Albert S. Berahas

• 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

Dr. Berahas’s research focuses on designing, developing, analyzing, and implementing algorithms to solve large-scale nonlinear optimization problems. His work spans a range of topics within nonlinear optimization, with particular interest in: (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. Through this diverse portfolio, Dr. Berahas aims to advance both the theoretical foundations and practical performance of optimization methods across a wide range of applications.

• Institute of Industrial and Systems Engineers (IISE)
• Institute for Operations Research and the Management Sciences (INFORMS)
• Mathematical Optimization Society (MOS)
• Society for Industrial and Applied Mathematics (SIAM)

• 2025 Alpha Pi Mu IOE Outstanding Instructor Award, Department of Industrial and Operations Engineering, University of Michigan, 2025
• 2022 Charles Broyden Prize, Best Paper 2022 Optimization Methods and Software Journal, 2025
• 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.
• INFORMS Moving Spirit Award for Forums, Institute for Operations Research and Management Sciences (INFORMS), 2024
• IISE Excellence in Teaching of Operations Research Award, Institute of Industrial and Systems Engineers (IISE), 2024.
• Meritorious Service Award, Mathematical Programming Journal, 2022.

• Berahas, A. S., Xie, M., & Zhou, B. (2025). A Sequential Quadratic Programming Method With High-Probability Complexity Bounds for Nonlinear Equality-Constrained Stochastic Optimization. SIAM Journal on Optimization, 35(1), 240-269.
• Cao, L., Berahas, A. S., & Scheinberg, K. (2024). First-and second-order high probability complexity bounds for trust-region methods with noisy oracles. Mathematical Programming, 207(1), 55-106.
• Shah, S. M., Berahas, A. S., & Bollapragada, R. (2024). Adaptive Consensus: A network pruning approach for decentralized optimization. SIAM Journal on Optimization, 34(4), 3653-3680.
• 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., 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., Curtis, F. E., & Zhou, B. (2022). Limited-memory BFGS with displacement aggregation. Mathematical Programming, 194(1), 121-157.
• 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., 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., Byrd, R. H., & Nocedal, J. (2019). Derivative-free optimization of noisy functions via quasi-Newton methods. SIAM Journal on Optimization, 29(2), 965-993.