Postdoctoral Research Associate
Stochastic and Global Optimization
Office: TA-3, Bldg 1690, Room 000
Mail Stop: B258
Phone: (505) 000-0000
Fax: (505) 665-2659
I am broadly interested in data-driven decision-making under uncertainty with application to energy and chemical process systems. My research interests include integrated machine learning and optimization under uncertainty, deterministic global optimization, and scalable solution methods for stochastic optimization.
- Ph.D. (2018) Chemical Engineering, MIT
- M.S. (2014) Chemical Engineering Practice, MIT
- B.Tech. (2012) Chemical Engineering, IIT Madras
- 2018-2020 Postdoctoral Research Associate, Wisconsin Institute for Discovery, UW-Madison
- Optimization under uncertainty
- Integrated learning and optimization
- Deterministic global optimization
- Applications in energy and chemical process systems
Selected Recent Publications:
- R. Kannan, G. Bayraksan, and J. R. Luedtke, Residuals-based distributionally robust optimization with covariate information, Under Review (2020).
- R. Kannan, G. Bayraksan, and J. R. Luedtke, Data-driven sample average approximation with covariate information, Under Review (2020).
- R. Kannan and J. R. Luedtke, A stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs, Forthcoming in Mathematical Programming Computation (2020).
- R. Kannan, J. R. Luedtke, and L. A. Roald, Stochastic DC optimal power flow with reserve saturation, XXI Power Systems Computation Conference (2020).
- R. Kannan and P. I. Barton, Convergence-order analysis of branch-and-bound algorithms for constrained problems, Journal of Global Optimization (2018).