased risk estimate (SURE)\, we introduce an almost unbiased estimator\, ASSURE\, for the expected utility of a proposed decision rule. ASSURE allows a user to choose a welfare-maximizing rule from a pre-specified class by optimizing the estimated welfare\, thereby producing selection decisions that borrow strength across noisy estimates. We show that ASSURE yields decision rules that are asymptotically no worse than the optimal but infeasible rule in the pre-specified class. We apply ASSURE to p-value decision procedures in A/B testing\, selecting Census tracts for economic opportunity\, and identifying discriminating firms.
URL:https://live-events-ucsc.pantheonsite.io/event/behavioral-econometrics-and-theory-seminar-series-presents-kevin-chen/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/10/ChenKevin.jpeg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251028T134000
DTEND;TZID=America/Los_Angeles:20251028T150000
DTSTAMP:20251105T190553Z
CREATED:20251022T210813Z
LAST-MODIFIED:20251105T190553Z
UID:10004988-1761658800-1761663600@live-events-ucsc.pantheonsite.io
SUMMARY:Macroeconomics & International Finance Seminar Series Presents: Zhiguo He
DESCRIPTION:Macroeconomics and International Finance Seminar\nDate: Tuesday\, October 28\, 2025\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Zhiguo He\nTitle: James Irvin Miller Professor of Finance\nAffiliation: Stanford University \nHost: Michael Leung \n \nSeminar title: Household Migration and Collateral Constraint: Cash-based Housing Resettlement in China\n \nABSTRACT:   Collateral constraints reduce household migration to expensive locations by restricting financing for home purchases. This endogenous location choice can amplify the impact of relaxing borrowing constraints. Using China’s cash-based shantytown renovation program (2015-2018) as a natural experiment\, we provide evidence that cash resettlement– by converting illiquid shanty houses into cash– facilitated household location upgrading and raised house prices in more expensive locations. A dynamic spatial model with collateral constraints confirms that endogenous location upgrading amplified the effect of cash transfer\, raising lifetime housing expenditures by nearly 50%\, and house price growth in low-tier cities by 9% in 2016-2020.
URL:https://live-events-ucsc.pantheonsite.io/event/macroeconomics-international-finance-seminar-series-presents-zhiguo-he/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/webp:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/10/Zhiguo-He.webp
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251030T134000
DTEND;TZID=America/Los_Angeles:20251030T150000
DTSTAMP:20251105T192322Z
CREATED:20251024T204207Z
LAST-MODIFIED:20251105T192322Z
UID:10005006-1761831600-1761836400@live-events-ucsc.pantheonsite.io
SUMMARY:Applied Microeconomics and Trade Seminar Series presents: Shanjun Li
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Thursday\, October 30th\, 2025\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Shanjun Li\nPersonal Webpage \nTitle: Steven and Roberta Denning Professor of Global Sustainability \nAffiliation: Stanford University \nHost: Peter Christensen \n \nSeminar title: Range Anxiety\n \nABSTRACT:   Range anxiety\, the fear of depleting battery before reaching a charging station\, is often cited as a major barrier to electric vehicle (EV) adoption\, yet there has been limited formal economic analysis to quantify its importance and understand the policy implications. We develop a continuous-time dynamic model of EV usage and charging decisions to quantify range anxiety as the utility loss from feasible yet unrealized trips due to perceived range constraints. Using high-frequency data of 188\,000 EV trips and 30\,000 charging events among 8\,000 EVs in Shanghai\, we recover model parameters governing consumer driving and charging decisions. The estimates imply that\, across EV models with varying driving ranges\, average range anxiety was about $1\,900 in 2021 but declined to $1\,200 in 2024\, driven by improvements in charging infrastructure and\, especially\, in creases in driving range. Policy simulations underscore the importance of coordinating investments in battery capacity and charging infrastructure to address range anxiety: relative to socially optimal levels\, Shanghai’s EV market has under-invested in driving range while over-investing in charging infrastructure.
URL:https://live-events-ucsc.pantheonsite.io/event/applied-microeconomics-and-trade-seminar-series-presents-shanjun-li/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/10/sl2448-shanjun-li.jpg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251106T134000
DTEND;TZID=America/Los_Angeles:20251106T150000
DTSTAMP:20251120T172052Z
CREATED:20251105T202234Z
LAST-MODIFIED:20251120T172052Z
UID:10005099-1762436400-1762441200@live-events-ucsc.pantheonsite.io
SUMMARY:Applied Microeconomics and Trade Seminar Series presents: Matt Pecenco
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Thursday\, November 6\, 2025\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Matt Pecenco\nTitle: Orlando Bravo Assistant Professor of Economics \nAffiliation: Brown University \nHost: Ariel Zucker \n \nSeminar title: Conviction\, Incarceration\, and Policy Effects in the Criminal Justice System\n \nABSTRACT:   The criminal justice system affects millions of Americans through criminal convictions and incarceration. In this paper\, we introduce a new method for credibly estimating the effects of both conviction and incarceration using randomly assigned judges as instruments for treatment. Misdemeanor convictions\, especially for defendants with a shorter criminal record\, cause an increase in the number of new offenses committed over the following five years. Incarceration on more serious felony charges\, in contrast\, reduces recidivism during the period of incapacitation\, but has no effect after release. Our method allows the researcher to isolate specific treatment effects of interest as well as estimate the effect of broader policies; we find that courts could reduce crime by dismissing marginal charges against defendants accused of misdemeanors\, with larger reductions among first-time defendants and those facing more serious charges.
