Summary
Daniel Marbach is a Senior Principal Scientist with 11+ years of experience applying machine learning and computational biology to translate large-scale biomedical data into patient- and tissue-specific disease mechanisms. Based in Basel, he has led high-impact, open-data initiatives such as multiple DREAM Challenges and built the most comprehensive resource for tissue- and cell-type regulatory circuits, work that underpinned a Nature Methods cover article. At Roche and in academic roles at MIT, the Broad Institute, and EPFL, he developed ensemble and network-inference methods that harness crowdsourced wisdom to improve pathway discovery and biomarker identification. He combines deep technical expertise in integrative omics and regulatory network reconstruction with a strong commitment to collaborative, reproducible science, often coordinating cross-disciplinary teams and wet-lab validation. A less obvious strength is his track record of turning competition-style benchmarking into widely used community resources that accelerate reproducible discovery.
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Ecole polytechnique fédérale de Lausanne
German, English, French, portugese