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Assistant Professor in Residence

Alejandro Schuler is an Assistant Professor in Residence in the Division of Biostatistics at UC Berkeley. His expertise is in nonparametric statistics, causal inference, and machine learning. Dr. Schuler is also passionate about pedagogy and making good statistics accessible to everyone regardless of background or experience.

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 Ph.D. 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.

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Research Associate

Santiago Papini in an NIMH T32 Postdoctoral Research Fellow in Clinical Informatics and Delivery Science at the Division of Research, Kaiser Permanente Northern California. He received his PhD in Clinical Psychology at The University of Texas at Austin and completed his clinical internship at UCSD/VA San Diego Healthcare System. Dr. Papini's research expertise is in experimental and computational approaches to development, evaluation, and delivery of interventions for mental health disorders. His collaborations with the RWM-Group are focused on the application of modern causal inference methods to real-world healthcare systems data.

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