Michał Dereziński

Michał Dereziński has led transformative projects in the realm of computer science, particularly focusing on the theoretical foundations of randomized algorithms for machine learning, optimization, and data science. As an Assistant Professor of Computer Science and Engineering at the University of Michigan, he is dedicated to advancing the understanding and application of these complex algorithms. His academic journey began with a Ph.D. in Computer Science from the University of California, Santa Cruz, where he honed his expertise in the field. Before joining the University of Michigan, Michał held prestigious positions at the University of California, Berkeley, and the Simons Institute for the Theory of Computing. These roles allowed him to collaborate with leading experts and contribute to cutting-edge research in computer science. His work is characterized by a deep interest in the foundations of machine learning and optimization, as well as in the intricate areas of randomized linear algebra and high-dimensional statistics. Michał's research is particularly focused on the application of random matrix theory, which plays a crucial role in understanding the behavior of complex systems in high-dimensional spaces. His contributions to the field have been recognized through numerous publications and presentations at international conferences. Through his work, Michał aims to develop innovative solutions that address the challenges posed by large-scale data analysis and machine learning. In addition to his research, Michał is passionate about teaching and mentoring the next generation of computer scientists. He is committed to fostering a collaborative and inclusive environment for students and researchers alike, encouraging them to explore new ideas and push the boundaries of what is possible in the field of computer science.