Cédric Josz

ASSISTANT PROFESSOR OF INDUSTRIAL ENGINEERING AND OPERATIONS RESEARCH

Research Interests

Polynomial optimization, Optimal power flow, Semidefinite programming, Sum-of-squares, Local search algorithms

Research Areas

Data Science

Of particular interest to Josz is the development of modern techniques for non-convex optimization, which encompasses many challenging problems in operations research and data science. He applies these techniques to deal with the increasing complexity of power grids, given the advent of renewable energy and electric vehicles. He is also developing new approaches to overcome non-convexity in large-scale machine learning problems.

Josz was a postdoctoral scholar at the University of California, Berkeley, in 2017-2019, and at the French National Center for Scientific Research (CNRS) Toulouse in 2016-2017. He received his PhD in applied mathematics in 2016 from the University of Paris VI in collaboration with the French transmission system operator and the French Institute for Research in Computer Science and Automation (INRIA). He received the 2016 Best Paper Award in Springer Optimization Letters and was a finalist of the competition for best PhD thesis of 2017 organized by French Agency for Mathematics in Interaction with Industry and Society.

RESEARCH EXPERIENCE

  • Postdoctoral scholar, University of California, Berkeley, 2017-2019
  • Postdoctoral scholar, LAAS CNRS, Toulouse, 2016-2017

PROFESSIONAL EXPERIENCE

  • Assistant professor of industrial engineering and operations research, Columbia University, 2019–

HONORS & AWARDS

  • 2016 Best Paper Award in Springer Optimization Letters
  • Finalist of competition for best PhD thesis of 2017 organized by French Agency for Mathematics in Interaction with Industry and Society

SELECTED PUBLICATIONS

  • C. Josz, D. K. Molzahn, “Lasserre hierarchy for large scale polynomial optimization in real and complex variables,” SIAM Journal on Optimization, 28, 2 (2018).
  • C. Josz, D. Henrion, “Strong duality in Lasserre's hierarchy for polynomial optimization,” Springer, Optimization Letters, 10, 1 (2016).
  • C. Josz, Y. Ouyang, R. Y. Zhang, J. Lavaei, S. Sojoudi, “A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization,” NeurIPS (2018).
  • C. Josz, J. B. Lasserre, B. Mourrain, “Sparse polynomial interpolation: compressed sensing, super resolution, or Prony?,” Advances in Computational Mathematics, 45, 3 (2019).