URL:https://live-events-ucsc.pantheonsite.io/event/applied-microeconomics-and-trade-seminar-series-presents-matt-pecenco/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/11/PecencoMatt.jpg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251113T134000
DTEND;TZID=America/Los_Angeles:20251113T150000
DTSTAMP:20251120T172017Z
CREATED:20251105T211520Z
LAST-MODIFIED:20251120T172017Z
UID:10005100-1763041200-1763046000@live-events-ucsc.pantheonsite.io
SUMMARY:Applied Microeconomics and Trade Seminar Series presents: Giovanni Peri
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Thursday\, November 13\, 2025\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Giovanni Peri\nTitle: C. Bryan Cameron Distinguished Professor in International Economics \nAffiliation: UC Davis\nHost: Gueyon Kim\n \nSeminar title: How the1942 Japanese Exclusion Impacted U.S. Agriculture\n \nABSTRACT:  In the early 1940s\, Japanese American farmers and farm workers represented an important part of agriculture-specific human capital in the United States. In 1942 all those living in the “exclusion zone” along the WestCoastwereforcefully relocated to internment camps and most of them never returned to farming. Using county-level panel data from historical agricultural censuses and a triple-difference (DDD) estimation approach we find that\, by 1960\, counties in the exclusion zone experienced 12% lower cumulative growth in farm value for each percentage point loss of their 1940 share of Japanese farm workers\, relative to counties outside the exclusion zone. Farm revenues\, farm productivity\, adoption of high-value crops\, mechanization\, and farm wages were also correspondingly lower. Taken together\, these findings are consistent with Japanese farmers representing hard-to-replace human capital\, rather than replaceable labor\, in US agriculture.
URL:https://live-events-ucsc.pantheonsite.io/event/applied-microeconomics-and-trade-seminar-series-presents-giovanni-peri/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/11/PeriGiovanni-1.jpg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251118T134000
DTEND;TZID=America/Los_Angeles:20251118T150000
DTSTAMP:20251120T171912Z
CREATED:20251107T004436Z
LAST-MODIFIED:20251120T171912Z
UID:10005109-1763473200-1763478000@live-events-ucsc.pantheonsite.io
SUMMARY:Macroeconomics & International Finance Seminar Series Presents: Yuriy Gorodnichenko
DESCRIPTION:Macroeconomics and International Finance Seminar\nDate: Tuesday\, November 18\, 2025\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Yuriy Gorodnichenko\nTitle: Quantedge Presidential Professor of Economics\nAffiliation: UC Berkeley\nHost: Pascal Michaillat\n \nSeminar title:  How costly are business cycle volatility and inflation? A Vox Populi approach\n \nABSTRACT:  Using surveys of households across thirteen countries\, we study how much individuals would be willing to pay to eliminate business cycles. These direct estimates are much higher than traditional measures following Lucas (2003): on average\, households would be prepared to sacrifice around 5-6% of their lifetime consumption eliminate business cycle fluctuations. A similar result holds for inflation: to bring inflation to their desired rate\, individuals would be willing to sacrifice around 5% of their consumption. Willingness to pay to eliminate business cycles and inflation is generally higher for those whose consumption is more pro-cyclical\, those who are more uncertain about the economic outlook\, and those who live in countries with greater historical volatility. 
