Alejandro Schuler

B.S. Mechanical Engineering - UC Berkeley
M.S. Mechanical Engineering - UCLA
Ph.D. Biomedical Informatics - Stanford University

Alejandro is a data scientist with expertise in the intersection of causal inference, machine learning, and health data. Before working at Unlearn, Alejandro oversaw the evaluation of predictive models running at Kaiser Permanente. Alejandro holds a Ph.D. in Biomedical Informatics from Stanford University, where he worked with Dr. Nigam Shah to develop methods for the groundbreaking Informatics Consult service. Alejandro is also an inventor of NGBoost, a method for probabilisitic regression via gradient boosting, and the author of Concepts in Supervised Learning, an upcoming, interactive machine learning textbook for undergraduates.

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