Recent News

Sept. 2023: Joint work with Avinash on Optimization under uncertainty of a hybrid waste tire and natural gas feedstock flexible polygeneration system has been accepted by Energy! [Paper]

Aug. 2023: Excited to start as an Assistant Professor of Operations Research in the Grado Department of Industrial and Systems Engineering at Virginia Tech!

Aug. 2023: Joint work with Guzin and Jim on Residuals-based DRO with covariate information has been accepted by Mathematical Programming! [Paper]

July 2023: Invited talk on Data-Driven Multistage Stochastic Optimization on Time Series at the 2023 XVI International Conference on Stochastic Programming (ICSP).

June 2023: Excited to begin working with Erin George on accelerating the global solution of quadratically-constrained quadratic programs using graph-based machine learning as part of the 2023 LANL Applied ML Summer Research Fellowship Program! Welcome, Erin!

May 2023: Invited talk on Learning to Accelerate the Global Optimization of QCQPs at the 2023 SIAM Conference on Optimization (OP23).

Feb. 2023: Check out my new preprint with Evren and Johannes on Optimality-Based Discretization Methods for Nonconvex Semi-Infinite Programs! [Preprint]

Dec. 2022: New preprint with Harsha and Deep on Learning to Accelerate the Global Optimization of Nonconvex QCQPs is up on arXiv! [Preprint]

Dec. 2022: Happy to announce that my proposal with Harsha and Deep on Using Graph Neural Networks to Accelerate Solutions to Nonconvex Optimization Problems will be funded by the 2023 LANL Applied ML Summer Research Fellowship Program.

Oct. 2022: Excited to begin working as a Co-Investigator on the LANL LDRD Exploratory Research project Learning to Accelerate Global Solutions for Non-Convex Optimization.

July 2022: Invited talk on Data-Driven Multistage Stochastic Optimization on Time Series at the 2022 International Conference on Continuous Optimization (ICCOPT).

May 2022: Excited to begin working with Mithun Goutham on using multistage stochastic programming to model the resilience of the power grid to hurricanes! Welcome, Mithun!

Projects

Using ML to Accelerate Global Optimization

This project explores the use of ML to predict optimal instance-specific heuristic parameters within global optimization algorithms without sacrificing global optimality guarantees. We have devised algorithms to optimally partition variable domains for piecewise convex relaxation of nonconvex problems. Our ML algorithms are able to accelerate the solution of challenging problems by up to three orders of magnitude for specific instances and families of nonconvex problems by an order of magnitude on average!

Enhancing the Resilience of the Power Grid

This project aims to leverage ensemble forecasts of extreme weather events (such as hurricanes and winter storms) to make proactive grid operation decisions and mitigate power outages. We model the operational resilience of the grid using multistage stochastic programming and use stochastic dual dynamic programming to joinly optimize anticipative and restorative actions. Our preliminary results show that using hurricane forecasts to make proactive decisions can reduce load shed by up to 5% on average!

Section Under Construction!

Papers

* = Graduate Student

Working Papers

  1. R. Kannan, H. Nagarajan, and D. Deka.
    Stochastic Unit Commitment with Reserve Saturation.
  2. R. Kannan, N. Ho-Nguyen, and J. R. Luedtke.
    Data-Driven Multistage Stochastic Optimization on Time Series.
    [Slides]
  3. R. Kannan and P. I. Barton.
    GOSSIP: Decomposition Software for the Global Optimization of Nonconvex Two-Stage Stochastic Mixed-Integer Nonlinear Programs.
    [Slides]
  4. R. Kannan and P. I. Barton.
    Integrating Benders Decomposition and Lagrangian Relaxation for Solving Two-Stage Stochastic Mixed-Integer Nonlinear Programs.

