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
- October 2022: New Paper “Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients: Convergence, Divergence and Limitations of its Continuous Approximation” (joint work with Vivak Patel)
- September 2022: Participated in a video about industrial engineering; check it out! Long version/Short version
- August 2022: Received a grant in collaboration with Clément Royer from the Thomas Jefferson Foundation – FACE Foundation (ALIAS: Adaptive, Local and Innovative Algorithms for Stochastic Optimization)
- July 2022: Our NeurIPS workshop “Order up! The Benefits of Higher-Order Optimization in Machine Learning” (co-organizers Jelena Diakonikolas, Jarad Forristal, Brandon Reese, Martin Takac and Yan Xu) has been accepted
- June 2022: New Paper “An Adaptive Sampling Sequential Quadratic Programming Method for Equality Constrained Stochastic Optimization” (joint work with Raghu Bollapragada and Baoyu Zhou)
- May 2022: Received a Seeding To Accelerate Research Themes (START) grant from the College of Engineering at the University of Michigan (with Professors Laura Balzano, Eunshin Byon, Salar Fattahi and Qing Qu)
- May 2022: Hosted Baoyu Zhou (Lehigh University) as a research visitor in the IOE department
- May 2022: New Paper “First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles” (joint work with Liyuan Cao and Katya Scheinberg)
- May 2022: Hosted Vivak Patel (University of Wisconsin-Madison) as a research visitor in the IOE department
- 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)
(For the full list of news please check the News tab.)