Alejandro Schuler

Alejandro Schuler is an Assistant Professor in Residence at UC Berkeley's division of Biostatistics with a strong focus on developing methods for clinical decision-making in the real world.

He is renowned for his work on NGBoost, the selectively adaptive lasso, and prognostic covariate adjustment, showcasing his expertise in statistically rigorous and economically essential approaches.

Alejandro collaborates closely with clinicians to transform research questions into mathematical frameworks that can be addressed using existing methodologies, emphasizing practical and user-friendly solutions.

With a diverse educational background, including a Ph.D. in Biomedical Informatics from Stanford University, an M.S. in Controls and Dynamical Systems from UCLA, and a B.S. in Mechanical Engineering from UC Berkeley, Alejandro brings a unique interdisciplinary perspective to his work.

Having held roles such as Postdoctoral Scholar and Adjunct Professor at UC Berkeley, Biostatistician at Unlearn.AI, Data Scientist at Kaiser Permanente Division of Research, and Engineering Consultant at Global Renewable Energy Engines, Alejandro has a broad range of experience in both academia and industry.

His passion for pedagogy and democratizing statistics underlines his commitment to making statistical concepts accessible to individuals from all backgrounds and skill levels.