Martin Arjovsky is a Research Scientist at DeepMind. Prior to that, he was a postdoc in SIERRA with Francis Bach, and before a PhD student at New York University, advised by Léon Bottou. He is from Buenos Aires, Argentina and received the BSc and MSc degrees from the University of Buenos Aires. He also spent time in different places (including Google, Facebook, Microsoft, Université de Montréal, and DeepMind). His master’s thesis advisor was Yoshua Bengio, who also advised him during his stay at UdeM. In general, he’s interested in the intersection between learning and mathematics, how we can ground the different learning processes that are involved in different problems, and leverage this knowledge to develop better algorithms. Along these lines, he’s worked in many different areas of machine learning, including optimization, unsupervised learning, out of distribution generalization, and exploration in reinforcement learning.