Alejandro Schuler is an Assistant Professor in Residence at UC Berkeley Biostatistics. His research focus is on developing methods for clinical decision-making in the real world that are economically or clinically necessary, statistically rigorous, and frictionless from the user perspective. He completed his Ph.D. at Stanford in 2018 and worked as a postdoc with CTML before starting on the faculty. Dr. Schuler is known for developing the Selectively Adaptive Lasso, NGBoost, and prognostic covariate adjustment methods, among others. In addition, he often collaborates with domain experts to translate their questions to mathematical formalisms and bring the right methods to bear on them. His experiences working as a data scientist at Kaiser Permanente's Division of Research and as an early employee of a health tech startup helped shape his research agenda into something with relevance beyond academia. Dr. Schuler is also passionate about pedagogy and making good statistics accessible to everyone regardless of background or experience.
semiparametric efficiency, causal inference, gradient boosting, power calculation, electronic health records, statistics pedagogy