Daniel Harasim receives EDDH thesis distinction

DCML head Martin Rohrmeier and Daniel Harasim during the June 9 award ceremony © Virginie Martin

DCML head Martin Rohrmeier and Daniel Harasim during the June 9 award ceremony © Virginie Martin

The College of Humanities congratulates Daniel Harasim for earning an EPFL Doctoral Program Thesis Distinction for his PhD research in the Digital and Cognitive Musicology Lab (DCML).

Harasim was awarded the distinction for his thesis, The Learnability of the Grammar of Jazz: Bayesian Inference of Hierarchical Structures in Harmony, which he completed within the framework of the Digital Humanities Doctoral Program (EDDH). His study investigates the prior knowledge required to learn musical grammar inductively from jazz chord sequences, using Bayesian statistical models and computational simulations.

“I had a wonderful experience doing my PhD in the EDDH as part of the DCML,” Harasim says. “The doctoral school facilitated my studies in multiple disciplines like music, data analysis, and machine learning. It was a pleasure working with my colleagues in the DCML's friendly and constructive atmosphere.”

Harasim received his award during a campus ceremony on June 9th, where he also presented the relationship of his work to transdisciplinary research in digital humanities. Currently a postdoctoral researcher in the DCML, he will complete his EPFL studies at the end of June. In July, he will start developing and implementing algorithms for probabilistic inference problems for tech start-up PlantingSpace.

“I am looking forward to this transition, because I can further broaden my horizons and apply the statistics knowledge and experience that I gained through my PhD studies,” he says.

Each year, distinctions are granted to a selection of very high quality EPFL theses, in order to highlight the doctoral candidates’ research work and their scientific merit. For each doctoral program, nominated graduates are selected on the basis of their oral examination. Then, the program committee evaluates the nominees and rewards the best 8%.

References

The Learnability of the Grammar of Jazz: Bayesian Inference of Hierarchical Structures in Harmony (2020) Harasim, Daniel. Advisor(s) Rohrmeier, Martin Alois, O'Donnell, Timothy John.