The Department of Statistics and Actuarial Science is a top-tier academic unit among statistical and actuarial science globally. Our community is engaged in topics such as actuarial science, biostatistics, data science, quantitative finance, statistics, & statistics-computing. Our department is home to more than 60 full-time faculty researching diverse and exciting areas, over 1000 undergraduate students from around the world, and more than 175 graduate students in master, doctoral, and professional programs.
News
Department of Statistics and Actuarial Science is pleased to welcome Subha Maity as an Assistant Professor
Maity, Subha (B.Stat, 2016; M.Stat, 2018, Indian Statistical Institute, Kolkata; Ph.D. (Statistics), 2024, University of Michigan) has joined the Department of Statistics and Actuarial Science on October 1st, 2024, as an Assistant Professor. Before that, Subha Maity was a Research Associate at the University of Pennsylvania. His primary research focuses on transfer learning and algorithmic fairness, with a broader focus on problems at the intersection of statistical theory and machine learning. His current research focuses on developing statistical methods for addressing distribution shift using pre-trained models.
2024 University Research Chairs named
Professor Mu Zhu has been named one of the 2024 University Research Chairs.
Mu's initial research interest was dimension reduction. In the early years of his faculty career, he devoted much attention to efficient kernel machines for rare target detection and ensemble methods for variable selection. He also worked on algorithms for making personalized recommendations, and applications of machine learning to healthcare informatics.
While ensemble learning continued to captivate his curiosity, in more recent years Mu has explored a hodgepodge of different topics—such as evaluation metrics, protein structures, transactional networks, and genetic epistasis. He also wrote a textbook for data science students. At present, he is studying various problems about dependence modeling, large covariance matrices, and generative neural networks.
His appointment was effective July 1, 2024.
Originally announced on the Daily Bulletin.
Department of Statistics and Actuarial Science is pleased to welcome Peter MacDonald as an Assistant Professor
MacDonald, Peter W. (BMath (Statistics & Pure Mathematics), 2017, University of Waterloo; MMath (Statistics), 2018, University of Waterloo; PhD (Statistics), 2023, University of Michigan) has joined the Department of Statistics and Actuarial Science on July 1, 2024 as an Assistant Professor. Before returning to Waterloo, Peter was a postdoctoral scholar at McGill University (2023-2024). Peter's research is focused on statistical network analysis. His specific research interests include latent space modeling of multilayer, multiplex, and dynamic network data, as well as inferential methods for comparing samples of networks. He also has interests in post-selective inference and multiple hypothesis testing.
Events
Seminar by Rui Gao
Actuarial Science and Financial Mathematics seminar series
Rui Gao
University of Texas at Austin
Room: M3 3127
David Sprott Distinguished Lecture by Paul Gustafson
Distinguished Lecture Series
Paul Gustafson
Department of Statistics at the University of British Columbia
Room: DC 1302
Bayesian Inference when Parameter Identification is Lacking: A Narrative Arc across Applications, Methods, and Theory
Partially identified models generally yield “in between” statistical behavior. As the sample size goes to infinity, the posterior distribution on the target parameter heads to a distribution narrower than the prior distribution but wider than a single point. Such models arise naturally in many areas, including the health sciences. They arise particularly when we own up to limitations in how data are acquired. I aim to highlight the narrative arc associated with partial identification. This runs from the applied (e.g., broaching the topic with subject-area scientists), to the methodological (e.g., implementing a Bayesian analysis without full identification), to the theoretical (e.g., characterizing what is going on as generally as possible). As per many areas of statistics, there is good scope to get involved across the whole arc, rather than just at one end or other.