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Methodologies: Algorithms, Data, Statistics and Stochastic Methods, Machine Learning, Optimization and Control

Albert S. Berahas

Assistant Professor, Industrial & Operations Engineering

Albert S. Berahas is an Assistant Professor of Industrial & Operations Engineering. Dr. Berahas received a BSC in Operations Research and Industrial Engineering from Cornell University, and his MSC and Ph.D.degrees in Engineering Sciences and Applied Mathematics from Northwestern University. Before coming to the University of Michigan, Dr. Berahas held positions as a Postdoctoral Research Fellow at Lehigh University and at Northwestern University.

Dr. Berahas’ research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous, and arise in a plethora of areas such as engineering design, economics, transportation, robotics, machine learning and statistics. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.