The most reliable way of contacting me: andrey.lokhov (at) gmail.com
Open positions: check the positions anouncement page
News and events:
- I am co-organizing an IMA workshop “Theory and Algorithms in Graph-Based Learning” to be held online on September 14-18 2020.
- Our paper on Information-Theoretic Learning of Gaussian Graphical Models has been presented at COLT 2020.
- Check out the latest version of our review on Implementation of Quantum Algorithms.
- On January 13-17, 2020, we organized the third edition of the Physics Informed Machine Learning (PIML 2020) workshop in Santa Fe, NM. Previously, we organized and hosted PIML 2018 and PIML 2016.
- Our demonstration of real-time anomaly detection and classification in streaming data from power grids has been presented at NeurIPS 2019.
My research interests lie at the intersection of statistical physics, computer science and machine learning. I develop new statistical physics inspired algorithms to deal with hard problems in graphical models, machine learning, complex systems, quantum computing and data mining. I am also interested in general optimization, inverse, combinatorial and out-of-equilibrium problems on graphs. Check out the research higlights for details.
2015-2017 Postdoctoral fellow in the Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, United States
2011-2014 PhD in statistical physics at Laboratoire de Physique Théorique et Modèles Statistiques, Université Paris-Sud, Orsay, France
PhD thesis: Dynamic cavity method and problems on graphs, under supervision of Marc Mézard, defended on November 14, 2014
2010-2011 Master in theoretical physics at M2 CFP Physique Théorique, École Normale Supérieure, Paris, France
Master thesis: Physics of heavy flavors in QCD, under supervision of Gregory Korchemsky, defended in September 2011
2008-2011 Undergraduate studies at École Polytechnique, Palaiseau, France
2005-2011 Undergraduate studies at Novosibirsk State University, Novosibirsk, Russia