Biography

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, on understanding the behavior of quantum computers, and on the development of new methods to control and optimize energy networks under uncertainty.

Machine Learning

• Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
  A. Dutt, A.Y. Lokhov, M. Vuffray, S. Misra,
  International Conference on Machine Learning (ICML), 2021, [online], [arXiv]

• Learning of Discrete Graphical Models with Neural Networks
  Abhijith J., A.Y. Lokhov, S. Misra, M. Vuffray
  Advances in Neural Information Processing Systems (NeurIPS), 2020, [online], [arXiv]

• Efficient Learning of Discrete Graphical Models
  M. Vuffray, S. Misra, A.Y. Lokhov
  Advances in Neural Information Processing Systems (NeurIPS), 2020, [online], [arXiv]

• Information Theoretic Optimal Learning of Gaussian Graphical Models
  S. Misra, M. Vuffray, A.Y. Lokhov
  Conference on Learning Theory (COLT), 2020, [online], [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]

Quantum Computing

• Programmable Quantum Annealers as Noisy Gibbs Samplers
  M. Vuffray, C. Coffrin, Y.A. Kharkov, A.Y. Lokhov
  PRX Quantum, 2022, [online], [arXiv]

• High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
  J. Nelson, M. Vuffray, A.Y. Lokhov, T. Albash, C. Coffrin
  Phys. Rev. Applied , 2022, [online], [arXiv]

• Single-Qubit Fidelity Assessment of Quantum Annealing Hardware
  J. Nelson, M. Vuffray, A.Y. Lokhov, C. Coffrin
  IEEE Transactions on Quantum Engineering, 2021, [online], [arXiv]

• The Potential of Quantum Annealing for Rapid Solution Structure Identification
  Y. Pang, C. Coffrin, A.Y. Lokhov, M. Vuffray
  Constraints, 2020, [online], [arXiv]

• Single-Qubit Cross Platform Comparison of Quantum Computing Hardware
  A. Suau, J. Nelson, M. Vuffray, A.Y. Lokhov, L. Cincio, C. Coffrin
  arXiv, 2021, [arXiv]

• Quantum Algorithm Implementations for Beginners
  J. Abhijith, …, S. Eidenbenz, P. J Coles, M. Vuffray, A.Y. Lokhov
  arXiv, 2018, [arXiv]

Energy Networks

• The Impacts of Convex Piecewise Linear Cost Formulations on AC Optimal Power Flow
  C. Coffrin, B. Knueven, J. Holzer, M. Vuffray
  Electric Power Systems Research, 2021, [online], [arXiv]

• Real-Time Anomaly Detection and Classification in Streaming PMU Data
  C. Hannon, D. Deka, D. Jin, M. Vuffray, A.Y. Lokhov
  IEEE Madrid PowerTech, 2021, [online], [arXiv]

• Efficient Polynomial Chaos Expansion for Uncertainty Quantification in Power Systems
  D. Metivier, M. Vuffray, S. Misra
  Power Systems Computation Conference (PSCC), 2020, [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]