Benjamin Van Roy
Professor of Electrical Engineering, of Management Science and Engineering and, by courtesy, of Computer Science
Bio
Benjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His current research focuses on reinforcement learning. Beyond academia, he leads a DeepMind Research team in Mountain View, and has also led research programs at Unica (acquired by IBM), Enuvis (acquired by SiRF), and Morgan Stanley.
He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he co-edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, and the INFORMS Journal on Optimization. He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tstitsiklis. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department's Graduate Teaching Award, and the Lanchester Prize. He was the plenary speaker at the 2019 Allerton Conference on Communications, Control, and Computing. He has held visiting positions as the Wolfgang and Helga Gaul Visiting Professor at the University of Karlsruhe, the Chin Sophonpanich Foundation Professor and the InTouch Professor at Chulalongkorn University, a Visiting Professor at the National University of Singapore, and a Visiting Professor at the Chinese University of Hong Kong, Shenzhen.
Academic Appointments
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Professor, Electrical Engineering
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Professor, Management Science and Engineering
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Professor (By courtesy), Computer Science
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Member, Bio-X
Honors & Awards
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Fellow, INFORMS (2015)
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Fellow, IEEE (2019)
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Lanchester Prize, INFORMS (2022)
Professional Education
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BS, Massachusetts Institute of Technology, Computer Science and Engineering (1993)
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MS, Massachusetts Institute of Technology, Electrical Engineering and Computer Science (1995)
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PhD, Massachusetts Institute of Technology, Electrical Engineering and Computer Science (1998)
2024-25 Courses
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Independent Studies (20)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390A (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390B (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390C (Aut, Win, Spr, Sum) - Directed Reading and Research
MS&E 408 (Aut, Win, Spr, Sum) - Independent Project
CS 399 (Aut, Win, Spr, Sum) - Independent Project
CS 399P (Aut, Win, Spr, Sum) - Independent Work
CS 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199P (Aut, Win, Spr, Sum) - Master's Thesis and Thesis Research
EE 300 (Aut, Win, Spr, Sum) - Part-time Curricular Practical Training
CS 390D (Aut, Win, Spr, Sum) - Programming Service Project
CS 192 (Aut, Win, Spr, Sum) - Senior Project
CS 191 (Aut, Win, Spr, Sum) - Special Studies and Reports in Electrical Engineering
EE 191 (Aut, Win, Spr, Sum) - Special Studies and Reports in Electrical Engineering
EE 391 (Aut, Win, Spr, Sum) - Special Studies and Reports in Electrical Engineering (WIM)
EE 191W (Aut, Win, Spr, Sum) - Special Studies or Projects in Electrical Engineering
EE 190 (Aut, Win, Spr, Sum) - Special Studies or Projects in Electrical Engineering
EE 390 (Aut, Win, Spr, Sum) - Writing Intensive Senior Research Project
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
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Prior Year Courses
2023-24 Courses
- Aligning Superintelligence
MS&E 338 (Spr) - Bandit Learning: Behaviors and Applications
EE 277, MS&E 237A (Aut) - Reinforcement Learning: Behaviors and Applications
EE 370, MS&E 237B (Win)
2022-23 Courses
- Reinforcement Learning: Behaviors and Applications
EE 277, MS&E 237 (Aut) - Reinforcement Learning: Frontiers
MS&E 338 (Spr)
2021-22 Courses
- Reinforcement Learning: Behaviors and Applications
EE 277, MS&E 237 (Aut) - Reinforcement Learning: Frontiers
MS&E 338 (Spr)
- Aligning Superintelligence
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Yuwei Luo, Fernando Rodriguez Silva Santisteban -
Doctoral Dissertation Advisor (AC)
Hong Jun Jeon, Anmol Kagrecha, Saurabh Kumar, Wanqiao Xu -
Master's Program Advisor
Huafan Cai, Stefano Delmanto, Yicheng Fu, Nachat Jatusripitak, Deepanjan Kundu -
Doctoral Dissertation Co-Advisor (AC)
Rui Yan -
Doctoral (Program)
Hong Jun Jeon, Saurabh Kumar, Henrik Marklund, Yifan Zhu
All Publications
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Deciding What to Learn: A Rate-Distortion Approach
JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021
View details for Web of Science ID 000683104600035
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Deep Exploration via Randomized Value Functions
JOURNAL OF MACHINE LEARNING RESEARCH
2019; 20
View details for Web of Science ID 000487068900008
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Information-Theoretic Confidence Bounds for Reinforcement Learning
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
View details for Web of Science ID 000534424302046
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A Tutorial on Thompson Sampling
FOUNDATIONS AND TRENDS IN MACHINE LEARNING
2018; 11 (1): 1–96
View details for DOI 10.1561/2200000070
View details for Web of Science ID 000438444300001
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Scalable Coordinated Exploration in Concurrent Reinforcement Learning
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461823304025
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An Information-Theoretic Analysis for Thompson Sampling with Many Actions
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461823304019
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Learning to Optimize via Information-Directed Sampling
OPERATIONS RESEARCH
2018; 66 (1): 230–52
View details for DOI 10.1287/opre.2017.1663
View details for Web of Science ID 000426081800015
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Conservative Contextual Linear Bandits
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
View details for Web of Science ID 000452649403094
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Ensemble Sampling
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
View details for Web of Science ID 000452649403032
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An Information-Theoretic Analysis of Thompson Sampling
JOURNAL OF MACHINE LEARNING RESEARCH
2016; 17
View details for Web of Science ID 000391522800001