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an American computer scientist and full professor at the [[University of Alberta]] with research focusing on [[Artificial Intelligence|artificial intelligence]], [[Learning|machine learning]], [[Games|games]], and [[Robots|robotics]]. He received his Ph.D. on [https://en.wikipedia.org/wiki/Multi-agent_system multiagent] learning under advisor [[Manuela Veloso]] at [[Carnegie Mellon University]], where he was involved in the [https://en.wikipedia.org/wiki/RoboCup RoboCup] initiative. At UofA, he is leader of the ''Computer Poker Research Group'' <ref>[http://poker.cs.ualberta.ca/ University of Alberta Computer Poker Research Group]</ref>, recently applying [[Deep Learning|deep learning]] to the game of [[Poker]] with the AI agent ''DeepStack'' <ref>[[Matej Moravčík]], [[Mathematician#MSchmid|Martin Schmid]], [[Neil Burch]], [[Viliam Lisý]], [[Dustin Morrill]], [[Nolan Bard]], [[Trevor Davis]], [[Kevin Waugh]], [[Michael Johanson]], [[Michael Bowling]] ('''2017'''). ''[http://science.sciencemag.org/content/356/6337/508 DeepStack: Expert-level artificial intelligence in heads-up no-limit poker]''. [https://en.wikipedia.org/wiki/Science_(journal) Science], Vol. 356, No. 6337</ref> <ref>[https://www.deepstack.ai/ DeepStack]</ref> <ref>[https://github.com/lifrordi/DeepStack-Leduc GitHub - lifrordi/DeepStack-Leduc: Example implementation of the DeepStack algorithm for no-limit Leduc poker]</ref>.

* [[Matej Moravčík]], [[Mathematician#MSchmid|Martin Schmid]], [[Neil Burch]], [[Viliam Lisý]], [[Dustin Morrill]], [[Nolan Bard]], [[Trevor Davis]], [[Kevin Waugh]], [[Michael Johanson]], [[Michael Bowling]] ('''2017'''). ''[http://science.sciencemag.org/content/356/6337/508 DeepStack: Expert-level artificial intelligence in heads-up no-limit poker]''. [https://en.wikipedia.org/wiki/Science_(journal) Science], Vol. 356, No. 6337==2020 ...==* [[Finbarr Timbers]], [[Edward Lockhart]], [[Mathematician#MSchmid|Martin Schmid]], [[Marc Lanctot]], [[Michael Bowling]] ('''2020'''). ''Approximate exploitability: Learning a best response in large games''. [https://arxiv.org/abs/2004.09677 arXiv:2004.09677]* [[Samuel Sokota]], [[Edward Lockhart]], [[Finbarr Timbers]], [[Elnaz Davoodi]], [[Ryan D'Orazio]], [[Neil Burch]], [[Mathematician#MSchmid|Martin Schmid]], [[Michael Bowling]], [[Marc Lanctot]] ('''2021'''). ''Solving Common-Payoff Games with Approximate Policy Iteration''. [https://arxiv.org/abs/2101.04237 arXiv:2101.04237]