Pavel Senin is a Computational Scientist Lead with 17 years of multidisciplinary experience building production-grade analytics and software for life sciences and industrial R&D. He architects and delivers spatial transcriptomics and federated learning products at Sanofi, bridging research, clinical studies, and regulatory SaMD pathways for precision medicine and rare-disease diagnostics. His background spans bioinformatics, high-performance computing, and embedded/FPGA-aware data acceleration—from metagenomics pipelines and R packages at Los Alamos to computational storage work at Western Digital. A polyglot developer comfortable from assembler to modern Python/Java and HDL, he consistently moves models from prototype to validated, auditable clinical software. He brings a rare combination of deep academic training (PhD-level work and complexity theory exposure) and hands-on systems engineering across cloud, HPC, and regulated environments. Colleagues rely on him for turning complex biological and IO-bound problems into scalable, compliant products that drive measurable clinical and operational impact.
17 years of coding experience
23 years of employment as a software developer
Summer School program at SFI Complexity Theory, Summer School program at SFI Complexity Theory at Santa Fe Institute
PhD Computer Science, PhD Computer Science at The University of New Mexico
ABD (all but dissertation) Computational and Applied Mathematics, ABD (all but dissertation) Computational and Applied Mathematics at Southern Federal University (former Rostov State University)
PhD Computer Science, PhD Computer Science at University of Hawaii at Manoa
Contributions:5 releases, 269 commits, 4 PRs in 2 years 3 months
metagenomicsgenomicsedgecomparativebioinformatics
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