Kjell Konis




Professor Konis' research interests are in statistical computing. He is the author and maintainer of several packages for the R environment for statistical computing including robust (robust statistical models), lpSolveAPI (linear programming), and RHugin (Bayesian belief networks). Before joining the Applied Mathematics department, Kjell was a postdoctoral research associate in the Chair of Mathematical Statistics at the Swiss Federal Institute of Technology in Lausanne, Switzerland (Ecole Polytechnique Federale de Lausanne; http://epfl.ch) where he conducted research in single particle electron microscopy, kernel smoothing, and forensic science.

Education: B.S. Mathematics & Statistics, B.S. Economics, University of Washington; M.S.c. Applied and Computational Mathematics, D.Phil. Computational Statistics, University of Oxford.

More info: http://depts.washington.edu/amath/people/Kjell.Konis/

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Jun 1st 2015

Mathematical Methods for Quantitative Finance covers topics from calculus and linear algebra that are fundamental for the study of mathematical finance. Students successfully completing this course will be mathematically well prepared to study quantitative finance at the graduate level.

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