Albert S. Berahas

Dedicated to building bridges between disciplines in the realm of optimization and machine learning, Albert S. Berahas serves as an Assistant Professor in Industrial and Operations Engineering at the University of Michigan. With a keen interest in the intricacies of optimization, his research is centered on the design, development, and analysis of algorithms aimed at solving large-scale nonlinear optimization problems. His work spans a variety of areas, including general nonlinear optimization algorithms, optimization algorithms for machine learning, and constrained optimization. Albert's expertise extends to stochastic optimization, where he explores the unpredictable nature of data and its implications on algorithm performance. He is also deeply involved in derivative-free optimization, a field that seeks solutions without the need for gradient information, making it particularly useful in complex, real-world applications. His contributions to distributed optimization highlight his commitment to advancing computational efficiency and scalability in solving optimization problems. In addition to his research, Albert is passionate about teaching and mentoring the next generation of engineers and researchers. He integrates his research findings into the classroom, providing students with a comprehensive understanding of both theoretical and practical aspects of optimization. His dedication to education is reflected in his innovative teaching methods and his ability to inspire students to pursue excellence in their academic and professional endeavors. Albert's work is not only significant in the academic community but also has practical implications in various industries, including computing, statistics, and artificial intelligence. By pushing the boundaries of what is possible in optimization, he continues to contribute to the advancement of technology and its application in solving complex, real-world challenges.