Welcome to my website. My name is Albert S. Berahas and I am an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan.
My research focuses on designing, developing, analyzing and implementing 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, I am interested in and have 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. I am affiliated with the Michigan Institute for Data Science (MIDAS), the Michigan Institute of Computational Discovery and Engineering (MICDE), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM).
Email: aberahas@umich.edu, albertberahas@gmail.com
Office: 2783 IOE Building, 1205 Beal Avenue Ann Arbor, MI 48109 (map)
Quick Links: Full CV, Bio, Google Scholar, arxiv, LinkedIn
Announcements
I am always looking for outstanding and highly motivated PhD students and Postdoctoral Research Fellows to join my team. If you are interested, please send me an email with your CV, and apply to our PhD program.
News
- September 2023: New Paper “Adaptive Consensus: A network pruning approach for decentralized optimization” (joint work with Suhail M. Shah and Raghu Bollapragada)
- August 2023: Presented at the Modeling and Optimization: Theory and Applications (MOPTA) conference in Bethlehem, PA; Also served on the organizing committee and chaired the poster competition
- August 2023: Baoyu Zhou joins the research team as a postdoctoral research fellow-Welcome, Baoyu!
- July 2023: Our paper “First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles” (joint work with Liyuan Cao and Katya Scheinberg) has been accepted for publication in Mathematical Programming
- June 2023: Visited Clément Royer at Université Paris Dauphine-PSL for 2 weeks
- June 2023: New Paper “Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design” (joint work with Xubo Yue, Raed Al Kontar, Yang Liu, Zhenghao Zai, Kevin Edgar and Blake N. Johnson)
- May 2023: Presented at the SIAM Optimization conference in Seattle, WA
- May 2023: Our paper “Multiblock Parameter Calibration in Computer Models” (joint work with Cheoljoon Jeong, Ziang Xu, Eunshin Byon and Kristen Cetin) has been accepted for publication in the INFORMS Journal on Data Science
- April 2023: Our paper “Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction” (joint work with Jiahao Shi, Zihong Yi and Baoyu Zhou) has been accepted for publication in Computational Optimization and Applications
- March 2023: New Paper “Balancing Communication and Computation in Distributed Optimization” (joint work with Raghu Bollapragada and Shagun Gupta)
- February 2023: Honored to be awarded the Mathematical Programming 2022 Meritorious Service Award
- January 2023: Presented at the Michigan Institute Computational Discovery and Engineering (MICDE) Seminar Series Winter 2023
- January 2023: Presented at the 12th US-Mexico Workshop on Optimization and Its Applications, Huatulco, Mexico
- January 2023: New Paper “A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization” (joint work with Miaolan Xie and Baoyu Zhou)
(For the full list of news please check the News tab.)