Philipp Hähnel
Postdoctoral Research Fellow at Harvard Medical School and Massachusetts General Hospital
Greater Boston United States
Summary
👤
Senior🎓
Top SchoolPhilipp Hähnel is a postdoctoral researcher and applied machine-learning scientist with a decade of experience bridging theoretical physics, cancer genomics, and scalable ML applications. He develops probabilistic and generative models for somatic variant discovery and selection detection in cancer while contributing to interdisciplinary teams at Harvard, MGH, Broad Institute, and Dana-Farber. Trained in higher-spin theory and differential geometry (PhD, Trinity College Dublin), he leverages deep mathematical intuition to design principled algorithms and interpretable models for real-world problems like pollution forecasting and genomic discovery. He has a track record of translating abstract theory into practical software—ranging from multi-sample sequencing workflows to an agent-based gaming platform—and is experienced in proposal writing and communicating complex ideas to diverse audiences. Based in Greater Boston, he seeks impactful projects that combine mathematical rigor, open dissemination, and societal benefit.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Theoretical and Mathematical Physics, Doctor of Philosophy - PhD, Theoretical and Mathematical Physics at Trinity College DublinMaster's degree, Physics, Master's degree, Physics at Humboldt University of Berlin
English, German
Github Skills (42)
spark-ml10
genome10
tetris10
sequencing10
bioinformatics10
genomics10
compilation9
pyro9
probabilistic9
diffraction9
gatk9
mcmc9
bayesian-inference9
science9
optimisation8
Programming languages (5)
Github contributions (5)
phylyc/gatk4-somatic-snvs-indels
Mar 2022 - Nov 2022
Updated multi-sample short variant calling workflow based on the GATK Best Practices for Mutect2.
Contributions:1 release, 45 commits, 1 PR in 8 months
dockerdockstoredirectorygatkstill
phylyc/somatic_workflow
Oct 2023 - Apr 2025
WDL workflows for multi-sample somatic analysis of tissue or liquid biopsy samples from cancer patients.
Contributions:3 releases, 2 reviews, 10 PRs in 1 year 5 months
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