Vladimir Trifonov is a Principal Machine Learning Engineer with 15 years of experience bridging deep academic research and industrial AI, currently leading ML efforts at Altos Labs after senior science roles at Janssen and GNF. He holds a PhD in Computer Science and has a strong track record in computational biology, genomics data analysis, and applied ML for drug discovery, informed by earlier work in computational complexity and discrete mathematics. Vladimir combines hands-on engineering—contributing backend formalization code to the UniMath Coq library—with leadership in productionizing models for life-science applications. Known for translating complex biological problems into scalable ML solutions, he brings both rigorous theoretical grounding and practical delivery experience from academia to industry. An interesting facet: his open-source contributions touch formalized mathematics in UniMath, reflecting a rare blend of formal methods and applied machine learning.
15 years of coding experience
13 years of employment as a software developer
PhD Computer Science, PhD Computer Science at The University of Texas at Austin
This coq library aims to formalize a substantial body of mathematics using the univalent point of view.
Role in this project:
Backend Developer
Contributions:157 commits, 42 PRs, 28 pushes in 3 years 1 month
Contributions summary:Vladimir made several commits focused on formalizing mathematics within the UniMath Coq library using the univalent point of view. Their work involved adding and modifying code related to bsystems, particularly in files concerning pre-towers, fibers, and pull-backs. Additionally, the user contributed to the Ktheory component and included code changes related to building structures and logic for number theory.
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Vladimir Trifonov - Principal Machine Learning Engineer at Altos Labs