Megan Heyman

Megan Heyman is an Associate Professor in the Department of Mathematics at Rose-Hulman Institute of Technology, specializing in nonparametric statistical methods and time-series data analysis for climate change and environmental phenomena.

Her educational qualifications include:

• PhD in Statistics from the University of Minnesota (2016)
• MS in Statistics from the University of Minnesota (2014)
• BS from Rose-Hulman Institute of Technology (2008)

Dr. Heyman's research centers on bootstrapping methods for spatio-temporal data and nonparametric environmental modeling, with significant contributions to climate informatics. She develops innovative statistical frameworks for tree-ring analysis, crop yield prediction, and climate model validation, while integrating pedagogical approaches like rubber chicken demonstrations for teaching statistical inference.

Her publication record reveals a consistent trajectory in applying bootstrap techniques to environmental datasets, evolving from foundational R package development (lmboot, WiSEBoot) toward interdisciplinary climate applications. Recent work emphasizes probabilistic evaluation of climate models and spatio-temporal relationships in ecological records, demonstrating strong collaboration with NASA's Jet Propulsion Laboratory.

Dr. Heyman has not listed any scientific awards in her current profile.

As co-director of the Independent Project/Research Opportunity Program, she mentors undergraduate researchers in statistics and environmental projects. Her NASA collaboration represents significant external research engagement, though specific grant details are not provided in the source material.

She operates within environmental statistics research networks focused on climate informatics, collaborating with NASA JPL on data analysis initiatives while maintaining active statistical consulting practice.