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 70 full-time faculty researching diverse and exciting areas, over 2000 undergraduate students from around the world, and approximately 200 graduate students in master, doctoral, and professional programs.
News
2024 Outstanding Performance Awards winners named
Created in 2005, the Outstanding Performance Awards recognize excellence in teaching and scholarship from faculty members across all of the University of Waterloo’s Faculties.
From biostatistics to transforming community health
Dr. Zainab Abdurrahman, the first Black female president of the Ontario Medical Association, is a clinical immunologist and allergist who sees herself as a bridge builder. Her MMath in Biostatistics (’03) from the University of Waterloo has given her a unique perspective, complementing her passion for patient communication and clinical care.
Read the full story on Waterloo News.
Check out the @waterloomath Instagram to learn more about Dr. Abdurrahman.
Statistical Society of Canada (SSC) approves University of Waterloo courses for A.Stat. Accreditation
The Department of Statistics and Actuarial Science is happy to announce that the Statistical Society of Canada has approved its courses for the purposes of A.Stat. Accreditation. Accreditation enhances the professional practice of statistics, facilitates professional development, and provides mentorship for new graduates through work with Accredited Professional Statisticians. The approved course list removes barriers for both undergraduate and graduate students who are interested in applying for an A.Stat. Accreditation and helps to streamline the process. For more information about accreditation and the application, please visit the SSC’s website.
Events
Joint Distinguished Lecture by Bin Yu
Dean's Distinguished Women in Mathematics, Statistics and Computer Science Lecture Series & David Sprott Distinguished Lecture Series
Bin Yu
CDSS Chancellor's Distinguished Professor, Statistics, EECS, Center for Computational Biology
Senior Advisor, Simons Inst for the Theory of Computing
Member, U.S. National Academy of Sciences, 2014
Member, American Academy of Arts and Sciences, 2013
Guggenheim Fellow, 2006
Room: DC 1302
Veridical Data Science towards Trustworthy AI
In this talk, I will introduce the Predictability-Computability-Stability (PCS) framework for veridical (truthful) data science, highlighting its critical role in producing reliable and actionable insights. I will share success stories from cancer detection and cardiology, showcasing how PCS principles have guided cost effective designs and improved outcomes in these projects. Since trustworthy uncertainty quantification is indispensable for trustworthy AI, I will discuss PCS uncertainty quantification for prediction in regression and multi-class classification. PCS-UQ consists of three steps: pred-check, bootstrap, and multiplicative calibration. Through test results over 26 benchmark datasets, PCS-UQ will be shown to outperform common forms of conformal prediction in terms of width, subgroup coverage, and subgroup interval width. Finally, the multiplicative step in PCS-UQ will be shown to be a new form of conformal prediction.