Prasad Patil
Profiles

Prasad Patil, PhD

Assistant Professor, Biostatistics - Boston University School of Public Health

(617) 358-2518
patil@bu.edu
Crosstown Center
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Biography

Dr. Prasad Patil studies machine learning applications in public health and the development of statistical methods focused on reproducibility and replicability. Dr. Patil has specific interest in techniques for combining data from and/or models trained in multiple studies to improve generalizbility of predictive models. Some of his application areas include: gene signatures for assessing Tuberculosis stage, multi-site modeling of air pollution spikes caused by airplane activity, opioid overdose risk modeling in incarcerated populations, longitudinal modleing of well-being indicies, and survey weight estimation for convenience samples of vulnerable/underrepresented populations.

Dr. Patil received his PhD in Biostatistics from the Johns Hopkins Bloomberg School of Public Health and completed a postdoctoral research fellowship at the Harvard Chan School of Public Health Department of Biostatistics/Dana-Farber Cancer Institute Department of Data Science.

Education

  • Johns Hopkins University, PhD Field of Study: Biostatistics
  • New York University, BA Field of Study: Mathematics/Computer Science

Websites

Classes Taught

  • SPHBS803
  • SPHBS845

Publications

  • Published on 12/19/2025

    Leibler JH, Yuan Y, Beatriz E, Lin TW, Sharff M, Stack C, Cardoso L, Tieskens K, Washington K, Peng X, Patil P. Mental health inequities affecting sexual and gender diverse individuals during the early COVID-19 period in Massachusetts. PLOS Mental Health. 2025; 12(2):e0000341.

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  • Published on 11/21/2025

    Dukes K, Ni P, Alperen J, Cesare N, LaValley M, Tripp T, Lane K, Mohammed S, Patil P, Emig D, Winter M, Davis B, Wang B, Jain A, Acker M, Rickles M. A national growth mixture modeling analysis of county-level COVID-19 incidence rate trajectories and health inequities during three successive pandemic waves in 2020. Sci Rep. 2025 Nov 21; 15(1):41272. PMID: 41271888.

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  • Published on 9/11/2025

    Mueller SC, Patil P, Levy JI, Hudda N, Durant JL, Gause EL, van Loenen BD, Bermudez M, Geddes JA, Lane KJ. Quantifying Aviation-Related Contributions to Ambient Ultrafine Particle Number Concentrations Using Interpretable Machine Learning. Environ Sci Technol. 2025 Sep 23; 59(37):19942-19952. PMID: 40934392.

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  • Published on 8/8/2025

    Wang X, Harper K, Sinha P, Johnson WE, Patil P. Response to "Correspondence on 'Analysis of the cross-study replicability of tuberculosis gene signatures using 49 curated human transcriptomic datasets'". Tuberculosis (Edinb). 2025 Sep; 154:102678. PMID: 40816961.

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  • Published on 6/17/2025

    van Loenen BD, Black-Ingersoll F, Durant JL, Levy JI, Patil P, Mueller SC, Gause E, Hudda N, Bermudez M, Lane KJ. Aircraft Arrival and Departure Contributions to Ultrafine Particle Size Distribution in a Near-Airport Community. Environ Sci Technol. 2025 Jul 01; 59(25):12853-12864. PMID: 40527744.

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  • Published on 5/8/2025

    Wang X, Harper K, Sinha P, Johnson WE, Patil P. Analysis of the cross-study replicability of tuberculosis gene signatures using 49 curated human transcriptomic datasets. Tuberculosis (Edinb). 2025 Jul; 153:102649. PMID: 40359654.

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  • Published on 1/1/2025

    Ren B, Patil P, Dominici F, Parmigiani G, Trippa L. Cross-validation approaches for multi-study predictions. Electronic Journal of Statistics. 2025; 19(2):4914-38.

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  • Published on 12/31/2024

    Shyr C, Ren B, Patil P, Parmigiani G. Multi-study R-learner for estimating heterogeneous treatment effects across studies using statistical machine learning. Biostatistics. 2024 Dec 31; 26(1). PMID: 41410481.

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  • Published on 6/20/2024

    Wang X, VanValkenberg A, Odom AR, Ellner JJ, Hochberg NS, Salgame P, Patil P, Johnson WE. Comparison of gene set scoring methods for reproducible evaluation of tuberculosis gene signatures. BMC Infect Dis. 2024 Jun 20; 24(1):610. PMID: 38902649.

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  • Published on 5/6/2024

    Yamkovoy K, Patil P, Dunn D, Erdman E, Bernson D, Swathi PA, Nall SK, Zhang Y, Wang J, Brinkley-Rubinstein L, LeMasters KH, White LF, Barocas JA. Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release. Ann Epidemiol. 2024 Jun; 94:81-90. PMID: 38710239.

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