URL:https://live-events-ucsc.pantheonsite.io/event/macroeconomics-international-finance-seminar-series-presents-yuriy-gorodnichenko/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/11/Yuriy-Gorodnichenko.jpg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251125T134000
DTEND;TZID=America/Los_Angeles:20251125T150000
DTSTAMP:20251120T174503Z
CREATED:20251108T002503Z
LAST-MODIFIED:20251120T174503Z
UID:10005117-1764078000-1764082800@live-events-ucsc.pantheonsite.io
SUMMARY:Macroeconomics & International Finance Seminar Series Presents: Helen Popper
DESCRIPTION:Macroeconomics and International Finance Seminar\nDate: Tuesday\, November 25\, 2025\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Helen Popper\nTitle: Professor of Economics\nAffiliation: Santa Clara University \nHost: Galina Hale\n \nSeminar title:  Artificial Intelligence and Macroeconomic Dynamics: Growth\, Pricing\, and Distribution\n \nABSTRACT:  This paper builds a simple general equilibrium model in which an AI producer is a monopolist who both learns by doing and uses AI recursively as an input. These mechanisms link today’s scale to tomorrow’s costs\, so pricing is dynamic: the firm sets a price below the static monopoly benchmark to expand capacity and speed learning. Final goods are produced by monopolistic competitors with constant returns to scale each period. We first use Cobb–Douglas technologies to solve for a generalized balanced growth path that pins down the condition for stable\, nonexplosive growth. On this path\, AI output grows faster than final output\, the relative price of AI falls persistently\, real wages rise with overall output\, and the specialized–to–nonspecialized wage ratio is flat. We then analyze CES versions of both sectors and derive a closed form effective demand elasticity for AI that combines input substitution in production with final-goods market substitution across varieties. Finally\, simulations link adoption and distribution to elasticities\, and they allow us to explore the dynamics. When final-goods inputs are complements\, adoption is learning-first and capital-light before scaling; when they are substitutes\, adoption is scale-first and the two-phase pattern attenuates. On the distribution side\, the specialized–to–nonspecialized wage premium is lowest with complements and rises with substitutes. Greater substitutability in AI production amplifies these patterns without changing their sign.
URL:https://live-events-ucsc.pantheonsite.io/event/macroeconomics-international-finance-seminar-series-presents-helen-popper/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/11/popperhelen.jpeg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251203T115000
DTEND;TZID=America/Los_Angeles:20251203T131000
DTSTAMP:20251125T164318Z
CREATED:20251108T002424Z
LAST-MODIFIED:20251125T164318Z
UID:10005121-1764762600-1764767400@live-events-ucsc.pantheonsite.io
SUMMARY:Applied Microeconomics and Trade Seminar Series presents: Matt Weinberg
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Wednesday\, December 3\, 2025\nTime: 11:50am – 1:10 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Matt Weinberg \nTitle: Professor of Economics \nAffiliation: Ohio State University\nHost: Jon Robinson\n \nSeminar title: Oligopsony and Collective Bargaining: Evidence from K-12 Teachers \n\nABSTRACT:  Employers facing limited labor market competition may suppress wages below socially optimal levels. Unions can counteract this wage suppression through collective bargaining\, though the may also push wages above the socially optimal level. To assess these forces\, we estimate a structural model of labor supply\, labor demand\, and Nashin-Nash bargaining over wages between teacher unions and school districts in Pennsylvania’s K-12 public school system from 2013 to 2020. Using the estimated parameters\, we compare negotiated equilibrium wages and employment to the pure oligopsony scenario and the social planner scenario. On average\, pure oligopsony reduces wages 16 percent below the social optimum\, while collective bargaining raises wages by 9 percent above the optimum. This average masks substantial district-level heterogeneity driven by variation in bargaining power. Twenty-seven percent of schools have negotiated salaries below the social optimum due to cross-district externalities\, where high salaries at one school lead to hiring reductions\, which increase labor supply in competing districts. 
URL:https://live-events-ucsc.pantheonsite.io/event/applied-microeconomics-and-trade-seminar-series-presents-matt-weinberg/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251204T134000
DTEND;TZID=America/Los_Angeles:20251204T150000
DTSTAMP:20251120T170815Z
CREATED:20251108T001824Z
LAST-MODIFIED:20251120T170815Z
UID:10005120-1764855600-1764860400@live-events-ucsc.pantheonsite.io
SUMMARY:Behavioral\, Econometrics and Theory Seminar Series Presents: Jacopo Magnani
DESCRIPTION:Economics Behavioral\, Econometrics\, & Theory Seminar\nDate: Thursday\, December 4\, 2025\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Jacopo Magnani \nTitle:  Associate Professor of Economics \nAffiliation: Norwegian University of Science and Technology\, visiting Caltech\nHost: Kristian Lopez Vargas\n \nSeminar title: Behavioral Limits to Complete Markets\n \nABSTRACT:  Standard economic theory predicts that individuals should prefer complete markets to incomplete markets\, as the former allow state-contingent claims for every possible outcome. Yet real-world markets remain incomplete\, and the demand-side origins of the phenomenon are poorly understood. We develop an experimental framework to examine whether investors may themselves prefer incomplete markets\, and highlight two potential mechanisms: preference instability\, which exposes agents to greater regret or temptation in complete markets\, and complexity costs\, which arise because higher dimensionality increases cognitive effort and errors. In our experiment\, participants consistently reveal a preference for in complete markets\, contradicting the rational benchmark. Comparing homegrown and induced-preference treatments\, we find no evidence that this behavior is driven by preference instability. Instead\, utility losses\, response times\, and subjective ratings indicate that complexity costs drive the preference for incompleteness. Structural estimation confirms that complete markets are several times more complex than incomplete ones\, providing a behavioral foundation for market incompleteness. 
