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Prasad Patil, PhD

Assistant Professor
Department of Biostatistics |
Boston University School of Public Health

WHEN:聽Wednesday, January 29, 2025, from 3:30 to 4:30 p.m.
WHERE:聽Hybrid | 2001 9I制作厂免费 College Avenue, Room 1140;
NOTE: Prasad Patil will be presenting from Boston

Abstract

Atmospheric concentration of ultrafine particles (UFP; particles <100nm) has the potential to affect human health in a distinct manner from larger particles such as PM2.5. In a study of aviation-attributable UFP from flight activity at Boston Logan Airport, our team implements stationary monitoring at varying distances along flightpaths from the airport. One goal is to develop a predictive model for UFP using meteorological and air traffic measurements that is generalizable to regions where direct monitoring cannot be conducted. Due to variations in orientation to the airport and local environmental factors, UFP measurement distributions from our monitoring sites are heterogeneous. There are advantages to training prediction models on the combination of all site data or by weighted combinations of predictions from single-site models. We show that for unbiased models, an optimal blending of predictions from both approaches performs at least as well as each scheme adaptively at differing levels of heterogeneity. We apply insights from these findings to propose an empirical weighting strategy based on cross-site model performance and agreement.

Speaker Bio

Prasad Patil is an Assistant Professor of Biostatistics at the Boston University School of Public Health. His primary research interests are reproducibility and replicability, with a focus on machine learning and prediction in public health. Areas of application include genomic biomarker development, air pollution monitoring, opioid overdose surveillance, and replication of published scientific results. He completed his PhD in biostatistics at Johns Hopkins BSPH and his postdoc at Harvard CSPH/Dana-Farber Cancer Institute.