Farid Alizadeh

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WP 1062
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Academic Info

Ph.D., University of Minnesota; Computer and Information Sciences


Mathematical optimization: in particular, convex, semidefinite, second order, nonnegative polynomials, linear and combinatorial optimization.
Modeling business, health care and engineering problems as optimization problems. Statistical learning theory, in particular, shape-constrained models. Software engineering, in particular object oriented programming, meta-programming, patterns.


Operations research/management at all levels, statistics at all levels, algorithms PhD level, semidefinite and second order cone programming PhD level, linear programming PhD and Masters level, software engineering at all levels. Teaches at operations research, information technology and quantitative finance programs.

Professor Alizadeh is a leading authority in the area of mathematical optimization. He is one of the originators of the field of semidefinite programming which has found numerous applications in areas as wide as quantitative finance, statistical learning theory, computer science and engineering. His work are some of the most highly cited in optimization theory, and his research has been supported by National Science Foundation and Office of Naval Research. He is among the first generation of scientist to receive NSF CAREER award. He is also the recipient  of the INFORMS Optimization Society 2014 Farkas prize.