Machine Learning


umut.simsekli [at]

Short bio

I am a Research Faculty (Chargé de Recherche) at INRIA – SIERRA team and École Normale Supérieure, Computer Science Department. I received my PhD from Boğaziçi University, Computer Engineering Department in 2015. During 2016-2020, I was an associate professor at Télécom Paris, Institut Polytechnique de Paris and I spent one year as a visiting faculty at the University of Oxford, Department of Statistics during 2019-2020.

Topics of interest

Theory of Deep Learning, Markov Chain Monte Carlo.

Project in Prairie

My main research area is machine learning, including theory, algorithms, and applications. My ultimate goal has been to develop theory that closely follows practice and leads to methods that have impact on real problems. In particular, I have been interested in (i) analyzing deep learning methods from the lens of heavy-tailed dynamical systems theory, and (ii) implicit generative modeling algorithms by using tools from computational optimal transport.


Machine learning is a fascinating field, which continuously generates exciting and quite nontrivial theoretical, practical, and even societal/ethical questions. Attacking the questions from all these aspects simultaneously, the partial solutions offered by machine learning researchers have only posed additional questions and revealed many interesting and sometimes surprising “phenomena”. With such puzzling observations, I believe the machine learning of today should be treated as a natural science, rather than an engineering science, with many mysteries to be discovered and with many potential outcomes that might outreach its apparent scope.