معرفی
Christiane Lemieux is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo and Associate Dean of Operations and Academic for the Faculty of Mathematics. Her research focuses on quasi-Monte Carlo methods, low-discrepancy sequences, and their applications in computational finance, machine learning, and risk management.
Education includes a Ph.D. in Computer Science from Université de Montréal (2000), M.Sc. in Mathematics from Université de Montréal (1996), and B.Sc. in Actuarial Science from Université Laval (1994).
Her research explores quasi-Monte Carlo methods for multidimensional integration, low-discrepancy sequence constructions, dependence concepts for point set characterization, machine learning-based construction paradigms, and applications in finance and risk management.
Recent publications demonstrate strong emphasis on combining quasi-Monte Carlo methods with machine learning techniques, particularly graph neural networks for low-discrepancy point set generation and randomized methods for various computational applications.
Awards include the Outstanding Performance Award from University of Waterloo (2012, 2018, 2022) and Journal of Complexity IBC Young Researcher Award (2004).
She actively supervises graduate students and postdoctoral researchers, and has led several NSERC and MITACS grants supporting her research program.
