Harsha Gangammanavar, Ph.D.

Associate Professor of Operations Research & Engineering Management

Office Location: Caruth 331

Education

Ph.D. in Operations Research from the Ohio State University
M.S. in Electrical Engineering from the Ohio State University
B.E in Electronics and Communication from Visvesvaraya Technological University

Biography

Harsha Gangammanavar is an Associate Professor in Operations Research and Engineering Management and an affiliated Data Science Institute faculty member at ��ϲ�Ԥ��. His research focuses on optimization under uncertainty, including developing models for large-scale infrastructure systems, designing algorithms for stochastic programming and distributionally robust optimization that combine decomposition and sampling, and developing scalable and general-purpose optimization solvers. Grants from the Air Force Office of Scientific Research, Office of Naval Research, and the Office of Science at the Department of Energy support his research. He received his Ph.D. in Operations Research and M.S. in Electrical Engineering from the Ohio State University and a B.E. in Electronics and Communication Engineering from Visvesvaraya Technological University, India.

Honors & Awards

Student award: INFORMS Undergraduate Student Paper (2021)
Student award: INFORMS Minority Affairs Poster competition (2016)

Research

Stochastic programming: models and stochastic optimization algorithms
Large-scale computational optimization
Applications of optimization in infrastructure systems, healthcare, and wireless communication.

Publications

H. Gangammanavar and M. Bansal, Stochastic Decomposition Method for Two-Stage Distributionally Robust Linear Optimization, SIAM Journal on Optimization, 32:3, 1901-1930, 2022.
S. Atakan, H. Gangammanavar, and S. Sen, Towards a Sustainable Power Grid: Stochastic Hierarchical Planning for High Renewable Integration, European Journal of Operational Research, Volume 302, Issue 1, p. 381-391, October 2022.
D. Troxell, M. Ahn, and H. Gangammanavar, A Cardinality Minimization Approach to Security-Constrained Economic Dispatch, IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3642-3652, Sept. 2022.
N. Sakhavand and H. Gangammanavar, Subproblem sampling vs. scenario reduction: Efficacy comparison for stochastic programs in power systems applications, Energy Systems, 2022.
S. Ariyarathne, H. Gangammanavar, and R. Sundararajan Change Point Detection in Nonstationary Sub-Hourly Wind Time Series, Applied Energy, Volume 310, 118501, 2022.

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