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Rohit Kannan

Assistant Professor
Rohit Kannan
233 Durham Hall
(MC 0118)
1145 Perry Street
Blacksburg, VA 24061
rohitkannan@vt.edu
google scholar
Curriculum Vitae
PERSONAL WEBSITE

For Prospective PhD Students: I am always excited to collaborate with talented and driven students! If you are highly motivated to pursue a Ph.D. in my lab and have a strong background in mathematics or operations research, please email me your resume and unofficial transcripts. I will reach out to students whose backgrounds align with current research opportunities.

  • Ph.D., Chemical Engineering, Massachusetts Institute of Technology, 2018
  • M.S., Chemical Engineering Practice, Massachusetts Institute of Technology, 2014
  • B.Tech., Chemical Engineering, Indian Institute of Technology Madras, 2012
  • Assistant Professor, Industrial and Systems Engineering, Virginia Tech, 2023 - present
  • Postdoctoral Associate, Center for Nonlinear Studies and Applied Mathematics & Plasma Physics, Los Alamos National Laboratory, 2021 - 2023
  • Postdoctoral Associate, Wisconsin Institute for Discovery, University of Wisconsin-Madison, 2018 - 2020
  • Learning + Optimization
  • Optimization Under Uncertainty
  • Global Optimization 
  • Computational Optimization 
  • Energy & Process Systems
  • R. Kannan, G. Bayraksan, and J. R. Luedtke (2024). “Data-Driven Sample Average Approximation with Covariate Information,” Forthcoming in Operations Research.
  • R. Kannan, G. Bayraksan, and J. R. Luedtke (2024). “Residuals-Based Distributionally Robust Optimization with Covariate Information,” Mathematical Programming, 207(1), 369-425.
  • A. Subramanian, R. Kannan, F. Holtorf, T. A. Adams II, T. Gundersen, and P. I. Barton (2023), “Optimization Under Uncertainty of a Hybrid Waste Tire and Natural Gas Feedstock Flexible Polygeneration System Using a Decomposition Algorithm,” Energy, 284, 129222, pp. 1-11.
  • E. M. Turan, J. Jäschke, and R. Kannan (2023). “Bounding-Focused Discretization Methods for the Global Optimization of Nonconvex Semi-Infinite Programs,” under review.
  • R. Kannan, H. Nagarajan, and D. Deka (2022). “Strong Partitioning and a Machine Learning Approximation for Accelerating the Global Optimization of Nonconvex QCQPs,” under review.
  • R. Kannan and J. R. Luedtke (2021). “A Stochastic Approximation Method for Approximating the Efficient Frontier of Chance-Constrained Nonlinear Programs,” Mathematical Programming Computation, 13, pp. 705–751.
  • R. Kannan, J. R. Luedtke, and L. A. Roald (2020). “Stochastic DC Optimal Power Flow with Reserve Saturation,” Electric Power Systems Research (special issue for the XXI Power Systems Computation Conference), pp. 1-9.
  • R. Kannan and P. I. Barton (2018). “Convergence-Order Analysis of Branch-and-Bound Algorithms for Constrained Problems,” Journal of Global Optimization, 71(4), pp. 753-813.
  • R. Kannan and P. I. Barton (2017). “The Cluster Problem in Constrained Global Optimization,” Journal of Global Optimization, 69(3), pp. 629-676.