Professors

Contact Information:
David Saunders

David Saunders's personal website

Research interests

Decision making under uncertainty is prevalent in all human endeavours. The pervasive need to make critical decisions while faced with imperfect knowledge of the future is perhaps most acute in the area of finance. This has resulted in a commensurate demand for tools that allow market participants to analyze and manage their risks effectively. Professor Saunders' research focuses on the application of stochastic optimization and probability, particularly to problems in finance.

Contact Information:
Paul Marriott

Research interests

Professor Marriott's research activity is split between theoretical and applied work.

Contact Information:
Pengfei Li

Pengfei Li's personal website

Research interests

Professor Li's research interests concern some areas of statistics, including finite mixture model, asymptotic theory, empirical likelihood, inference with constraints, experimental design, and smoothing technique.

Contact Information:
Christiane Lemieux

Christiane Lemieux's personal website

Research interests

Professor Lemieux is interested in quasi-Monte Carlo methods and their applications. These methods can be thought of as deterministic versions of the well-known and highly used Monte Carlo method. They are designed to improve upon the performance of Monte Carlo by replacing random sampling by a more uniform sampling mechanism based on low-discrepancy point sets. A major goal of Professor Lemieux's research is to improve the applicability of quasi-Monte Carlo methods to a wide variety of practical problems.

Canada Research Chair, Risk Theory

Contact Information:
David Landriault

Research interests

Actuarial Science, Quantitative Risk Management, Applied Probability, Stochastic Process, Risk and Ruin Theory, Stochastic Control Problems in Insurance and Finance, Stochastic Modelling in Insurance

Contact Information:
Ali Ghodsi

Ali Ghodsi's personal website

Research interests

Machine learning, Deep learning, Computational statistics, Dimensionality reduction, Natural language processing, Bioinformatics.

Professor Ghodsi's current research sweeps across a broad swath of AI encompassing machine learning, deep learning, and dimensionality reduction.  He studies theoretical frameworks and develops new machine-learning algorithms for analyzing large-scale data sets, with applications in natural language processing, bioinformatics, and computer vision. Dr. Ghodsi's work has been published extensively in high-quality proceedings and journals. He is the co-author of the "Elements of Dimensionality Reduction and Manifold Learning" (Springer)  and several US patents. His popular lectures on YouTube have more than one million views. View a complete list of his online lectures.

​Contact Information:
Joel Dubin

​Health Data Science Lab (HDSL) Lead:

HDSL Website 

Research interests

My primary research interest is in the area of methodological development in longitudinal data analysis, including for multivariate longitudinal data, where more than one outcome, (e.g., systolic and diastolic blood pressure) are each followed for individuals over time. Methods pursued for this type of data include the correlation of different longitudinal outcomes over time using curve-based methods, and incorporating lags and derivatives of the curves. I am also interested in change point and latent response models for longitudinal data, as well as prediction models, including the consideration of similarity to improve prediction accuracy.

Contact Information:
Steve Drekic

Steve Drekic's Google Scholar profile

Research interests

Professor Drekic has co-authored over 40 articles, and has published in key journals across several disciplines, including actuarial science, operations research, and statistics. His work has garnered particular attention in the fields of applied probability, insurance risk/ruin theory, and queueing theory. Professor Drekic's expertise lies in the use of probabilistic/stochastic techniques with advanced computational methods to analyze mathematical problems arising in several different application areas.

Contact Information:
Cecilia Cotton

Research interests

The underlying theme of my research interests is using longitudinal data to solve problems in public health:

• Inference for comparing survival across multiple dynamic treatment regimens based on observational longitudinal data.
• Applications to epoetin dosing strategies for hemodialysis subjects with chronic kidney disease.
• Joint modeling of longitudinal and survival data in the context of causal inference.

Contact Information:
Richard J. Cook

My personal website
My books with Jerry Lawless

Research interests

My research interests include the development and application of statistical methods for public health. Specific areas of interest include the analysis of life history data, longitudinal data, incomplete data, sequential methods, multivariate analysis, clinical trial design, and the assessment of diagnostic tests.