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.
I have a masters degree in epidemiology from the LSHTM where my thesis was on developing AI models to segment spinal cord tissue from MRI data, align to an atlas, calculate percent tissue damaged in different tracts and predict recovery trajectories after spinal cord trauma. I worked at UCSF department of radiology and internal medicine for 5 years as a data scientist before starting my PhD. I started my PhD to pursue less biased statistical approaches and to investigate how environmental exposures lead to deleterious health outcomes.