Leah Comment is a Senior Principal Statistical Scientist with 11 years of experience applying Bayesian and frequentist causal inference to real-world health data, currently shaping statistical research at Genentech. She builds methods at the intersection of causal inference and decision theory to enable scalable clinical decision support and evidence-generation tools, drawing on prior leadership roles in decision sciences and causal modeling at Foundation Medicine. Her technical toolkit includes Bayesian modeling, ensemble learning, and data-driven sensitivity analysis, informed by training in health policy, economics, and human biology. A Harvard-trained biostatistician, Leah has tackled problems from semicompeting risks to data integration for unmeasured confounding, and she’s driven by a vision of learning health systems that deliver personalized care. Less obvious: she pairs deep methodological research with pragmatic prototyping of ML-driven decision support for oncology, bridging theoretical rigor and real-world deployment.
11 years of coding experience
6 years of employment as a software developer
B.S., Microbiology and molecular genetics, 3.67, B.S., Microbiology and molecular genetics, 3.67 at Michigan State University Honors College
Master of Public Health (MPH), MS, Epidemiology, Biostatistics, 4.0, Master of Public Health (MPH), MS, Epidemiology, Biostatistics, 4.0 at University of Michigan
Doctor of Philosophy (Ph.D.), Biostatistics, 3.75, Doctor of Philosophy (Ph.D.), Biostatistics, 3.75 at Harvard University
R package implementing parametric semicompeting risks in Stan
Contributions:73 commits, 19 PRs, 62 pushes in 1 year 10 months
r-packageparametricrisksstan
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Leah Comment - Senior Principal Statistical Scientist