Kinshuk Panda is a postdoctoral researcher in the Complex Systems Simulation and Optimization Group. In his role, he is investigating the application of multi-fidelity methods for modeling optimal grid operations and wind farm design under uncertainty. Furthermore, he enabled the development and deployment of scalable software on the U.S. Department of Energy’s leadership-class supercomputers for modeling grid operations under extreme weather events as part of the Exascale Computing Project. Panda is passionate about using advanced computing systems to develop solutions for various water and clean energy technologies. He is currently developing analysis tools for evaluating the performance of water treatment models using high-performance computing (HPC) systems. Furthermore, he is facilitating the use of HPC in other projects by maintaining documentation and how-to guides on running applications on NREL’s HPC Eagle system.
Panda has a background in uncertainty quantification and PDE-constrained optimization. He investigated the use of Krylov methods for dimension reduction for uncertainty propagation in his doctoral dissertation at Rensselaer Polytechnic Institute.
For additional information, see Kinshuk Panda's LinkedIn profile.
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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 Polytechinc Institute (2014–2019)
Teaching Assistant, Rensselaer Polytechinc Institute (2013–2015, Fall 2019)
Associations and Memberships
Member, Society for Industrial and Applied Mathematics
Member, Institute of Electrical and Electronics Engineers
Member, IEEE Power and Energy Society
Visualization of Multi-Fidelity Approximations of Stochastic Economic Dispatch, ACM e-Energy (2021)
Multi-fidelity Active Subspaces for Wind Farm Uncertainty Quantification, AIAA Scitech Forum (2021)
Hessian-based Dimension Reduction for Optimization Under Uncertainty, AIAA Aviation Multidisciplinary Analysis and Optimization Conference (2018)