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Welcome to my website!
I am a postdoctoral research associate at Harvard University where my research is supported by the Harvard Data Science Postdoctoral Research Fund Award.
My interests are primarily in statistics and data science with a focus on causal inference and machine learning and their application in health and social sciences.
In my work, I develop and combine statistical methods to address real-world problems. I start from policy-relevant questions and then develop methods to provide science-based answers. As a believer in open and accessible science, I also study algorithmic interpretability and simplicity, and disseminate open-source software and code.
If you’d like to learn more about me, you can find my CV here.
You can connect with me using the contacts below.
News:
12/23 Funding: Awarded the Pilot Project grant from the NIEHS-Harvard Center for Environmental Health (P30ES000002) as a Principal Investigator (PI) to investigate the environmental and health inequities of tech-induced air pollution.
12/23 Paper: "Confounder-Dependent Bayesian Mixture Model: Characterizing Heterogeneity of Causal Effects in Air Pollution Epidemiology" (with Dafne Zorzetto, Antonio Canale, and Francesca Dominici) has been conditionally accepted in Biometrics.
12/23 Paper: "Air Quality Disparities Mapper: An Open-Source Web Application for Environmental Justice" (with Joel Schwartz, Francesca Dominici, Heresh Amini, Edgar Castro, and Ethan McFarlin) has been published in Environmental Modeling & Software.
12/23 Paper: "CRE: An R Package for Interpretable Discovery and Inference of Heterogeneous Treatment Effects" (with Riccardo Cadei, Naeem Khoshnevis, Kwonsang Lee, and Daniela Garcia) has been published in the Journal of Open Source Software.
11/23 Mapper: The latest version of our "Air Quality Disparity Mapper" is now online.