Location
2783 IOE
Phone
Primary Website
Biography
Albert S. Berahas is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan. Prior to this appointment, he was a Postdoctoral Research Fellow at Lehigh University (2018-2020) and at Northwestern University (2018). He received his PhD in Engineering Sciences and Applied Mathematics in 2018 from Northwestern University. He received his undergraduate degree in Operations Research and Industrial Engineering from Cornell University in 2009, and in 2012 obtained an MSC degree in Engineering Sciences and Applied Mathematics from Northwestern University. His research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. 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.
Education
- PhD, Northwestern University, 2018, Engineering Sciences and Applied Mathematics
- MSC, Northwestern University, 2012, Engineering Sciences and Applied Mathematics
- BSc, Cornell University, 2009, Operations Research and Industrial Engineering
Research Interests
- Nonlinear Optimization
- Machine Learning
- Deep Learning
- Stochastic Optimization
- Constrained Optimization
- Derivative-Free Optimization
- Distributed Optimization
Professional Society Memberships
Awards
- IOE Department Faculty Award, College of Engineering, University of Michigan, 2024.
- MLK Spirit Award for Community Building & Impact, College of Engineering, University of Michigan, 2024.
- IISE Excellence in Teaching of Operations Research Award, Institute of Industrial and Systems Engineers, 2024.
- Meritorious Service Award, Mathematical Programming Journal, 2022.
Sample Publications
- Berahas, A. S., Shi, J., Yi, Z., & Zhou, B. (2023). Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction. Computational Optimization and Applications, 86(1), 79-116.
- Berahas, A. S., Curtis, F. E., Robinson, D., & Zhou, B. (2021). Sequential quadratic optimization for nonlinear equality constrained stochastic optimization. SIAM Journal on Optimization, 31(2), 1352-1379.
- Berahas, A. S., Curtis, F. E., O’Neill, M. J., & Robinson, D. P. (2023). A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear-Equality-Constrained Optimization with Rank-Deficient Jacobians. Mathematics of Operations Research.
- Berahas, A. S., Cao, L., Choromanski, K., & Scheinberg, K. (2022). A theoretical and empirical comparison of gradient approximations in derivative-free optimization. Foundations of Computational Mathematics, 22(2), 507-560.
- Berahas, A. S., Cao, L., & Scheinberg, K. (2021). Global convergence rate analysis of a generic line search algorithm with noise. SIAM Journal on Optimization, 31(2), 1489-1518.
- Berahas, A. S., Curtis, F. E., & Zhou, B. (2022). Limited-memory BFGS with displacement aggregation. Mathematical Programming, 194(1), 121-157.
- Berahas, A. S., Byrd, R. H., & Nocedal, J. (2019). Derivative-free optimization of noisy functions via quasi-Newton methods. SIAM Journal on Optimization, 29(2), 965-993.
- Berahas, A. S., Bollapragada, R., Keskar, N. S., & Wei, E. (2018). Balancing communication and computation in distributed optimization. IEEE Transactions on Automatic Control, 64(8), 3141-3155.
- Jeong, C., Xu, Z., Berahas, A. S., Byon, E., & Cetin, K. (2023). Multiblock parameter calibration in computer models. INFORMS Journal on Data Science, 2(2), 116-137.
- Pillai, R., Triantopoulos, V., Berahas, A. S., Brusstar, M., Sun, R., Nevius, T., & Boehman, A. L. (2022). Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NO x) Emissions Using Deep Learning. Frontiers in Mechanical Engineering, 8, 840310.