URL:https://live-events-ucsc.pantheonsite.io/event/behavioral-econometrics-and-theory-seminar-series-presents-jacopo-magnani/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/11/jacopo.jpg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260105T160000
DTEND;TZID=America/Los_Angeles:20260105T170000
DTSTAMP:20251218T002005Z
CREATED:20251217T182411Z
LAST-MODIFIED:20251218T002005Z
UID:10005858-1767628800-1767632400@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar with Dr. Truong Vu
DESCRIPTION:Presenter: Dr. Truong Vu\, IPAM and MSU \nDescription: 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. \nBio: 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. \nHosted by: Applied Mathematics 
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/12/txvu.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260112T160000
DTEND;TZID=America/Los_Angeles:20260112T170000
DTSTAMP:20260112T164010Z
CREATED:20260112T164010Z
LAST-MODIFIED:20260112T164010Z
UID:10008343-1768233600-1768237200@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Science in the Age of Foundation Models
DESCRIPTION:Presenter: Dr. Danielle Robinson\, AWS AI \nDescription: 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. \n\n\n\n\n\n\n\n\n\nBio: 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. \n\n\n\nHosted by: Applied Mathematics\n\n\n\nLink: https://ucsc.zoom.us/j/96136632376?pwd=yb27lop8mnhnsairAPgezmVJZzFb74.1.
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-science-in-the-age-of-foundation-models/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option-1.jpg
LOCATION: https://ucsc.zoom.us/j/96136632376?pwd=yb27lop8mnhnsairAPgezmVJZzFb74.1.
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260112T160000
DTEND;TZID=America/Los_Angeles:20260112T170000
DTSTAMP:20251219T164251Z
CREATED:20251219T164251Z
LAST-MODIFIED:20251219T164251Z
UID:10007701-1768233600-1768237200@live-events-ucsc.pantheonsite.io
SUMMARY:Kathleen Schmidt: Sequential Experimental Design for Materials Strength Model Calibration
DESCRIPTION:Presenter: Katie Schmidt\, UQ & Optimization Group Leader\, Lawrence Livermore National Laboratory \nDescription: 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. \nLLNL-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. \nBio: 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. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/kathleen-schmidt-sequential-experimental-design-for-materials-strength-model-calibration/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2025/12/ph.d.-presentation-graphic-option-1-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260113T134000
DTEND;TZID=America/Los_Angeles:20260113T150000
DTSTAMP:20251219T220255Z
CREATED:20251211T224403Z
LAST-MODIFIED:20251219T220255Z
UID:10005826-1768311600-1768316400@live-events-ucsc.pantheonsite.io
SUMMARY:Macroeconomics & International Finance Seminar Series Presents: Dean Corbae
DESCRIPTION:Macroeconomics and International Finance Seminar\nDate: Tuesday\, January 13\, 2026\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Dean Corbae\nTitle: William Sellery Trukenbrod Chair in Finance\nAffiliation: University of Wisconsin – Madison\nHost: Grace Gu Steadmon\n \nSeminar title:  A Quantitative Model of Bank Merger Dynamics\n \n\nABSTRACT: \nWe 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?
URL:https://live-events-ucsc.pantheonsite.io/event/macroeconomics-international-finance-seminar-series-presents-dean-corbae/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260115T014000
DTEND;TZID=America/Los_Angeles:20260115T014000
DTSTAMP:20251219T220029Z
CREATED:20251211T212236Z
LAST-MODIFIED:20251219T220029Z
UID:10005825-1768441200-1768441200@live-events-ucsc.pantheonsite.io
SUMMARY:Applied Microeconomics and Trade Seminar Series Presents: Olivia Bordeu
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Thursday\, January 15\, 2026\nTime: 1:40 – 3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Olivia Bordeu \nTitle: Assistant Professor of Economics \nAffiliation: University of California\, Berkeley  \nHost: Jeremy West \nSeminar title: Bank Branches and the Allocation of Capital across Cities\n\nABSTRACT: 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.
