Professors

Contact Information:

Office: M3 3131

Email: kenneth.zhou@uwaterloo.ca

Yangjianchen Xu Personal Website

Research Interests

My research focuses on developing novel statistical methods and theory for survival analysis to address challenges in biomedical and public health sciences. I specialize in semiparametric models for univariate and multivariate time-to-event data under right or interval censoring.

Contact Information:
Zelalem Negeri

I am an Assistant Professor in the Department of Statistics and Actuarial Science. I completed a two-year Post-Doctoral Fellowship at McGill University before joining the University of Waterloo on July 1, 2022.

My research interest focuses on developing and validating statistical methods for applications in public health research, emphasizing both aggregate data and individual participant data meta-analyses of diagnostic and screening test accuracy studies. My research uses computational statistics methods such as parametric and non-parametric bootstrap approaches and deterministic and Monte Carlo expectation-maximization (MCEM) algorithms.

Contact Information:
Fangda Liu

Research Interests

Dr. Liu’s research interests lie in actuarial science and quantitative risk management. Her recent research focuses on the study of model uncertainty in (re-)insurance design problems and risk aggregation problems.

Contact Information:
Lan Wen

My primary areas of research to date have been on the development and application of statistical methods in causal inference and the analysis of observational studies where complications can arise due to model misspecification, time-varying confounding and censoring/missing data. I believe that the development of novel methodologies should be rooted in practical applications. Thus, motivated by real-life applications in public health and medicine, my research addresses methodological and conceptual challenges that scientists may face in answering clinically relevant questions using real-world data studies. Challenges that arise in these observational studies include lack of randomization to the strategies of interest, and/or subjects dropping out of the study due to unknown reasons.

Contact information:

Samuel Wong

Samuel Wong personal website

Research interests

My research focus is in developing methodology for data science problems, with an emphasis on applications in structural biology, dynamic systems, and engineering. I am particularly interested in using Bayesian modelling, Monte Carlo methods, and statistical computation to advance our scientific knowledge in these areas. More generally, I enjoy working on collaborative projects where principled statistical thinking can be combined with scientific expertise to solve new problems.

Contact Information:
Glen McGee

Glen McGee personal website

Research interests

My research interests mainly lie in developing statistical tools to solve problems in epidemiology, environmental health, and health policy. I’m currently interested in Bayesian frameworks for modelling multi-pollutant mixtures, designing and analyzing multigenerational studies, and more broadly in methods for longitudinal and cluster-correlated data.

Contact Information:
Audrey Béliveau

Research Interests

My research is mainly concerned with Bayesian hierarchical modeling and is motivated by applications in a variety of fields, namely ecology and epidemiology. A lot of the problems I work on involve the integration of multiple sources of data in a single analysis (e.g. network meta-analysis in epidemiology and integrated population modeling in ecology). I have a number of interdisciplinary collaborations with academics as well as industry partners.

Contact Information:
Aukosh Jagannath

Research Interests

I am a mathematician working at the interface of mathematical physics, mathematical data science, optimization, and high-dimensional statistics. There are deep connections and analogies arising at the interface of these four fields. The common threads tying them together are fundamental problems in mathematics. The goal of my research is to understand the statistical properties of high-dimensional, complex energy landscapes and how these properties affect the behaviour of algorithms and dynamical systems on these landscapes. I first began studying these questions from the perspective of statistical physics, with an emphasis on analyzing glassy systems. More recently, I have turned to analyzing the average case behaviour of optimization and sampling problems arising in combinatorics, data science, and high-dimensional statistics.

Contact Information:
Nathaniel Stevens

In general Nathaniel is interested in using data to make decisions, solve problems, and improve processes. Specifically, his research interests lie at the intersection of data science and industrial statistics. He is interested in methodological developments in experimental design and A/B testing, process monitoring and network surveillance, reliability and survival analysis, and the assessment and comparison of measurement systems. Recently Nathaniel has developed a family of comparative probability metrics that may be used in place of traditional hypothesis tests in any setting that requires a comparison of statistical quantities.