Daniel Pirutinsky

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.

He earned his 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.

His 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.

Research Interests

• Primary: Bandit Problems, Markov Decision Processes, Stochastic Processes, Applied Probability, Reinforcement Learning

• Secondary: Discrete Optimization, Mathematical Games, Machine Learning

Skills

• Mathematics: Optimization, Machine Learning, Optimal Control, Applied Mathematical Modeling

• Programming: Python, R, AMPL, LATEX, VBA, Visual Basic, JavaScript

• Languages: English, Hebrew, Yiddish

Education

• Doctor of Philosophy (Ph.D.) Rutgers Business School (2015 — 2020)
• Bachelor of Science Excelsior College (2007 — 2009)

Companies

• Assistant Teaching Professor University of California, Berkeley (2020)
• Adjunct Professor Rutgers Business School (2018 — 2020)
• Teaching Assistant PCS Agudah (2018 — 2019)
• Teaching Assistant Rutgers Business School (2015 — 2018)
• Adjunct Professor of Computer Science Yeshiva at IDT (2015 — 2015)
• Research Assistant Rutgers University (2014 — 2014)
• Legal and Business Analyst Business Licenses, LLC (2008 — 2009)
• IT Administrator Feldheim Publishers (2007 — 2008)