Alejandro Schuler
Assistant Professor In Residence at University of California, Berkeley
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolAlejandro Schuler is an Assistant Professor in Residence in Biostatistics at UC Berkeley with 11 years of experience translating clinical problems into statistically rigorous, actionable solutions. He is known for developing NGBoost, the selectively adaptive lasso, and prognostic covariate adjustment, and contributed core enhancements to the widely used NGBoost library for probabilistic prediction. His work spans methods development and clinical collaboration, emphasizing economically and operationally feasible decision rules that are frictionless for users. Prior roles at Kaiser Permanente and Unlearn.AI reflect a track record of moving models from research to live clinical deployment and modernizing analytics stacks. Trained in biomedical informatics (PhD, Stanford) with engineering roots from UC Berkeley and UCLA, he pairs deep theory with practical implementation and a passion for accessible pedagogy.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Science (BS), Mechanical Engineering, Bachelor of Science (BS), Mechanical Engineering at UC BerkeleyUniversity of California, Los AngelesDoctor of Philosophy (Ph.D.), Biomedical Informatics, Doctor of Philosophy (Ph.D.), Biomedical Informatics at Stanford University
Github Skills (4)
machine-learning10
python10
scikit-learn9
scikit9
Programming languages (7)
Github contributions (5)
stanfordmlgroup/ngboost
Jun 2018 - May 2021
Natural Gradient Boosting for Probabilistic Prediction
Role in this project:
ML Engineer
ML EngineerContributions:46 reviews, 200 commits, 38 PRs in 2 years 11 months
Contributions summary:Alejandro made significant contributions to the natural gradient boosting library, with commits focused on enhancing the core functionality and usability. They implemented a multiclass classification example, addressed bugs related to staged prediction, and improved the handling of distribution slicing and parameter access. Furthermore, the user refactored the distribution and scoring rule system for improved structure.
pythonpredictionnaive-bayesnatural-gradientsmachine-learning
alejandroschuler/r4ds-courses
Jun 2020 - Aug 2022
Contributions:1 release, 3 reviews, 90 commits in 2 years 2 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.