Submitted Papers

  1. M. Goutham*, R. Kannan, D. Deka, H. Nagarajan, and R. Bent.
    Operational Resilience Enhancement of Electric Grids using Uncertain Hurricane Forecasts.
  2. E. M. Turan*, J. Jäschke, and R. Kannan (2023).
    Optimality-Based Discretization Methods for the Global Optimization of Nonconvex Semi-Infinite Programs, pp. 1-22. Status: Under Review.
    [Preprint] [Slides]
  3. R. Kannan, D. Deka, and H. Nagarajan (2023).
    Learning to Accelerate the Global Optimization of Quadratically-Constrained Quadratic Programs, pp. 1-27. Status: Under Review.
    [Preprint] [Slides]
  4. R. Kannan, G. Bayraksan, and J. R. Luedtke (2020).
    Data-driven sample average approximation with covariate information, pp. 1-57. Status: Under Revision.
    [Preprint] [Slides] [Poster] [Code]

Peer-Reviewed Journal Papers

  1. A. Subramanian*, R. Kannan, F. Holtorf, T. Adams, 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, Forthcoming in Energy, pp. 1-28. [Journal] [Preprint]
  2. R. Kannan, G. Bayraksan, and J. R. Luedtke (2023).
    Residuals-based distributionally robust optimization with covariate information, Forthcoming in Mathematical Programming, pp. 1-57.
    [Journal] [Preprint] [Slides] [Code]
  3. 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, 705–751.
    [Journal] [Preprint] [Slides] [Poster] [Code]

  4. 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.
    [Journal] [Preprint] [Slides] [Code]

  5. 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.
    [Journal] [PDF] [Slides]

  6. R. Kannan and P. I. Barton (2017).
    The cluster problem in constrained global optimization, Journal of Global Optimization, 69(3), pp. 629-676.
    [Journal] [PDF] [Slides]

  7. R. Kannan and A. K. Tangirala (2014).
    Correntropy-based partial directed coherence for testing multivariate Granger causality in nonlinear processes, Physical Review E, 89(6), 062144, pp. 1-15.
    [Journal] [PDF]

Peer-Reviewed Conference Papers

  1. E. M. Turan*, R. Kannan, and J. Jäschke (2022).
    Design of PID controllers using semi-infinite programming, Proceedings of the 14th International Symposium on Process Systems Engineering, pp. 439-444.
    [Conference] [Preprint]

  2. R. Kannan and P. I. Barton (2016).
    The cluster problem in constrained global optimization, Proceedings of the XIII Global Optimization Workshop (GOW’16), pp. 9-12.
    [Conference]

Technical Reports

  1. R. Kannan, G. Bayraksan, and J. R. Luedtke.
    Heteroscedasticity-aware residuals-based contextual stochastic optimization, pp. 1-15.
    [Preprint]

Theses

  1. Ph.D. (2018): Algorithms, analysis and software for the global optimization of two-stage stochastic programs, MIT.
    [DSpace@MIT] [PDF]

  2. B.Tech. (2012): Partial directed coherence for nonlinear Granger causality: A generalized correlation function-based approach, IIT Madras.

Collaborators

I have been fortunate to work with several excellent collaborators over the years.

Students

Avinash Subramanian NTNU
Erin George UCLA
Evren Turan NTNU
Mithun Goutham OSU

Scientists

Deepjyoti Deka LANL
Harsha Nagarajan LANL
Russell Bent LANL

Faculty

Arun Tangirala IIT Madras
Güzin Bayraksan OSU
James Luedtke UW-Madison
Johannes Jäschke NTNU
Line Roald UW-Madison
Nam Ho-Nguyen USYD
Paul Barton MIT

Teaching & Outreach

Instructor, Dept. of Industrial and Systems Engineering, Virginia Tech, 2023-present

  • ISE 6514: Advanced Math Programming, Fall 2023 (11 students)
    Syllabus: convex analysis, optimality conditions, duality, linear programming, decomposition methods, conic programming (emphasis on SOCPs, SDPs), solution approaches, applications in OR, energy, ML.

Teaching Assistant, Dept. of Chemical Engineering, MIT, 2015

  • Teaching assistant for the graduate Chemical Reactor Engineering course (50 students)
  • Shared responsibility for office hours, online discussion forums, homeworks, and grades

Math Lecturer for the IIT Joint Entrance Exam (IIT JEE), MIT, 2016

  • Recorded online video lectures for the entrance exam to the IITs as part of an MIT team
  • Featured on MIT OpenCourseWare and supported by the MIT Office of Digital Learning

Math Olympiad Trainer, Science & Math Academy for Real Talents, India, 2008-2011

  • Coached 30 middle-school and high-school students each year for the Math Olympiad
  • One trainee was selected for the 2010 International Math Olympiad Training Camp

Volunteer, National Services Scheme, IIT Madras, 2008-2009

  • Designed and demonstrated science experiments to students in underprivileged schools

Videos recorded as a Math Lecturer for the IIT JEE

Selected Talks

Check out my CV for other talks I have given.

