Danil Provodin

Danil Provodin

PhD Candidate

  • Research profile: Machine Learning and Sequential Decision-Making.
  • Research expertise: Bandits, reinforcement learning, stochastic optimization.


Danil is a Ph.D. candidate in JADS. He is broadly interested in statistical machine learning and sequential decision-making. In particular, his research focuses on bandit learning, reinforcement learning, stochastic optimization, and causal inference. He earned his master’s degree from the Higher School of Economics and has since gained extensive expertise in machine learning and data science. Danil has published several research papers at top-tier conferences in machine learning and has presented his work at various conferences and workshops. Furthermore, his experience also includes a research internship at Zalando, where he has contributed to the successful development of the bidding system based on his research. In addition to his research experience, Danil has served as a lecturer for the Causal Inference for Business Development course and supervised undergraduate and graduate students in their research projects.

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JADS also participates in several (international) projects, which contribute to grand societal challenges like health, food security, smart transport and secure societies. Together with companies, government, NGO’s and other knowledge institutions, JADS works on solutions by using data.

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