Donald Estep

 

Director, Canadian Statistical Sciences Institute

SIAM Fellow

Chalmers University Jubilee Professor 2013-14

Professor

 

Department of Statistics and Actuarial Science

Simon Fraser University

Welcome!

 

I am the Director of CANSSI and a professor in the Department of Statistics and Actuarial Science at Simon Fraser University. Previously, I was a University Distinguished Professor, University Interdisciplinary Research Scholar, and Chair of the Department of Statistics at Colorado State University. Before that, I was a professor in the School of Mathematics at Georgia Tech for 13 years. I have also spent significant amounts of time at Chalmers University of Technology in Göteborg, Sweden and Caltech.

 

My research interests include uncertainty quantification for simulations of complex models, stochastic inverse problems for parameter determination, algorithms for efficient computation, and stochastic modeling of multiscale systems. My scope embraces theoretical analysis, development and implementation of algorithms, and application to scientific and engineering problems. I have close interdisciplinary collaborations with scientists and engineers in applications including ecology, materials science, detection of black holes, modeling of fusion reaction, analysis of nuclear fuels, hurricane storm surge forecasting, flow in porous media, and electromagnetic scattering. My research has received strong financial support from multiple government agencies, national laboratories, and industry.

 

I have extensive experience teaching mathematics and statistics courses, especially for engineering and science students. I have written several textbooks that reflect my ideas about learning. I am generally interested in learning and teaching, and have devised training and review programs for teaching for both graduate students and faculty.

 

In these pages, you will find information about my research and educational activities.

Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise. - John Tukey

 

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