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) and with the Michigan Institute for Computational Discovery and Engineering (MICDE).
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 looking for outstanding and highly motivated PhD students and Postdoctoral Research Fellows to join my team. If interested, please send me an email with your CV, and apply to our PhD program.
News
- April 2021: Our ICML 2020 workshop “Beyond First Order Methods in Machine Learning Systems“ (co-organizers Raghu Bollapragrada, Rixon Crane, Amir Gholami, J. Lyle Kim, Anastasios (Tasos) Kyrillidis, and Michael Mahoney, Fred Roosta, Rachael Tappenden) has been accepted (all past workshops can be found here)
- March 2021: Our paper “A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization“ (joint work with Liyuan Cao, Krzysztof Choromanski and Katya Scheinberg) has been accepted for publication in Foundations of Computational Mathematics
- March 2021: Our paper “Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise“ (joint work with Liyuan Cao and Katya Scheinberg) has been accepted for publication in SIAM Journal on Optimization
- March 2021: Presented in a session organized by Amir Gholaminejad, Fred Roosta and Michael Mahoney (Beyond First Order Methods in Machine Learning Systems) at the SIAM CSE 2021
- February 2021: Organized the IOE department seminars this semester
- February 2021: Our paper “SONIA: A Symmetric Blockwise Truncated Optimization Algorithm“ (joint work with Majid Jahani, Mohammadreza Nazari, Rachael Tappenden and Martin Takáč) has been accepted for publication at the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)
- January 2021: Our paper “Limited-Memory BFGS with Displacement Aggregation“ (joint work with Frank E. Curtis and Baoyu Zhou) has been accepted for publication in Mathematical Programming
- December 2020: Elected INFORMS Optimization Society Vice Chair for Nonlinear Optimization
(For the full list of news please check the News tab.)