# Biography

I am an assistant professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. Prior to this, I was an NSF Mathematical Sciences Postdoctoral Research Fellow, working on causal inference and machine learning applied to problems with complex study designs, especially vaccine efficacy trials. I obtained my PhD in biostatistics from UC Berkeley, where I worked on non/semi-parametric estimation and causal inference for continuous-valued exposures and on mediation analysis. In that time, I was on the founding core development team of the tlverse project, an open source software ecosystem for targeted learning, and I enjoyed diverse collaborations with the Bill and Melinda Gates Foundation, the Fred Hutchinson Cancer Center, and Netflix Research.

My research interests combine causal inference and machine learning, aiming to formulate robust, efficient, and assumption-lean methods for statistical inference from a translational perspective, tailored to issues arising naturally in collaborative science. Broadly, I am motivated by methodological topics from nonparametric (or distribution-free) inference, semiparametric-efficient inference, high-dimensional inference, (targeted) loss-based estimation, uses of biased sampling procedures, and adaptive experimentation. While I maintain eclectic scientific interests, I have recently been captivated by problems – both statistical and substantive – arising from the analysis of clinical trials of infectious diseases and in their epidemiology. I am also deeply interested in high-performance statistical/numerical computing and in the development of open source software for promoting reproducibility and transparency in the practice of applied statistics and statistical data science.

### Interests

• causal machine learning, causal inference, missing data
• non/semi-parametric inference, high-dimensional inference
• statistical machine learning, nonparametric estimation
• adaptive experimental designs, biased sampling methods
• statistical computing, reproducible data science

### Education

• PhD in Biostatistics (designated emphasis in Computational & Genomic Biology), 2021

University of California, Berkeley

• MA in Biostatistics, 2017

University of California, Berkeley

• BA with a triple major in Molecular & Cell Biology (em. Neurobiology), Psychology, and Public Health, 2015

University of California, Berkeley

# Recent Publications

(see CV for a full list)

Quickly discover relevant content by filtering publications.
(2022). Cross-validated loss-based covariance matrix estimator selection in high dimensions. In Journal of Computational and Graphical Statistics.

(2022). Nonparametric causal mediation analysis for stochastic interventional (in)direct effects. In Biostatistics.

# Recent & Upcoming Talks

Efficient Estimation of Modified Treatment Policy Effects Based on the Generalized Propensity Score
A Framework for Causal Segmentation Analysis with Machine Learning in Large-Scale Digital Experiments
Nonparametric Estimation of the Generalized Propensity Score Based on the Highly Adaptive Lasso
Leveraging the Causal Effects of Stochastic Interventions to Evaluate Vaccine Efficacy in Two-phase Trials
Leveraging the Causal Effects of Stochastic Interventions to Evaluate Vaccine Efficacy in Two-phase Trials

# Teaching

## current courses

I won’t be teaching during the 2022-2023 academic year. I’ll resume in 2023-2024.

## Carpentries workshops

I am a member of Software Carpentry and Data Carpentry, through which I work on curriculum development, maintenance of lesson materials, and workshop delivery.

# Software

Collected collateral damage from doing statistics research, hopefully useful to others.

## Targeted Learning in the tlverse

The tlverse is an ecosystem of R packages for Targeted Learning, of which I am a co-founder and core developer. A few of the tlverse packages to which I’ve made significant contributions include

## Causal Machine Learning

A significant focus of my research program centers on the intersection of causal inference and statistical machine learning. I’ve (co-)developed R packages for a range of problems: causal mediation analysis, evaluating the effects of stochastic interventions under two-phase sampling, conditional density estimation, causal segment discovery and offline policy evaluation, and survival analysis.

• sherlock: An R package for employing causal machine learning and non/semi-parametric estimation to discover population segments (or subgroups) based on treatment effect heterogeneity. Flexible techniques for defining segment-specific treatment rules and efficient estimators of the causal effects of these dynamic treatment regimes are implemented. Joint work with Wenjing Zheng as part of an internship at Netflix Research.
[Docs] | [GitHub]

• medshift: An R package for estimating the population intervention (in)direct effects based on stochastic interventions. Classical and efficient estimators are supported for the effects of incremental propensity score interventions and modified treatment policies. Joint work with Iván Díaz.
[Docs] | [GitHub]

• medoutcon: An R package for efficient estimation of interventional (in)direct effects subject to intermediate confounding, including one-step and targeted minimum loss estimators. Joint work with Iván Díaz and Kara Rudolph.
[Docs] | [GitHub] | [Paper]

• txshift: An R package for efficient estimation of and inference on causal effects of stochastic interventions on continuous-valued exposures. Robust estimation and efficient inference under two-phased sampling is supported. Joint work with David Benkeser.
[Docs] | [GitHub] | [CRAN] | [Paper]

• haldensify: An R package for nonparametric conditional density estimation based on the highly adaptive lasso, designed for estimating the generalized propensity score. Joint work with David Benkeser and Mark van der Laan.
[Docs] | [GitHub] | [CRAN]

• survtmle: An R package for the construction of targeted maximum likelihood estimates of marginal cumulative incidence in right-censored survival settings with and without competing risks, including estimation procedures that respect bounds. Joint work with David Benkeser.
[Docs] | [GitHub] | [CRAN]

## High-Dimensional Biology

A parallel thread of my research concerns the development of novel statistical methodology for application in high-dimensional and computational biology. I have (co-)developed several R packages extending the Bioconductor Project.