URL:https://live-events-ucsc.pantheonsite.io/event/applied-microeconomics-and-trade-seminar-series-presents-olivia-bordeu/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260120T134000
DTEND;TZID=America/Los_Angeles:20260120T150000
DTSTAMP:20260108T184635Z
CREATED:20251211T224823Z
LAST-MODIFIED:20260108T184635Z
UID:10005827-1768916400-1768921200@live-events-ucsc.pantheonsite.io
SUMMARY:Behavioral\, Econometrics and Theory Seminar Series Presents: Roberto Corrao
DESCRIPTION:Economics Behavioral\, Econometrics\, & Theory Seminar\nDate: Tuesday\, January 20\, 2026\nTime: 1:40-3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Roberto Corrao\nTitle:  Assistant Professor of Economics \nAffiliation:  Stanford University\nHost: Gerelt Tserenjigmid\n \nSeminar title: Contractibility Design\n \nABSTRACT: \nWe introduce a model of incentive contracting in which the principal\, in addition to\nwriting contracts\, must engage in contractibility design: creating an evidence structure\nthat allows them to prove when the agent has breached the contract. Designing an\nevidence structure entails both (i) front-end costs borne ex ante\, such as those of\ndrafting contracts\, and (ii) back-end costs borne ex post\, such as those of generating\nevidence. We find that\, under even small front-end costs\, optimal contracts are coarse\,\nspecifying finitely many contingencies out of a continuum of possibilities. In contrast\,\nunder even large back-end costs\, optimal contracts are complete. Applied to the design\nof procurement contracts\, our results rationalize: (i) the discreteness of contracts\, (ii)\nthe presence of similarly vague contracts in low-stakes and high-stakes settings\, and\n(iii) the discontinuous adjustment of contracts to changes in the economic environment.
URL:https://live-events-ucsc.pantheonsite.io/event/behavioral-econometrics-and-theory-seminar-series-presents-roberto-corrao/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260122T014000
DTEND;TZID=America/Los_Angeles:20260122T014000
DTSTAMP:20260108T184752Z
CREATED:20251211T230012Z
LAST-MODIFIED:20260108T184752Z
UID:10005828-1769046000-1769046000@live-events-ucsc.pantheonsite.io
SUMMARY:Applied Microeconomics and Trade Seminar Series Presents: Guo Xu
DESCRIPTION:Applied Microeconomics and Trade Seminar\nDate: Thursday\, January 22\, 2026\nTime: 1:40 – 3:00 p.m.\nLocation: E2-499\n\n \n\nSpeaker: Guo Xu\nTitle: Associate Professor of Economics \nAffiliation: University of California\, Berkeley  \nHost: Ajay Shenoy \n  \nSeminar title: Personnel is Policy: Delegation and Political Misalignment in the Rulemaking Process\n\nABSTRACT: 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.
URL:https://live-events-ucsc.pantheonsite.io/event/applied-microeconomics-and-trade-seminar-series-presents-guo-xu/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260123T120000
DTEND;TZID=America/Los_Angeles:20260123T130000
DTSTAMP:20260122T174111Z
CREATED:20260120T214846Z
LAST-MODIFIED:20260122T174111Z
UID:10008680-1769169600-1769173200@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Heterogeneous Statistical Transfer Learning
DESCRIPTION:Presenter: Subhadeep Paul\, Associate Professor\, Ohio State University \nDescription: 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. \nBio: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. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/94465292273?pwd=bQ6MCX0OHYxHqgqNwbEYfgbKWqgNVy.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-heterogeneous-statistical-transfer-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/option-3-1.png
LOCATION:https://ucsc.zoom.us/j/94465292273?pwd=bQ6MCX0OHYxHqgqNwbEYfgbKWqgNVy.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260126T120000
DTEND;TZID=America/Los_Angeles:20260126T130000
DTSTAMP:20260121T182735Z
CREATED:20260121T182735Z
LAST-MODIFIED:20260121T182735Z
UID:10009084-1769428800-1769432400@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Boosting Biomedical Imaging Analysis via Distributed Functional Regression and Synthetic Surrogates
DESCRIPTION:Presenter: Guannan Wang\, Associate Professor\, The College of William & Mary \nDescription: 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\nbe 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. \nBio: 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. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/92479478035?pwd=S6b9SNtCorApA04sISbDwWqaF3wyPZ.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-boosting-biomedical-imaging-analysis-via-distributed-functional-regression-and-synthetic-surrogates/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/option-3-2.png
LOCATION:https://ucsc.zoom.us/j/92479478035?pwd=S6b9SNtCorApA04sISbDwWqaF3wyPZ.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260126T160000
DTEND;TZID=America/Los_Angeles:20260126T170000
DTSTAMP:20260120T184604Z
CREATED:20260120T184336Z
LAST-MODIFIED:20260120T184604Z
UID:10008394-1769443200-1769446800@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Probing Forced Responses and Causality in Data-Driven Climate Emulators: Conceptual Limitations and the Role of Reduced-Order Models
DESCRIPTION:Presenter: Fabrizio Falasca\, New York University \nDescription: 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. \nBio: 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. \n\n\n\n\n\nHosted by: Applied Mathematics \nZoom Link: https://ucsc.zoom.us/j/97450297092?pwd=Bp4GIgR8dAuBeCd1Sz9vXo8unkYWQW.1
