Michal is primarily interested in designing algorithms that would require as little human supervision as possible. That is why he is working on methods and settings that are able to deal with minimal feedback, such as deep reinforcement learning, bandit algorithms, self-supervised learning, or self play. Michal has recently worked on representation learning, word models and deep (reinforcement) learning algorithms that have some theoretical underpinning. In the past he has also worked on sequential algorithms with structured decisions where exploiting the structure leads to provably faster learning. Michal is now working on large large models (LMMs), in particular providing algorithmic solutions for their scalable fine-tuning and alignment.

His interest in science began at the Grammar School of Alejová in Košice. In hindsight, he now realizes that the close cooperation between his grammar school and the UPJŠ Faculty of Science aroused his interest in science. He graduated from the Department of Artificial Intelligence and Mathematical Methods in Informatics at the Faculty of Mathematics, Physics and Informatics, Comenius University. He received his Ph.D. in 2011 from the University of Pittsburgh under the supervision of Miloš Hauskrecht and was a postdoc of Rémi Munos. From 2009 to 2010, he did internship at Intel in Silicon Valley, where he worked on designing autonomous systems to help the blind recognize other people’s faces. He got his tenure at Inria in 2012 and started Google DeepMind Paris in 2018. He has also worked on joint projects with Adobe, Technicolor and Microsoft Research.

Michal Valko is also a choir singer and is an active volunteer in working with seniors.