Albert Berahas
Assistant Professor at University of Michigan
Ann Arbor, Michigan, United States
Summary
👤
Senior🎓
Top SchoolAlbert Berahas is an Assistant Professor in Industrial and Operations Engineering at the University of Michigan with a decade of experience bridging optimization and machine learning through research, teaching, and mentorship. He earned a PhD in Engineering Sciences and Applied Mathematics from Northwestern and has held postdoctoral and instructional roles that reflect a steady trajectory from applied research to academic leadership. His work focuses on algorithmic foundations and practical optimization methods, bringing rigorous mathematical training to problems in ML and operations research. Based in Ann Arbor, he combines classroom experience dating back to early teaching roles with ongoing research that informs both theory and implementation. Notably, his career includes international teaching and diverse academic appointments that give him a broad perspective on training the next generation of engineers.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (PhD), Engineering Sciences and Applied Mathematics, Doctor of Philosophy (PhD), Engineering Sciences and Applied Mathematics at Northwestern UniversityBachelor of Science (BS), Operations Research and Industrial Engineering, Bachelor of Science (BS), Operations Research and Industrial Engineering at Cornell University
English, Greek
Github Skills (8)
newton-method8
optimization7
deep-learning4
constraint2
stochastic2
deterministic2
markov-decision-processes1
constraint-programming1
Programming languages (2)
Github contributions (4)
keskarnitish/minSQN
Mar 2016 - Feb 2017
Optimization using Stochastic quasi-Newton methods
Contributions:2 commits, 2 pushes in 10 months
newton-methodstochastic-quasi-newtonoptimizationoptimization-algorithms
OptMLGroup/SQN
Feb 2019 - Feb 2019
Sampled Quasi-Newton Methods for Deep Learning
Contributions:37 commits, 37 pushes in 2 days
deep-learningnewton-method
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