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

Honors & Awards

  • Fellow, INFORMS (2015)
  • Fellow, IEEE (2019)
  • Lanchester Prize, INFORMS (2022)

Professional Education

  • BS, Massachusetts Institute of Technology, Computer Science and Engineering (1993)
  • MS, Massachusetts Institute of Technology, Electrical Engineering and Computer Science (1995)
  • PhD, Massachusetts Institute of Technology, Electrical Engineering and Computer Science (1998)

Contact

  • Academic
    bvr@stanford.edu
    University - Faculty Department: Electrical Engineering Position: Professor
    University - Faculty Department: Management Science and Engineering Position: Professor
    • Packard 273
    • Stanford,  California  94305 

Additional Info

  • Mail Code: 4026

2024-25 Courses

Stanford Advisees

All Publications

  • Deciding What to Learn: A Rate-Distortion Approach Arumugam, D., Van Roy, B., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104600035

  • Deep Exploration via Randomized Value Functions JOURNAL OF MACHINE LEARNING RESEARCH Osband, I., Van Roy, B., Russo, D. J., Wen, Z. 2019; 20

    View details for Web of Science ID 000487068900008

  • Information-Theoretic Confidence Bounds for Reinforcement Learning Lu, X., Van Roy, B., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000534424302046

  • A Tutorial on Thompson Sampling FOUNDATIONS AND TRENDS IN MACHINE LEARNING Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., Wen, Z. 2018; 11 (1): 1–96

    View details for DOI 10.1561/2200000070

    View details for Web of Science ID 000438444300001

  • Scalable Coordinated Exploration in Concurrent Reinforcement Learning Dimakopoulou, M., Osband, I., Van Roy, B., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

    View details for Web of Science ID 000461823304025

  • An Information-Theoretic Analysis for Thompson Sampling with Many Actions Dong, S., Van Roy, B., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

    View details for Web of Science ID 000461823304019

  • Learning to Optimize via Information-Directed Sampling OPERATIONS RESEARCH Russo, D., Van Roy, B. 2018; 66 (1): 230–52

    View details for DOI 10.1287/opre.2017.1663

    View details for Web of Science ID 000426081800015

  • Conservative Contextual Linear Bandits Kazerouni, A., Ghavamzadeh, M., Abbasi-Yadkori, Y., Van Roy, B., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

    View details for Web of Science ID 000452649403094

  • Ensemble Sampling Lu, X., Van Roy, B., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

    View details for Web of Science ID 000452649403032

  • An Information-Theoretic Analysis of Thompson Sampling JOURNAL OF MACHINE LEARNING RESEARCH Russo, D., Van Roy, B. 2016; 17

    View details for Web of Science ID 000391522800001