Alejandro Schuler

Alejandro Schuler is an Assistant Professor at the University of California, Berkeley, specializing in causal inference, biostatistics, and machine learning. He earned his PhD in Biomedical Informatics from Stanford University (2018) and completed a postdoctoral fellowship under Mark van der Laan. His industry experience at Kaiser Permanente’s Division of Research and a health tech startup informs his applied research agenda focused on bridging mathematical formalisms with real-world healthcare challenges.

• PhD – Biomedical Informatics, Stanford University, 2018
• MS – Mechanical Engineering, University of California, Los Angeles, 2013
• BS – Mechanical Engineering, University of California, Berkeley, 2012

His research centers on developing statistical methods for causal inference, including the selectively adaptive lasso, prognostic adjustment, and NGBoost. Recent work emphasizes power calculation in randomized trials, robust neural network frameworks, and stochastic intervention models for healthcare and environmental exposure analysis.

His publications highlight advancements in targeted maximum likelihood estimation, Riesz regression, and efficient covariate adjustment. These methods aim to improve the accuracy and interpretability of causal effect estimates in complex datasets.

Dr. Schuler collaborates with domain experts to translate clinical and public health questions into mathematical models, ensuring his work maintains relevance beyond academia. He is actively involved in methodological development and application to healthcare data, focusing on reducing hospital readmissions and optimizing trial design.