Kristin "Kris" Munch is the Laboratory Program Manager for Advanced Computing at NREL, where she manages and develops computing facilities and targeted strategy to enable advanced computing across multiple U.S. Department of Energy Office of Energy Efficiency and Renewable Energy (EERE) technology offices and programs. In addition to managing EERE’s High-Performance Computing Program, she also works to develop cloud and high-throughput computing capabilities and drives visualization and data-driven computing efforts, toward enabling a program that is inclusive of all types of computing done at EERE.

As a computer scientist, Kristin provides leadership to advance the strategies and capabilities required for an increasingly data-intensive research and development enterprise, including new efforts in state-of-the-art immersive, collaborative, and high-resolution visualization capabilities; data science solutions in streaming data and big data analytics; and computing supporting the integration of experimental, observational, simulated, and real-time hardware-in-the-loop science efforts.

Kristin previously worked in NREL's Computational Science Center for over a decade as the group manager of the Data, Analysis and Visualization Group, which provides expertise and advanced systems in scientific visualization, data science, and data management to NREL researchers, EERE programs, and partner projects.

Before coming to NREL, Kristin worked in several industry sectors, including telecommunications and pharmaceutical research. She has a doctorate in Computer Science from the University of Minnesota, where she was an IGERT Fellow and researched machine learning techniques applied to functional brain images for the early detection of dementia. She has an master's degree in Computer Engineering from the University of Mississippi and a B.S in Mathematics from Millsaps College. 

High-performance computing

Computing systems architecture and engineering

Scientific visualization

Cloud computing

Computing operations

Next-generation computing platforms

Streaming data platforms and real-time analytics

Experimental scientific data integration and workflows