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-probing-forced-responses-and-causality-in-data-driven-climate-emulators-conceptual-limitations-and-the-role-of-reduced-order-models/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option2.jpg
LOCATION: https://ucsc.zoom.us/j/97450297092?pwd=Bp4GIgR8dAuBeCd1Sz9vXo8unkYWQW.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260128T120000
DTEND;TZID=America/Los_Angeles:20260128T130000
DTSTAMP:20260128T171042Z
CREATED:20260121T235125Z
LAST-MODIFIED:20260128T171042Z
UID:10009090-1769601600-1769605200@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar:  Inferring Unobserved Trajectories from Multiple Temporal Snapshots
DESCRIPTION:Presenter: Yunyi Shen\, Ph.D. Candidate\, Department of Electrical Engineering and Computer Science\, Massachusetts Institute of Technology \n\nDescription: 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. \nBio: 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. \nHosted by: Statistics Department  \nZoom link: https://ucsc.zoom.us/j/93769232971?pwd=msPkbjtoK3LiI9qHjLT1bv8idV23qU.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-inferring-unobserved-trajectories-from-multiple-temporal-snapshots/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option2-1.jpg
LOCATION:https://ucsc.zoom.us/j/93769232971?pwd=msPkbjtoK3LiI9qHjLT1bv8idV23qU.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260202T120000
DTEND;TZID=America/Los_Angeles:20260202T130000
DTSTAMP:20260128T171007Z
CREATED:20260122T191932Z
LAST-MODIFIED:20260128T171007Z
UID:10009093-1770033600-1770037200@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Mathematical Foundations for Machine Learning from a Nonlinear Time Series Perspective
DESCRIPTION:Presenter: Jiaqi Li\, William H. Kruskal Instructor\, University of Chicago \nDescription: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. \nBio: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\ncollaborates with neuroscientists on applications in fMRI and EEG data. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/96647674332?pwd=rCHfeGpKslaGS5iIPP5Jh29mQiMJID.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-mathematical-foundations-for-machine-learning-from-a-nonlinear-time-series-perspective/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option-1-1.jpg
LOCATION:https://ucsc.zoom.us/j/96647674332?pwd=rCHfeGpKslaGS5iIPP5Jh29mQiMJID.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260202T160000
DTEND;TZID=America/Los_Angeles:20260202T170000
DTSTAMP:20260128T184233Z
CREATED:20260128T184233Z
LAST-MODIFIED:20260128T184233Z
UID:10009126-1770048000-1770051600@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Are Graph Learning Methods Actually Learning?
DESCRIPTION:Presenter: Seshadhri Comandur\, Professor of Computer Science\, UCSC \nDescription: 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. \nBio: 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. \nHosted by: Ashesh Chattopadhyay\, Applied Mathematics Department
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-are-graph-learning-methods-actually-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/sesh.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260204T120000
DTEND;TZID=America/Los_Angeles:20260204T130000
DTSTAMP:20260128T170858Z
CREATED:20260128T170858Z
LAST-MODIFIED:20260128T170858Z
UID:10009124-1770206400-1770210000@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Statistical Inference for Multi-Modality Data in the AI Era
DESCRIPTION:Presenter: Qi Xu\, Postdoctoral Researcher\, Department of Statistics & Data Science\, Carnegie Mellon University \nDescription: 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 \nBio: 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. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/91740050783?pwd=joK9hfwvM7FZ48acaiow8OY4ZlBDXA.1
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-statistical-inference-for-multi-modality-data-in-the-ai-era/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/Screenshot-2026-01-28-at-9.08.20-AM.png
LOCATION:https://ucsc.zoom.us/j/91740050783?pwd=joK9hfwvM7FZ48acaiow8OY4ZlBDXA.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T160000
DTEND;TZID=America/Los_Angeles:20260209T170000
DTSTAMP:20260114T182750Z
CREATED:20260114T182449Z
LAST-MODIFIED:20260114T182750Z
UID:10008393-1770652800-1770656400@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Data Driven Modeling for Scientific Discovery and Digital Twins
DESCRIPTION:Presenter: Dongbin Xiu\, Professor\, Ohio State University \nDescription: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. \nBio: 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. \nHosted by: Daniele Venturi\, Applied Mathematics
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-data-driven-modeling-for-scientific-discovery-and-digital-twins/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/option-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260223T160000
DTEND;TZID=America/Los_Angeles:20260223T170000
DTSTAMP:20260126T202042Z
CREATED:20260126T202042Z
LAST-MODIFIED:20260126T202042Z
UID:10009108-1771862400-1771866000@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Rotated Mean-Field Variational Inference and Iterative Gaussianization
