Makoto Kelp is currently a postdoctoral fellow working with Dr. Loretta Mickley in the Atmospheric Chemistry Modeling Group at Harvard University, where he received his PhD. He will be starting as a NOAA Climate & Global Change (C&GC) postdoctoral fellow working with Prof. Noah Diffenbaugh at Stanford University in September 2023. His current research centers on applying data science methods, including machine learning and compressed sensing, to uncover new perspectives in atmospheric chemistry, air quality engineering, and land-climate interactions. He places special emphasis on exploring the interplay among fires, climate, and society. Makoto’s PhD research with Prof. Daniel Jacob and Dr. Loretta Mickley at Harvard University focused on (1) using machine learning to expand the capabilities of atmospheric chemistry models, (2) developing dimensionality reduction algorithms that can determine the optimal and equitable placement of air quality sensors, (3) investigating the potential for prescribed fires to abate wildfire smoke exposures in the Western United States, and (4) quantifying impacts of chemical data assimilation on air pollution forecasts for NASA’s GEOS Composition Forecasting model (GEOS-CF).