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 2022: Elected Vice-President/President-Elect of the INFORMS Junior Faculty Interest Group (JFIG)
- April 2022: New Paper “Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction” (joint work with Jiahao Shi, Zihong Yi and Baoyu Zhou)
- March 2022: Co-organized an INFORMS Junior Faculty Interest Group (JFIG) panel on “From Finding Funding Opportunities to CAREER Awards: A Guide for Junior Faculty” (recording)
- March 2022: Hosted Clément Royer (Université Paris Dauphine-PSL) as a research visitor in the IOE department (Clément Royer’s MIDAS talk)
- March 2022: Presented at Mathematics in Imaging, Data and Optimization (MIDO) Seminar (Department of Mathematical Science) at Rensselaer Polytechnic Institute (RPI)
- February 2022: New Paper “Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide NOx Emissions using Deep Learning” (joint work with Rinav Pillai, Vassilis Triantopoulos, Matthew Brusstar, Ruonan Sun, Tim Nevius and André L. Boehman) has been published in Frontiers in Mechanical Engineering (Engine and Automotive Engineering, Special Issue: Artificial Intelligence for Future Internal Combustion Engines: Experiments, Modeling, and Optimization)
- February 2022: Presented at the Computational Mathematics Seminar at the Mathematical Sciences Institute at the Australian National University (Canberra, Australia; virtual)
- January 2022: Presented at the Workshop on Optimization, Probability, and Simulation (WOPS 2022, The Chinese University of Hong Kong, Shenzhen)
- December 2021: Invited to be the co-chair of the Nonlinear Optimization cluster for the ICCOPT 2022 conference (International Conference on Continuous Optimization)
- October 2021: Very interesting presentations in the Nonlinear Optimization cluster at the INFORMS Annual Meeting 2021; Thank you to all the speakers and session organizers.
- October 2021: Presented in a session organized by Lijun Ding and Madeleine Udell (Recent Advances in Nonconvex Optimization II) at the INFORMS Annual Meeting 2021
- September 2021: Organized the IOE department seminars this semester
- September 2021: Presented in the OR Colloquium at Penn State University (Department of Industrial and Manufacturing Engineering)
- September 2021: Invited to be the chair of the Nonlinear Optimization cluster for the INFORMS 2022 Optimization Society Conference
- August 2021: Our paper “Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample“ (joint work with Majid Jahani, Peter Richtárik and Martin Takáč) has been accepted for publication in Optimization Methods & Software
- August 2021: New Paper “Full-low evaluation methods for derivative-free optimization“ (joint work with Frank E. Curtis, Michael J. O’Neill and Daniel P. Robinson)
- August 2021: Chair of the organizing committee of the MOPTA 2021 conference August 2-4, 2021
- July 2021: Co-organized the “Beyond First Order Methods in Machine Learning Systems“ workshop at ICML 2021 (co-organizers Raghu Bollapragrada, Rixon Crane, Amir Gholami, J. Lyle Kim, Anastasios (Tasos) Kyrillidis, and Michael Mahoney, Fred Roosta, Rachael Tappenden)
(For the full list of news please check the News tab.)