Oleg Sofrygin is a senior applied scientist in San Francisco with 12 years of experience specializing in causal inference on networks, statistical efficiency theory, and high-performance machine learning for large-scale health data. He combines rigorous academic training (PhD in Biostatistics from UC Berkeley) with applied work at Kaiser Permanente and Uber, developing open-source statistical software and cutting-edge estimators that bridge theory and production. His work spans deep-learning NLP on EMRs, adversarial and meta-learning approaches to improve estimator performance, and efficient computation for network-dependent data. Known for translating semi-parametric causal methods into practical tools, he brings uncommon depth in both mathematical foundations and scalable implementation.
12 years of coding experience
7 years of employment as a software developer
PhD Biostatistics & Bioinformatics, PhD Biostatistics & Bioinformatics at University of California, Berkeley
Post-Bac Extension School Health Careers Program, Post-Bac Extension School Health Careers Program at Harvard University
Mathematics / Computer Science, Mathematics / Computer Science at Ural State University
MA Mathematics, MA Mathematics at Brandeis University
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