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
Students Tackle Data at 2024 ASA DataFest
From April 26-28, teams of undergraduate student competed at the 2024 American Statistical Association (ASA) DataFest hosted by the Department of Statistics and Actuarial Science. Students worked around the clock with complex datasets assisted by graduate students, faculty and industry professionals to compete for prizes.
Cecilia Cotton and Jordan Hamilton win Distinguished Teacher Awards
Congratulations to Cecilia Cotton and Jordan Hamilton, recipients of the 2024 Distinguished Teacher Awards from Waterloo’s Centre for Teaching Excellence. Since 1975, the award has been given annually to four exemplary instructors from across the university who have a “record of excellent teaching over an extended period at Waterloo, usually at least five years.”
Read the full article on the Faculty of Mathematics website.
Qiuqi Wang Won the 2024 SSC Pierre Robillard Award
The Statistical Society of Canada (SSC) citation reads: "The Pierre Robillard Award is awarded annually by the SSC to recognize the best Ph.D. thesis in probability or statistics defended at a Canadian university during the previous year.
Qiuqi Wang is the 2024 winner of the Pierre Robillard Award of the Statistical Society of Canada. Qiuqi’s thesis, entitled "Characterizing, optimizing and backtesting metrics of risk", was written while he was a doctoral student at the University of Waterloo working under the supervision of Ruodu Wang."
Read the full article on the SSC website.
Events
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
Despite several proposed roadmaps to increase diversity in scientific research, most of the world's research data are collected on people of European ancestry. We rely on summary statistics from historically privileged populations and then devise clever statistical methods to transfer/transport them for cross-ancestry use. In this talk, I would first argue the obvious: for building fair algorithms we need fair training datasets. However, till we have reached the dream of equitable big data at a global scale, statisticians have an important role to play. In fact we have the perfect tools to study the "unobserved" through modeling of missing data, selection bias and alike. I will share examples from my personal journey as a statistician where doing good and timely statistical work with imperfect data quantified important disparity in health outcomes and led to policy impact. I will conclude the talk with a call to arms for statisticians to lead efforts for creating, curating, collecting data and pioneering new scientific studies, not just remain on the design and analytic fringes. As public health statisticians, our job is not just to predict, but to prevent. The talk is based on years of work with my students and colleagues at the Department of Biostatistics, University of Michigan and inspired by the transformative experience we shared as a statistical team working on the COVID-19 pandemic.