Kinshuk Panda is a postdoctoral researcher in the Complex Systems Simulation and Optimization Group. He is currently investigating dimension reduction and multi-fidelity methods for nonintrusive uncertainty quantification for wind farm design and stochastic optimal power flow. Additionally, as part of the Exascale Computing Project, he is working on developing high-fidelity models for wind scenario generation for solving security-constrained optimal power flow problems on the next generation of U.S. Department of Energy leadership-class supercomputers.
Panda has a background in PDE-constrained optimization and investigated the use of Krylov methods for dimension reduction for uncertainty propagation in his doctoral dissertation.
For additional information, see Kinshuk Panda's LinkedIn profile.
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Optimal power flow
Multidisciplinary design analysis and optimization
Ph.D., Mechanical Engineering, Rensselaer Polytechnic Institute
B.E., Mechanical Engineering, Manipal Institute of Technology
Postdoctoral Researcher, Computational Sciences, NREL (2020–present)
Research Assistant, Rensselaer Polytechnic Institute (2014–2019)
Teaching Assistant, Rensselaer Polytechnic Institute (2013–2014, 2015, 2019)
Associations and Memberships
Member, Society for Industrial and Applied Mathematics
Member, American Institute of Aeronautics and Astronautics
Multi-fidelity Active Subspaces for Wind Farm Uncertainty Quantification, AIAA Scitech (2021)
Hessian-based Dimension Reduction for Optimization Under Uncertainty, AIAA Aviation Multidisciplinary Analysis and Optimization Conference (2018)