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.
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.
Ruodu Wang and Aukosh Jagannath named Canada Research Chairs
Two professors from the Faculty of Mathematics have been named NSERC Canada Research Chairs. Ruodu Wang, professor of actuarial science and quantitative finance, has been named a Tier 1 NSERC CRC in Quantitative Risk Management, and Aukosh Jagannath, assistant professor of statistics and actuarial science, has been named a Tier 2 NSERC CRC in Mathematical Foundations of Data Science.
Second-annual Scotiabank Data Science Discovery Days prepares students to work with AI
More than 150 students participated last week in the second annual Scotiabank Data Science Discovery Days.
The event, which took place both online and on campus from January 26 to February 2, invited Waterloo students to analyze large volumes of data in projects that would emulate the real work data scientists di every day. This year, the event focused on “Using AI to derive business insights from customer feedback.”
Read the full article on the Faculty of Math site.
Events
(Cancelled) Seminar by Gradon Nicholls
Please note that this talk has been cancelled.
Student seminar series
Gradon Nicholls
PhD candidate
Room: M3 3103
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
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.