I am a PhD Candidate in Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health, where my research sits at the intersection of aerosol science, air-quality measurement, exposure science, and machine learning.

My work focuses on improving how we measure and interpret airborne pollutants. I use low-cost sensor networks, reference-grade aerosol instrumentation, controlled laboratory experiments, and computational methods to understand measurement error, improve sensor calibration and quality control, and better characterize environmental exposures.

More broadly, I am interested in connecting aerosol physics and chemistry with modern statistical and machine-learning methods. My goal is to develop measurement systems and analytical approaches that are not only technically rigorous, but capable of improving how we understand real-world air pollution and its implications for human health.