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Jon Maack

Researcher III-Computational Science


303-384-6739

Jon Maack's time at NREL has been focused on stochastic optimization, including renewable scenario selection and building and modeling using Julia and JuMP, and electromagnetic transients, including modeling and detecting, for the power grid.

Research Interests

Applied probability and statistics

Software development (especially computational software)

Optimization, dynamic programming and optimal control

Mathematical foundations of power systems

Education

Ph.D., Applied Mathematics, University of Massachusetts Amherst ​

M.S., Computational and Applied Mathematics, Colorado School of Mines​

B.S., Computational and Applied Mathematics, Colorado School of Mines​

Professional Experience

Graduate Teaching Assistant, University of Massachusetts Amherst (2013–2018)

Senior Software Engineer, Lockheed Martin (2012–2013)

Software Engineer, Lockheed Martin (2009–2012)

Software Engineer Internship, Lockheed Martin (2008–2009)

Featured Work

A Multi-Function AAA Algorithm Applied to Frequency-Dependent Line Modeling, IEEE Power and Energy Society General Meeting (2020)

Scenario Creation and Power-Conditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch, IEEE Power and Energy Society General Meeting (2020)

Scenario Creation and PowerConditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch, IEEE Power and Energy Society General Meeting (2020)

Scalable Transmission Expansion Under Uncertainty Using Three-stage Stochastic Optimization, IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (2020)