Summary
Vojtech Kejzlar is a data scientist and academic with eight years of experience applying Bayesian methods, predictive modeling, and uncertainty quantification to real-world scientific problems. He builds statistical and ML tools that bridge research and production, most recently validating large multimodal perception models at Zoox. As an assistant professor he led multi-institutional, grant-funded projects, taught and developed courses on Bayesian modeling, and mentored students from deep learning to recommender systems. His PhD work accelerated training for Gaussian process models with dependent data, turning hours of computation into minutes—a practical innovation that reflects his focus on scalable inference. Based in Menlo Park, he pairs rigorous theory with science communication to create resources that improve data science literacy beyond academia.
8 years of coding experience
4 years of employment as a software developer
Bachelor's degree, Mathematics, Bachelor's degree, Mathematics at Masaryk University Brno
Secondary, cum laude, Visual and communication technology, Secondary, cum laude, Visual and communication technology at Communications Technology High School, Panská, Prague
ISEP Exchange, Mathematics, ISEP Exchange, Mathematics at Beloit College
Doctor of Philosophy (Ph.D.), Statistics, Doctor of Philosophy (Ph.D.), Statistics at Michigan State University
English, Czech