I connect physical concepts to atmospheric data so students can move from understanding a mechanism to evaluating a model. My teaching experience includes two graduate course modules at the University at Albany.
Graduate teaching
Machine Learning and Aerosol Microphysics
ATM 515: Aerosol Physics · Fall 2022 · Four-hour module
I introduced machine learning in atmospheric and aerosol science, examined cloud condensation nuclei estimation, and led a hands-on example in R using aerosol datasets. The exercise used GEOS-Chem–APM output to introduce predictor selection, training and test subsets, and random-forest regression for CCN. The module connected the physical question to the choices involved in building and assessing a model.
Aerosol Exposure and COVID-19
EHS 545: Global Climate Change, Extreme Weather & Public Health · Spring 2023 · One-hour module
I examined aerosol exposure, disparities in exposure and health outcomes, and the interpretation of aerosol–health associations in a changing climate.
How I teach
I start with a physical question, then use theory, observations, and computation to investigate it. I ask students to examine assumptions and uncertainty alongside model results. My goal is to build transferable skills in reproducible analysis, critical reading, and scientific communication.
My teaching interests include atmospheric physics, aerosol science, chemical transport modeling, environmental data science, physics-informed machine learning, and air quality and health.
Mentoring and outreach
My research mentoring has included undergraduate and graduate students, alongside master’s summer-project co-guidance at the Indian Institute of Tropical Meteorology.
My mentoring and outreach experience includes STEP/CORE, Project SHORT, and Wonderland Literacy Center. I value guidance that meets students at their current level and makes the next step in learning or research concrete.