Plenary

  • A Stochastic Approximation Method for Approximating the Efficient Frontier of Chance-Constrained Nonlinear Programs [Slides]
    CAST Division Plenary, AIChE Annual Meeting, Nov. 2021.
  • GOSSIP: Decomposition Software for the Global Optimization of Nonconvex Two-Stage Stochastic Mixed-Integer Nonlinear Programs,
    CAST Division Plenary, AIChE Annual Meeting, Nov. 2016.

Invited

  • Data-Driven Multistage Stochastic Optimization on Time Series
    XVI International Conference on Stochastic Programming (ICSP), July 2023.
  • Learning to Accelerate the Global Optimization of QCQPs
    SIAM Conference on Optimization (OP23), May 2023.
  • Learning-Assisted Data-Driven Optimization [Slides]
    Grado Department of Industrial and Systems Engineering, Virginia Tech, Feb. 2023.
  • Integrating Time Series Predictions Within Multistage Stochastic Optimization
    INFORMS Annual Meeting, Oct. 2022.
  • Data-Driven Multistage Stochastic Optimization on Time Series
    Seventh International Conference on Continuous Optimization (ICCOPT), July 2022.
  • Residuals-Based Distributionally Robust Optimization with Covariate Information
    INFORMS Annual Meeting, Oct. 2021.
  • Data-Driven Sample Average Approximation with Covariate Information [Slides]
    INFORMS Annual Meeting, Nov. 2020.
  • Stochastic DC Optimal Power Flow With Reserve Saturation [Slides]
    INFORMS Annual Meeting, Nov. 2020.
  • Predict, then Smart Optimize with Stochastic Programming [Slides]
    IPAM Workshop on Intersections between Control, Learning & Optimization, Feb. 2020.
  • GOSSIP: decomposition software for the Global Optimization of nonconvex two-Stage Stochastic mixed-Integer nonlinear Programs [Slides]
    INFORMS Annual Meeting, Nov. 2018.
  • Convergence-Order Analysis of Lower Bounding Schemes for Constrained Global Optimization Problems [Slides]
    Fifth International Conference on Continuous Optimization (ICCOPT), Aug. 2016.

Contributed

  • Learning to Accelerate the Global Solution of QCQPs [Slides]
    AIChE Annual Meeting, Nov. 2022.
  • Tighter Lower Bounds for SIPs Using Parametric Sensitivity Theory
    AIChE Annual Meeting, Nov. 2022.
  • Data-Driven Multistage Stochastic Optimization on Time Series
    AIChE Annual Meeting, Nov. 2021.
  • Data-Driven Sample Average Approximation with Covariate Information
    AIChE Annual Meeting, Nov. 2021.
  • Residuals-Based DRO with Covariate Information [Slides]
    Robust Optimization Webinar, April 2021.
  • The Cluster Problem in Constrained Global Optimization
    Thirteenth Global Optimization Workshop, Aug. 2016.

Service

Student Mentorship

Committees and Editorial Service

Invited External Examiner for the following students:

Invited Peer-Reviewer for the following journals/conferences (Reviewer Profile):

  • Operations Research
  • Mathematical Programming
  • SIAM Journal on Optimization
  • Journal of Global Optimization
  • Optimization Letters
  • Set-Valued & Variational Analysis
  • INFORMS Journal on Computing
  • Electric Power Systems Research
  • American Control Conference
  • Journal of Optimization Theory and Applications
  • Computational Optimization and Applications
  • Optimization Methods and Software
  • Mathematics of Operations Research
  • Computers and Chemical Engineering
  • INFORMS Journal on Optimization
  • Industrial and Engineering Chemistry Research
  • IEEE Transactions on Control Systems Technology

Professional and Institutional Service