CRiSM colloquium
CRiSM colloquium
Summer II 2025/26
Speaker: Bin Yu
Title: TBC
Detail: B3.03, 2-3pm on Wednesday 10 June 2026, followed by coffee and cakes outside B3.03.
Abstract: TBC
Bio:
Bin Yu is the Chancellor’s Distinguished Professor in the UC Berkeley Departments of Statistics and EECS. She was Chair of the Department of Statistics at UC Berkeley from 2009 to 2012. She is a member of National Academy of Sciences and currently serves on the editorial board of Proceedings of National Academy of Sciences (PNAS).
Professor Bin Yu has received many awards and honors throughout her career. She has been elected to the National Academy of Sciences and the American Academy of Arts and Sciences. Her major awards include the Guggenheim Fellowship, COPSS E. L. Scott Prize, and most recently, the COPSS Distinguished Achievement Award and Lecture (DAAL) (formerly Fisher Award and Lecture) at JSM in 2023. She has delivered several distinguished lectures, including the Wald Memorial Lectures of the Institute of Mathematical Statistics (IMS), the Tukey Memorial Lecture of the Bernoulli Society, and the Rietz Lecture of IMS. She holds an Honorary Doctorate from the University of Lausanne in Switzerland.
Professor Bin Yu has held many leadership positions in the statistical and data science communities. She served as President of the Institute of Mathematical Statistics (IMS) and was Chair of the Department of Statistics at UC Berkeley from 2009 to 2012. She served on the Inaugural Scientific Committee of the UK Turing Institute for Data Science and AI. Her editorial leadership includes current service on the Editorial Board of Proceedings of National Academy of Sciences (PNAS), and previous service on editorial boards of Annals of Statistics, Journal of American Statistical Association, and Journal of Machine Learning Research. Her committee and advisory work includes co-chairing the National Scientific Committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), serving on scientific advisory committees of SAMSI and IPAM, and on the board of trustees of ICERM and the Board of Governors of IEEE-IT Society. She recently served on the scientific advisory committee for the IAS Special Year on optimization, statistics and theoretical machine learning, and the Scientific Advisory Boards of Canadian Statistical Sciences Institute (CANSSI). Currently, she serves on the advisory board of the AI Policy Hub at UC Berkeley, the Scientific Advisory Committee of the Department of Quantitative and Computational Biology at USC, and on the External Advisory Committee for Learning the Earth with Artificial Intelligence and Physics (LEAP), an NSF Science and Technology Center (STC), at Columbia University. She is a Chan-Zuckerberg Biohub Investigator and Weill Neurohub Investigator. She is a member of the UC Berkeley Center for Computational Biology and serves as a scientific advisor at the Simons Institute for the Theory of Computing.
Summer I 2025/26
Speaker: Nicholas Polson
Title: TBC
Detail: MB0.07, 2-3pm on Thursday 28 May 2026, followed by coffee and cakes in the Atrium.
Abstract: TBC
Bio:
Nicholas G. Polson is a Bayesian Statistician who conducts research on Financial Econometrics, Markov Chain Monte Carlo, Particle Learning and Bayesian inference. Inspired by an interest in probability, Polson has added a number of new algorithms to the fields of Financial Econometrics including the Bayesian analysis of Stochastic Volatility and sequential Particle learning.
Polson's articles have appeared in a number of academic journals, such as the Journal of Finance, Review of Financial Studies, Journal of the American Statistical Association, Journal of Royal Statistical Society, Statistical Science, as well as Chance and the Wall Street Journal. His article, "Bayesian Analysis of Stochastic Volatility Models," was named one of the most influential articles in the 20th anniversary issue of the Journal of Business and Economic Statistics.
He also is the author of Bayesian Inference, edited with G. Tiao and published by Edward Elgar Publishing.
He is currently an associate editor for the Journal of the American Statistical Association.
Polson earned a master's degree with First Class Honours from Worcester College at Oxford University in 1984. He earned a PhD from the University of Nottingham in 1988. He joined Chicago Booth in 1991 after teaching at Carnegie Mellon University and Nottingham University.
Spring 2025/26
Speaker: Daniela Witten
Title: Valid F-screening in linear regression
Detail: MS.04, 2-3pm on Tuesday 27 January 2026, followed by coffee and cakes in the Zeeman foyer.
Abstract: Suppose that a data analyst wishes to report the results of a least squares linear regression only if the overall null hypothesis—namely, that all non-intercept coefficients equal zero—is rejected. This practice, which we refer to as F-screening (since the overall null hypothesis is typically tested using an F-statistic), is in fact common practice across a number of applied fields. Unfortunately, it poses a problem: standard guarantees for the inferential outputs of linear regression, such as Type 1 error control of hypothesis tests and nominal coverage of confidence intervals, hold unconditionally, but fail to hold conditional on rejection of the overall null hypothesis.
This is joint work with Olivia McGough (U. Washington) and Daniel Kessler (UNC Chapel Hill).