DESCRIPTION:Presenter: Sifan Liu\, Assistant Professor\, Department of Statistical Science\, Duke University \nDescription:Mean-field variational inference (MFVI) approximates a target distribution with a product distribution in the standard coordinate system\, offering a scalable approach to Bayesian inference but often severely underestimating uncertainty due to neglected dependence. We show that MFVI can be greatly improved when performed along carefully chosen principal component axes rather than the standard coordinates. The principal components are obtained from a cross-covariance matrix of the target’s score function and identify orthogonal directions that capture the dominant discrepancies between the target distribution and a Gaussian reference. Performing MFVI in a rotated system defines a rotation followed by a coordinatewise transformation that moves the target closer to Gaussian. Iterating this procedure yields a sequence of transformations that progressively Gaussianize the target. The resulting algorithm provides a computationally efficient construction of normalizing flows\, requiring only MFVI sub-problems and avoiding large-scale optimization. In posterior sampling tasks\, we demonstrate that the proposed method greatly outperforms standard MFVI while achieving accuracy comparable to normalizing flows at a much lower computational cost. \nBio: Sifan Liu is an Assistant Professor in the Department of Statistical Science at Duke University. She was previously a research scientist at the Flatiron Institute and received her Ph.D. in Statistics from Stanford University. Her research interests include sampling\, generative modeling\, and selective inference. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-rotated-mean-field-variational-inference-and-iterative-gaussianization/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/ph.d.-presentation-graphic-option-1-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260223T160000
DTEND;TZID=America/Los_Angeles:20260223T170000
DTSTAMP:20260219T193254Z
CREATED:20260114T175234Z
LAST-MODIFIED:20260219T193254Z
UID:10008383-1771862400-1771866000@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: Multiscale Modeling of Cellular Membranes and Oncogenic Proteins
DESCRIPTION:Presenter: Liam Stanton\, Professor\, San Jose State University \nDescription: In this talk\, I will present a multiscale model for cellular membranes\, which is trained on molecular dynamics simulations. The model is constructed within the formalism of dynamic density functional theory and can be extended to include features such as the presence of proteins and membrane deformations. This new framework has enabled simulations that can access length-scales on the order of microns and time-scales on the order of seconds\, all while maintaining near fidelity to the underlying molecular interactions. Such scales are significant for accessing biological processes associated with signaling pathways within cells and experimentally relevant regimes. As applications\, we consider the cellular interactions of two membrane proteins of biological interest: G protein-coupled receptors (GPCRs) and RAS-RAF complexes\, the latter being implicated in roughly 30% of human cancers. \nBio: Dr. Stanton received his PhD in Applied Mathematics from Northwestern University in 2009. He went on to do a postdoc at Lawrence Livermore National Laboratory (LLNL)\, where he later became a staff scientist at the Center for Applied Scientific Computing. In 2018\, he joined the faculty at San Jose State University in the Department of Mathematics and Statistics\, where he is now an associate professor and a recent recipient of the Dean’s Scholar Award in Research Excellence. Dr. Stanton’s current research interests are in the multiscale modeling of non-equilibrium\, many-body systems. In particular\, he focuses on areas such as fusion energy\, biophysical systems and statistical mechanics. \nHosted by: Applied Mathematics
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-multiscale-modeling-of-cellular-membranes-and-oncogenic-proteins/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/01/Liam-Stanton-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260225T150000
DTEND;TZID=America/Los_Angeles:20260225T170000
DTSTAMP:20260218T010402Z
CREATED:20260211T203445Z
LAST-MODIFIED:20260218T010402Z
UID:10009206-1772031600-1772038800@live-events-ucsc.pantheonsite.io
SUMMARY:February 25\, 2026 | Works-in-Progress with Geoffrey Bowker
DESCRIPTION:Wednesday\, February 25\, 2026 \n3:00 – 5:00 PM \nHumanities 1\, Room 210 or Zoom (Registration) \nJoin SJRC scholars in Humanities 1\, room 210 or on Zoom for an open discussion of works-in-progress! This is a wonderful chance to engage with one another’s ideas\, and support our own internal work. \nAt this session\, we will hear from Geoffrey Bowker\, Emeritus Professor in Irvine and Science & Justice Advisor about works-in-progress and ongoing work on the death of infrastructure\, AI\, and underwater network cables and his collaborative comic book on Actor Network Theory. SJRC members Warren Sack and Dimitris Papadopolous will act as “warm up” discussants. \nContact Colleen Stone (colleen@ucsc.edu) or Maria Puig de la Bellacasa (puig@ucsc.edu) for the readings\, including a new comic book on the graveyard of machines! \nRegister for Zoom here. \nGeoffrey C. Bowker is Emeritus Professor at the School of Information and Computer Science\, University of California at Irvine\, where he directed a laboratory for Values in the Design of Information Systems and Technology. He was also Professor of and Senior Scholar in Cyberscholarship at the University of Pittsburgh School\, and Executive Director\, Center for Science\, Technology and Society\, Santa Clara. He was awarded the prestigious 4S Bernal Prize in 2024 for his distinguished\, career-long contributions to the field of Science and Technology Studies (STS). His book Memory Practices in the Sciences (MIT Press 2008) won the 2007 Ludwig Fleck Prize of the Society for Social Studies of Science\,  and was awarded “Best Information Science Book” by the American Society for Information Science and Technology (ASIS&T). \nCo-sponsored by earthecologies x technoscience conversations\, History of Consciousness
