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.

For instance, some of her work on lattice methods-which provide one way of constructing low-discrepancy point sets-has helped to make these methods more attractive to practitioners by providing explicit constructions shown to work well in practical settings, for instance for high-dimensional finance problems. She has also worked on the combination of quasi-Monte Carlo methods with commonly used variance reduction techniques, thereby increasing the ability for these methods to be a substitute for Monte Carlo without losing any advantages.

Professor Lemieux's contributions in the field of quasi-Monte Carlo methods have also involved exploring a wide range of settings where Monte Carlo could be replaced by quasi-Monte Carlo. She has done so by looking at applications from different fields (finance, actuarial science, biology, computer vision), and also by considering various types of Monte Carlo approaches (Monte Carlo integration/simulation, Markov Chain Monte Carlo, sequential Monte Carlo).

Her current research interests include the construction of new low-discrepancy point sets and sequences designed to work well in practical settings, and the use of quasi-Monte Carlo methods in various risk management problems.

Education/biography

After obtaining her PhD degree under the supervision of Professor Pierre L'Ecuyer at the University of Montreal, Professor Lemieux spent the first six months of 2000 as a postdoctoral scholar in the statistics department at Stanford University, where she worked under the supervision of Professor Art B. Owen. In July 2000, she joined the Department of Mathematics and Statistics at the University of Calgary as an assistant professor, where she also held a joint appointment with the Department of Computer Science. She joined the Department of Statistics and Actuarial Science in Waterloo as an associate professor in July 2006.

Professor Lemieux has collaborators in Canada, the U.S., France, and Australia.

Selected publications

• H. Faure, C. Lemieux. Improvements on the star discrepancy of (t,s)-sequences. To appear in Acta Arithmetica, 2012.
• H. Faure, C. Lemieux, X. Wang. Extension of Atanassov's methods for Halton sequences. To appear in Monte Carlo and Quasi-Monte Carlo Methods 2010, Springer, 2012.
• H. Faure, C. Lemieux. Improved Halton sequences and discrepancy bounds. Monte Carlo Methods and Applications, 16, 231-250, 2010.
• C. Lemieux. Monte Carlo and Quasi-Monte Carlo Sampling. Springer Series in Statistics. Springer, New York, 2009.
• C. Lemieux, H. Faure. New Perspectives on (0,s)-Sequences. Monte Carlo and Quasi-Monte Carlo 2008, P. L'Ecuyer and A.B. Owen eds, Springer-Verlag, 113--130, 2009.
• H. Faure, C. Lemieux. Generalized Halton Sequences in 2008: A Comparative Study. ACM Transactions on Modeling and Computer Simulation, 19 (Article 15), 2009.
• M. Cieslak, C. Lemieux, J. Hanan and P. Prusinkiewicz. Quasi-Monte Carlo Simulation of the Light Environment for Plants. Functional Plant Biology, 35, 837-849, 2008.
• C. Bernard, C. Lemieux. Fast simulation of equity-linked life insurance contracts with a surrender option. Proceedings of the 2008 Winter Simulation Conference, 444-452, IEEE Press, Piscataway, NJ, 2008.
• C. Bernard, C. Lemieux. Fast simulation of equity-linked life insurance contracts with a surrender option. Proceedings of the 2008 Winter Simulation Conference, 444-452, IEEE Press, Piscataway, NJ, 2008.
• R.V. Craiu,  C. Lemieux. Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling. Statistics and Computing, 17, 109-120, 2007