Daniel (dah-NEE-yell) Pirutinsky is an Assistant Teaching Professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley.
They earned their Ph.D. in Operations Research at Rutgers University in October 2020, and joined the IEOR Department in Fall 2020.
His focus is primarily on educating IEOR’s growing student population and developing effective pedagogical techniques that allow a wider range of students to succeed.
Their current research is on bridging the theoretical gap between provable optimal Reinforcement Learning algorithms which are mainly of limited practical use and those with seemingly empirically good success but with weak, if any, theoretical guarantees.