Summary
Daniel Harasim is a mathematician and machine-learning researcher with nine years’ experience applying Bayesian inference, computational modeling, and predictive statistics across academia and industry. With a PhD in Computational Music Cognition from EPFL and postdoctoral work there, he has published extensively (10 peer-reviewed papers, 7 as first author) and presented at major international venues. He now applies his expertise as a Bayesian Inference Researcher and Software Engineer at PlantingSpace, building probabilistic models with Python and Julia and a strong foundation in algorithms and data structures. Daniel pursues cross-domain impact—seeking to extend his modeling skills into NLP, optimal planning, or molecular analysis—and enjoys experimenting with languages like Haskell, Rust, and ClojureScript. He has lectured and taught at institutions including TU Dresden and McGill, and organized a symposium at the Cognitive Science Society, reflecting both research leadership and community engagement. Outside work he plays upright bass in jazz ensembles and is an avid coffee roaster and home cook, signaling a creative, detail-oriented approach to problem solving.
9 years of coding experience
2 years of employment as a software developer
Master of Science -, Mathematics and Computer Science, 1.1, Master of Science -, Mathematics and Computer Science, 1.1 at Technische Universität Dresden
Doctor of Sciences (PhD), Computational Music Cognition, Graduation with Distinction Award, Doctor of Sciences (PhD), Computational Music Cognition, Graduation with Distinction Award at EPFL (École polytechnique fédérale de Lausanne)
English, German, French