I’m a staff research scientist in the Theoretical Division at the Los Alamos National Laboratory (LANL), New Mexico, where I am part of the Advanced Network Science Initiative (ANSI) as well as the Condensed Matter and Complex Systems Group (T-4). My background is in statistical physics and information theory.
My current work focuses on the design of machine learning techniques for learning probabilistic networks and on the development of new methods to control and optimize energy networks under uncertainty.
PhD in Computer and Communication Sciences, 2014
EPFL – École Polytechnique Fédérale de Lausanne
MSc in Physics, 2008
EPFL – École Polytechnique Fédérale de Lausanne
BSc in Physics, 2006
EPFL – École Polytechnique Fédérale de Lausanne
Efficient Learning of Discrete Graphical Models
M. Vuffray, S. Misra, A.Y. Lokhov
arXiv, 2019, [arXiv]
Information Theoretic Optimal Learning of Gaussian Graphical Models
S. Misra, M. Vuffray, A.Y. Lokhov
arXiv, 2019, [arXiv]
Optimal Structure and Parameter Learning of Ising Models
A.Y. Lokhov, M. Vuffray, S. Misra, M. Chertkov
Science Advances, 2018, [online], [arXiv]
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
M. Vuffray, S. Misra, A.Y. Lokhov, M. Chertkov
Advances in Neural Information Processing Systems (NeurIPS), 2016, [online], [arXiv]
Uncovering Power Transmission Dynamic Model from Incomplete PMU Observations
A.Y. Lokhov, D. Deka, M. Vuffray, M. Chertkov
IEEE Conference on Decision and Control (CDC), 2018, [online]
Online Learning of Power Transmission Dynamics
A.Y. Lokhov, M. Vuffray, D. Shemetov, D. Deka, M. Chertkov
Power Systems Computation Conference (PSCC), 2018, [online], [arXiv]
Graphical Models for Optimal Power Flow
K. Dvijotham, M. Chertkov, P. Van Hentenryck, M. Vuffray, S. Misra
Constraints, 2017, [online], [arXiv]
Monotonicity of Dissipative Flow Networks Renders Robust Maximum Profit Problem Tractable: General Analysis and Application to Natural Gas Flows
M. Vuffray, S. Misra, M. Chertkov
IEEE Conference on Decision and Control (CDC), 2015, [online], [arXiv]
The Bethe Free Energy Allows to Compute the Conditional Entropy of Graphical Code Instances: A Proof From the Polymer Expansion
N. Macris, M. Vuffray
IEEE Transactions on Information Theory, 2016, [online], [arXiv]
Approaching the Rate-Distortion Limit with Spatial Coupling, Belief Propagation, and Decimation
V. Aref, N. Macris, M. Vuffray
IEEE Transactions on Information Theory, 2015, [online], [arXiv]