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Published in , 1900
This paper provides privacy-preserving, communication-efficient, and robust estimation and inference for targeted average treatment effects in the presence of heterogeneity.
Published in , 1900
This paper develops semi-supervised, transfer learning methods to improve efficiency of pragmatic trials when the study endpoint is time-to-event and is captured using imperfect surrogates in electronic health records.
Published in In preparation; Winner of the 2021 ENAR Distinguished Student Paper Award, 2020
This paper provides doubly robust estimation and inference for the proportion of treatment effect on a true outcome that is explained by a surrogate marker in observational data settings.
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Head Teaching Fellow, Harvard Biostatistics, 2021
I served as the head teaching fellow in Spring 2021 and a teaching fellow in Spring 2020 for this introductory course about topics in the design, analysis, and interpretation of clinical trials.
Head Teaching Fellow, Harvard Biostatistics, 2021
I served as the head teaching fellow in Spring 2021 for this doctoral course for biostatistics students on advanced topics in the design, analysis, and interpretation of clinical trials, including study design; choice of endpoints; interim analyses and group sequential methods; subgroup analyses; and meta-analyses. I served as a teaching fellow in Spring 2020.
Teaching Fellow, Harvard Biostatistics, 2022
I served as a teaching fellow in Spring 2022 for the Harvard Biostatistics department’s course on applied survival analysis taught by Professor Sebastien Haneuse (class size: 100).
Head Teaching Fellow, Harvard Statistics, 2022
I served as the head teaching fellow in Spring 2022 for this reading-based doctoral course on causal inference, taught by Professor Jose Zubizarreta (class size: 20).
Teaching Fellow, Harvard Biostatistics, 2022
I served as a TF in Fall 2022 for BST 222 taught by Rui Duan. I also TF’ed for this course in 2021, 2020, and 2019. I taught labs for this course in probability theory and statistical inference for masters students in biostatistics and doctoral students in epidemiology, environmental sciences, and other departments, using Casella and Berger.
Teaching Fellow, Harvard Statistics, 2022
I served as a teaching fellow in Fall 2022 for this doctoral course on causal inference, taught by Professor Kosuke Imai (class size: 60).
Instructor, Northeastern University Department of Health Sciences, 2024
This course examines advanced methods in conducting observational research across pharmacoepidemiology, emphasizing approaches used by the Observational Health Data Sciences and Informatics (OHDSI) community. Focuses on using open-source software and open-science principles to conduct and interpret a real-world evidence (RWE) study.