I am a staff scientist in Theoretical Division at Los Alamos National Laboratory.
CNLS, MS B258
Los Alamos National Laboratory
Los Alamos, NM 87545, United States
The most reliable way of contacting me: andrey.lokhov (at) gmail.com
Open positions:
- I am searching for postdocs and students interested in machine learning algorithms and applications for physics-rich problems, check the positions anouncement page
News and events:
- Stay tuned for the third edition of the Physics Informed Machine Learning (PIML 2020) workshop in Santa Fe, NM in January 2020. Previously, we organized and hosted PIML 2018 and PIML 2016.
- Our new paper on learning arbitrary discrete graphical models is out on arXiv
- Our paper “Optimal structure and parameter learning of Ising models” has been published in Science Advances
Research interests:
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.
Past:
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