Bio


Benjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His research focuses on understanding how an agent interacting with a poorly understood environment can learn over time to make effective decisions. He is interested in the design of efficient reinforcement learning algorithms, understanding what is possible or impossible in this domain, and applying the technology toward the benefit of society. 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-edits 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. 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, and the Management Science and Engineering Department's Graduate Teaching Award. 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)

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)

2019-20 Courses


Stanford Advisees


  • Doctoral Dissertation Reader (AC)
    Nikhil Garg, Chuan-Zheng Lee, Tong Mu, Carrie Wu
  • Orals Chair
    Jeremy Morton
  • Doctoral Dissertation Advisor (AC)
    Shi Dong, Vikranth Dwaracherla, Ravi Haksar, Xiuyuan Lu
  • Master's Program Advisor
    Gorish Aggarwal, Fang-I Hsiao, Tzu-Sheng Kuo, Stephen Palmieri
  • Doctoral (Program)
    Shane Barratt, Christopher Choy, Sanath Kumar Krishna Murthy

All Publications


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

  • 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
  • Learning to Optimize via Information-Directed Sampling OPERATIONS RESEARCH Russo, D., Van Roy, B. 2018; 66 (1): 230–52
  • 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
  • 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
  • An Information-Theoretic Analysis of Thompson Sampling JOURNAL OF MACHINE LEARNING RESEARCH Russo, D., Van Roy, B. 2016; 17