Christopher Hartl
Chief Technology Officer at Rancho BioSciences
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolChristopher Hartl is a CTO and bioinformatics leader with 14 years of experience translating complex genomics and multi-omic research into production-ready analytics and diagnostic products. He has progressed from computational roles at the Broad Institute and UCLA to technical leadership at Epigenome Technologies and Rancho BioSciences, building data platforms, ML pipelines, and ontologically driven databases for single-cell and bulk sequencing. His work spans developing scalable cloud deployment for clinical-grade assays, statistical methods for rare-variant association, and bespoke libraries for single-cell coexpression and receptor–ligand inference. Christopher pairs rigorous academic training (PhD in Bioinformatics, UCLA; BA in Applied Mathematics, Harvard) with hands-on engineering—authoring tools that processed hundreds of millions of flow-cytometry events and automated genome assembly/annotation workflows. He is known for shrinking analytical bottlenecks via novel statistical approaches and for estimating diagnostic yield to guide product strategy, bringing a rare combination of research depth and product-focused execution.
14 years of coding experience
8 years of employment as a software developer
BA Applied Mathematics, BA Applied Mathematics at Harvard UniversityUniversity of California, Los Angeles
Russian
Github Skills (70)
probabilistic-programming10
python10
mcmc10
bayesian-inference10
variational-inference10
bayesian10
probabilistic-models10
bayesian-statistics10
statistical-inference10
probabilistic10
modeling10
genomics9
science8
adam8
avro8
Programming languages (8)
Github contributions (5)
dhglab/multiregional-networks
Apr 2020 - Apr 2020
Contributions:4 commits, 9 pushes, 1 branch in 19 days
chartl/pymc3
Mar 2019 - Apr 2019
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
Contributions:2 pushes, 1 branch in 1 month
pythontheanomachine-learningprobabilistic-programmingbayesian-statistics
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