Kyle Monahan enjoys using data science, remote sensing, and spatial statistics to analyze big, messy data and complex environmental and social systems, and teach these techniques to all.
Kyle has a background in environmental science and engineering, with a specific focus on geochemical techniques applied to water. His previous work includes developing sediment contaminant chronologies for the Hudson River at Rensselaer Polytechnic Institute (Troy, NY), and work in developing low-cost filters with arsenic adsorbents at Clarkson University (Potsdam, NY). His most recent graduate work at Tufts University used agent-based models to investigate the role of social and behavioral factors on the feasibility of point-of-use water technologies.
He co-taught various courses at Harvard Extension since 2014. In 2016, he worked as a GIS Analyst and later a Statistics Specialist for Tufts, providing individualized consultations and statistical services to students and faculty. He is a trained Software Carpentry instructor and a founding member of NESCLiC, and cares deeply about making statistics and data science methods understandable and accessible to all. He currently works with several local community groups, most recently with the Center for Health and Environmental Justice.
He is currently Manager, Data Science Services at Tufts University, providing statistical consulting, data visualization, and high-performance computing (HPC) support. You can learn more about his work at www.kylemonahan.info, and selected publications via Google Scholar.