Bio: Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning.
She has received a number of awards for her research in statistical machine learning: most notably the Spiegelman Award from the American Public Health Association for a (bio)statistician under age 40, and the Presidents’ Award from the Committee of Presidents of Statistical Societies for a statistician under age 41.
Daniela is a co-author of the textbook "Introduction to Statistical Learning", and since 2023 serves as Joint Editor of Journal of the Royal Statistical Society, Series B.
Summer 2024/25
Prof. Jonathan Wakefield
Prof. Wakefield is Professor of Statistics and Biostatistics at the University of Washington. His research interests include the analysis of complex survey data, small area estimation, spatial epidemiology, space-time models for infectious disease data, software development, estimation of excess mortality, estimating national and subnational mortality burden including child mortality, ecological inference for non-infectious and infectious disease data and the links between Bayes and frequentist procedures. He is a Fellow of the American Statistical Association and was awarded the Guy Medal in Bronze by the Royal Statistical Society in 2000.
Small Area Estimation in Low- and Middle-Income Countries
Small Area Estimation (SAE) in Low- and Middle-Income Countries (LMICs) is fundamentally more difficult than in high income countries, in part because of the lack of good auxiliary information, with census data often being unreliable and/or outdated. In this talk I will discuss models and software for carrying out SAE in LMICs, using examples from the work of my collaborators and I with the United Nations (UN) and the World Health Organization (WHO). These examples include child mortality, vaccination coverage, female educational attainment and fertility. I will discuss the pros and cons of both area-level and unit-level models. Area-level (Fay-Herriot) models rely on less assumptions but require sufficient data to obtain a weighted estimator and an associated variance. Unit-level models need to acknowledge the survey design, and require a tricky aggregation step but can be used with sparse data. I will discuss the nature of spatial smoothing, and strategies for the avoidance of over-smoothing. Other topics to be discussed will include benchmarking, variance modeling, combining different data sources, and the use of machine learning models within Bayesian SAE models.
Details
The talk will take place on Thursday 29th May at 11am in B3.03 in Zeeman. Lunch will be provided afterwards.
Spring 2024/25
Prof. Nicolas Chopin
Nicolas is Professor of Data Sciences/Statistics/Machine Learning at ENSAE, Institut Polytechnique de Paris. He is a world-renowned expert on Bayesian computation, and author of the book "An introduction to Sequential Monte Carlo", with Omiros Papaspiliopoulos.
Saddlepoint Monte Carlo and its Application to Exact Ecological Inference
Assuming X is a random vector and A a non-invertible matrix, one sometimes need to perform inference while only having access to samples of Y = AX. The corresponding likelihood is typically intractable. One may still be able to perform exact Bayesian inference using a pseudo-marginal sampler, but this requires an unbiased estimator of the intractable likelihood.
We propose saddlepoint Monte Carlo, a method for obtaining an unbiased estimate of the density of Y with very low variance, for any model belonging to an exponential family. Our method relies on importance sampling of the characteristic function, with insights brought by the standard saddlepoint approximation scheme with exponential tilting. We show that saddlepoint Monte Carlo makes it possible to perform exact inference on particularly challenging problems and datasets. We focus on the ecological inference problem, where one observes only aggregates at a fine level. We present in particular a study of the carryover of votes between the two rounds of various French elections, using the finest available data (number of votes for each candidate in about 60,000 polling stations over most of the French territory).
We show that existing, popular approximate methods for ecological inference can lead to substantial bias, which saddlepoint Monte Carlo is immune from. We also present original results for the 2024 legislative elections on political centre-to-left and left-to-centre conversion rates when the far-right is present in the second round. Finally, we discuss other exciting applications for saddlepoint Monte Carlo, such as dealing with aggregate data in privacy or inverse problems.
joint work with: Théo Voldoire (Harvard), Guillaume Rateau (INSEE) and Robin J. Ryder (Imperial)
Details
The talk will take place on Tuesday 18th March at 11am in MS.02. Lunch will be provided afterwards in the The Street (the Zeeman Atrium).
Autumn 2024/25
Prof. Sofia Olhede
Sofia is Chair of Statistical Data Science at EPFL. She is a Fellow of the IMS for "seminal contributions to the theory and application of large and heterogeneous networks, random fields and point process, for advancing research in data science”.
Modelling multiplex networks
Multiple networks are complex and highly non-Euclidean objects. This talk will focus on what is known as multiplex networks, representing multiple interaction between the same set of objects, but where the interactions may be of several different types. We shall discuss how a graph limit framework can be used for such objects, and how to naturally characterise the complexity of the whole system of interactions. This characterisation will be used on complexity measures introduced in information theory, and the notion of entropy. This gives us a tool to generally think about systems of relationship.
Details
The talk will take place on Monday 9th December at 11am in MS.03. Lunch will be provided afterwards in the The Street (the Zeeman Atrium).