URL:https://live-events-ucsc.pantheonsite.io/event/february-25-2026-works-in-progress-with-geoffrey-bowker/
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T160000
DTEND;TZID=America/Los_Angeles:20260302T170000
DTSTAMP:20260225T181221Z
CREATED:20260225T181221Z
LAST-MODIFIED:20260225T181221Z
UID:10009355-1772467200-1772470800@live-events-ucsc.pantheonsite.io
SUMMARY:AM Seminar: The Evolving Landscape of AI for Science and Engineering: Bridging Simulation\, Experiment\, and Multi-scale Dynamics
DESCRIPTION:Presenter: Aditi Krishnapriyan\, Assistant Professor\, UC Berkeley \nDescription: Recent advances in large-scale scientific datasets are creating new opportunities for machine learning (ML) methods to more effectively capture scientific phenomena with greater accuracy and reach. In this talk\, I will discuss how these advances are both shifting ML design paradigms and enabling new scientific inquiries. This includes investigations into understanding if neural networks can autonomously discover fundamental physical relationships from data\, and demonstrating how more flexible machine learning modeling design choices enable capturing physical dynamics across multiple scales. I will also explore how generative modeling approaches rooted in statistical physics can be applied to accelerate the sampling of dynamic pathways\, and as a framework to align and bridge the gap between simulated data and experimental observations. \nBio: Aditi Krishnapriyan is an Assistant Professor at UC Berkeley where she is part of Chemical and Biomolecular Engineering\, Electrical Engineering and Computer Sciences\, and Berkeley AI Research; as well as a faculty scientist in the Applied Mathematics division at Lawrence Berkeley National Laboratory. She holds a PhD from Stanford University\, supported by the DOE Computational Science Graduate Fellowship\, was the Luis W. Alvarez Fellow in Computing Sciences at Lawrence Berkeley National Laboratory\, and is a recipient of the Department of Energy Early Career Award and RCSA Scialog. Her research focuses on developing physics-inspired machine learning methods that bridge machine learning with physical science applications to capture phenomena across multiple length and timescales. \nHosted by: Applied Mathematics
URL:https://live-events-ucsc.pantheonsite.io/event/am-seminar-the-evolving-landscape-of-ai-for-science-and-engineering-bridging-simulation-experiment-and-multi-scale-dynamics/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T160000
DTEND;TZID=America/Los_Angeles:20260302T170000
DTSTAMP:20260202T195322Z
CREATED:20260202T195322Z
LAST-MODIFIED:20260202T195322Z
UID:10009146-1772467200-1772470800@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Decoding Phytoplankton Responses to a Changing Ocean
DESCRIPTION:Presenter: Francois Ribalet\, Research Associate Professor\, School of Oceanography\, University of Washington \nDescription: François Ribalet will present new observational technologies and computational approaches for studying phytoplankton responses to ocean warming. Using SeaFlow\, a custom-built automated flow cytometer deployed on over 100 research cruises\, his team has collected nearly 850 billion cell measurements across global oceans. Matrix population models applied to these data reveal how temperature affects phytoplankton division rates and biomass. The research shows that Prochlorococcus\, the ocean’s most abundant photosynthetic organism\, experiences sharp declines in growth above 28°C. Climate projections incorporating these metabolic constraints predict a 40-60% decrease in Prochlorococcus production in tropical regions by 2100\, with Synechococcus partially compensating through a 20-40% increase. These shifts between dominant phytoplankton groups will likely disrupt ocean food webs and carbon cycling\, raising questions about whether tropical ecosystems can adapt to warming oceans. \n\n\n\n\n\n\n\n\n\nBio: François Ribalet is a research associate professor at the University of Washington studying phytoplankton and their role in ocean food webs and carbon cycling. He combines field observations with statistical models to understand how environmental changes affect the growth and community dynamics of these microscopic organisms. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-decoding-phytoplankton-responses-to-a-changing-ocean/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T080000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260225T190019Z
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009358-1773043200-1773075600@live-events-ucsc.pantheonsite.io
SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://live-events-ucsc.pantheonsite.io/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/1/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
END:VCALENDAR