Jordi Silvestre-ryan is a Senior Machine Learning Scientist with nine years of experience applying deep learning to biological problems, currently building ML-driven drug discovery tools at insitro. A UC Berkeley–UCSF PhD candidate in Bioengineering, he developed computational methods that halved nanopore sequencing error by combining complementary reads—an approach adopted in Oxford Nanopore’s basecaller. Jordi has transitioned his academic expertise in stochastic gene-regulatory modeling and biophysical ensemble fitting into industry roles at Atomwise/Numerion Labs, designing benchmarks and models to screen ultra-large chemical libraries. Based in the Bay Area, he blends rigorous probabilistic modeling with practical productionization of ML for biology, and has a track record of turning noisy experimental data into deployable algorithms.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (PhD) Bioengineering, Doctor of Philosophy (PhD) Bioengineering at University of California, Berkeley
Master of Science (MS) Biophysics, Master of Science (MS) Biophysics at University of California, San Francisco
Contributions:1 release, 270 commits, 29 PRs in 5 years 2 months
nanoporedecodingconsensusbasecalling
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Jordi Silvestre-ryan - Senior Scientist at Numerion Labs