🔗 CV | Google Scholar
I'm a 1st year PhD student in Biostatistics at the University of California, Berkeley. I'm also a part-time Senior Data Analyst at Columbia University, where I work on both methodological and application side causal inference. My work involves developing and maintaining research software for novel causal estimators.
I am particularly interested in:
- Nonparametric Causal Inference
- The Intersection of Machine Learning and Statistics
- Design Focused Open Source Statistical Software
- lmtp – An R package that provides an estimation framework for the casual effects of feasible interventions based on point-treatment and longitudinal modified treatment policies.
- mlr3superlearner – A modern implementation of the Super Learner prediction algorithm using the mlr3 framework.
- crumble – An R package for general targeted machine learning for modern causal mediation analysis.
- ife - Forward mode autodiff for influence function based estimates.
- codebreak - A light weight codebook framework for R.