Alejandro Schuler is an Assistant Professor in Residence in the Division of Biostatistics at UC Berkeley with expertise in nonparametric statistics, causal inference, and machine learning.
Dr. Schuler is known for developing NGBoost, the selectively adaptive lasso, and prognostic adjustment, among other methods. Besides methods development, he collaborates with domain experts to translate their questions to mathematical formalisms and bring the right methods to bear on them.
He completed his PhD at Stanford in 2018 and worked as a postdoc with Mark van der Laan before starting on the faculty at Berkeley. 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.
- PhD Biomedical Informatics, Stanford University 2018
- MS Mechanical Engineering, UCLA 2013
- BS Mechanical Engineering, UC Berkeley 2012
- Nonparametric statistics
- Gradient boosted trees
- Power calculation for adjustment in randomized trials