My current research focus revolves mainly around causal inference. Both applications (mainly around healthcare) and methodology (including usability). I also dabble in information visualization and genetic-based risk models (polygenic risk scores). Prior to that, I worked on computational methods in molecular biology (genomics and transcriptomics). In addition to academic publications, I also issued several patents in the US.
I will do my best to keep this listing updated, but in the plausible case I won’t, please see my Google Scholar page for the latest version.
Publications
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Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores
Epidemiology (2024)
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Causalvis: Visualizations for Causal Inference
CHI: Conference on Human Factors in Computing Systems (2023)
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FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization
Pacific Symposium on Biocomputing (2023)
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Screening human embryos for polygenic traits has limited utility
Cell (2019)
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A discriminative approach for finding and characterizing positivity violations using decision trees
Arxiv (2019)
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Predicting breast cancer by applying deep learning to linked health records and mammograms
Radiology (2019)
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An evaluation toolkit to guide model selection and cohort definition in causal inference
Arxiv (2019)
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Comment: causal inference competitions: where should we aim?
Statistical Science (2019)
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*In vivo* cleavage rules and target repertoire of RNase III in *Escherichia coli*
Nucleic Acids Research (2018)
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Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
Arxiv (2018)
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