This fall, U-M Industrial and Operations Engineering (IOE) welcomes Albert S. Berahas to the department as an assistant professor.
Berahas’ research broadly focuses on designing, analyzing and implementing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous and arise in many areas such as engineering design, economics, transportation, robotics, machine learning and statistics. More specifically, the goal of his research is to develop scalable and robust optimization algorithms with sound theoretical properties and good practical performance. The development of such algorithms can help solve even larger and more complex optimization problems that arise in science, engineering and other fields.
At U-M IOE, Berahas plans to continue his research on developing next generation algorithms for nonlinear optimization. He is very interested in developing collaborations with faculty in the department and establishing new interdisciplinary collaborations.
“I am delighted to be joining IOE,” Berahas said. “I look forward to meeting and interacting with the research community at U-M, and to establishing new interdisciplinary collaborations. I am also very excited about teaching courses to undergraduate and graduate students. I would like to thank the department and U-M for this great opportunity.”
Berahas has a Bachelor of Science in Operations Research and Engineering from Cornell University and completed both his master’s and doctoral degrees at Northwestern University in Engineering Sciences and Applied Mathematics. His dissertation was focused on “Methods for Large Scale Nonlinear and Stochastic Optimization.”
While earning his PhD at Northwestern, Berahas served as a graduate research assistant and as a teaching assistant. Following graduation, Berahas became a postdoctoral research fellow at Northwestern before joining Lehigh University’s Industrial and Systems Engineering Department in the same capacity. Berahas has served Lehigh University for the past two years.
Berahas has a passion for teaching. He has teaching experience at both the undergraduate and graduate levels, and has been recognized for his excellence in teaching. At U-M he intends to develop new courses at the intersection of optimization, data science and machine learning for undergraduate and graduate students.
“In the Big Data era, where machine learning has become a part of our everyday lives, it is important to train our students to understand and be able to use these technologies, and to build their mathematical foundations and develop their quantitative and problem solving skills in order to prepare them for their future careers,” he said.
“A favorite quote of mine from Nikos Kazantzakis guides my teaching style: ‘True teachers are those who use themselves as bridges over which they invite their students to cross; then, having facilitated their crossing, joyfully collapse, encouraging them to create their own.’”