Paweł Biernat is a Staff AI Researcher with 17 years of experience applying mathematics, physics and bioinformatics to clinical and biological problems across academia, R&D and client-facing roles. He builds and deploys robust ML systems—from Bayesian and classical models for small, high-dimensional clinical cohorts to deep learning and generative models for proteins and sequencing data—backed by strong production practices (Docker, Kubernetes, DVC, MLflow). Paweł has led interdisciplinary teams and authored company-wide ML packages, combining hands-on model development (NumPyro, JAX, PyTorch, Hugging Face, AlphaFold2) with rigorous model verification and tailored cross-validation. He’s comfortable moving projects from exploratory research to large-scale pipelines and deployment, and has a PhD in Mathematics that informs his emphasis on principled, reproducible methods. A less obvious strength is his track record of integrating formal numerical and probabilistic techniques (variational inference, MCMC, differential-equation methods) into practical bioinformatics tools used in production.
17 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Mathematics, Doctor of Philosophy (Ph.D.) Mathematics at Jagiellonian University
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