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
- January 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
- November 2020: Organized four sessions (Methods for Large Scale, Nonlinear and Stochastic Optimization I/II/III/IV) at the INFORMS Annual Meeting
- November 2020: Presented in a session organized by Siqian Shen (Computational Methods for Large-scale Stochastic Programs) at the INFORMS Annual Meeting
- September 2020: Our paper “Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations“ (joint work with Zheng Shi, Nur Sila Gulgec, Shamim N. Pakzad and Martin Takáč) has been accepted for publication at the 19th IEEE International Conference on Machine Learning and Applications
- September 2020: Presented in the MIDAS (Michigan Institute for Data Science) Faculty Pitch Presentations (link)
- September 2020: Became an Affiliated Faculty member of MIDAS (Michigan Institute for Data Science) and MICDE (Michigan Institute of for Computational Discovery and Engineering)
- August 2020: Officially joined the Department of Industrial & Operations Engineering at the University of Michigan as an Assistant Professor (faculty profile, IOE news)
(For the full list of news please check the News tab.)