Related people (15)
Luis Guillermo Villanueva Torrijo
Guillermo Villanueva is a Tenure Track Assistant Professor at the Ecole Polytechnique Federale de Lausane (EPFL), Switzerland, in the Mechanical Engineering Institute (IGM). Before joining EPFL he was a Marie Curie post-doctoral scholar at DTU (Denmark) and Caltech (California, US); and before a post-doc at EPFL-LMIS1. He received his M.Sc. in Physics in Zaragoza (Spain) and his PhD from the UAB in Barcelona (Spain). Since the start of his PhD (2002), Prof. Villanueva has been active in the fields of NEMS/MEMS for sensing, having expertise from the design and fabrication to the characterization and applicability. He has co-authored more than 75 papers in peer-reviewed journals (h-index of 24 WoK, 32 GoS) and more than 100 contributions to international conferences. He is serving, or has served, on the program committees of IEEE-NEMS, IEEE-Sensors, MNE, IEEE-FCS and Transducers. He is editor of Microelectronic Engineering. He has co-organized MNE2014 and SNC2015; and he is currently co-organizing the short courses at Transducers 2019 and the 16th International Workshop on Nanomechanical Sensors (NMC2019).
Johannes Hentschel
Johannes Hentschel studied music education, music theory, and Romance studies in Freiburg i. Br., Lübeck, and Helsinki. Proficient as an accordionist, singer and conductor, he is a lecturer for music theory at music universities. In 2018, however, he suspended this activity for the Digital Humanities Doctoral Program at the Swiss Federal Insititute of Technology Lausanne (EPFL). Supervised by Prof. Dr. Martin Rohrmeier at the Digital and Cognitive Musicology Lab (DCML), Johannes is preparing a thesis on diachronic style change in music while deepening his knowledge in corpus building and metadata organization.
Daniel Harasim
I am a Postdoctoral Researcher in Computational Musicology at the Digital and Cognitive Musicology Lab (DCML).My main research focus lies on probabilistic modeling of musical structures at the moment, combining approaches from machine learning, Bayesian statistics, computer linguistics, and music theory. Besides, I am studying and developing methods related to representation learning and probabilistic programming.In my PhD thesis The Learnability of the Grammar of Jazz: Bayesian Inference of Hierarchical Structures in Harmony, supervised by Martin Rohrmeier (EPFL) and Timothy O’Donnell (McGill University), I simulated how abstract knowledge about musical structure is learnable without a teacher from listening and engaging with music.In 2015, I earned a master’s degree in mathematics and computer science at the TU Dresden where I in particular worked on geometric structures of voice-leading spaces. My research interests further include topics from mathematical music theory, music cognition, and computational cognitive science. Aside from my academic activities, I enjoy playing the upright bass in Jazz improvisations.
Gabriele Cecchetti
After being awarded an MPhil at the Centre for Music and Science (University of Cambridge), Gabriele Cecchetti joined the Digital and Cognitive Musicology Lab (EPFL) as a doctoral assistant in 2019. In Rome, his hometown, he previously graduated in physics (Università ‘La Sapienza’) and as a cellist (Conservatorio ‘S. Cecilia’). His research interests lie on a spectrum spanning from music analysis and performance to the mathematical and theoretical modelling of the cognitive underpinnings of the experience of music. For his master’s project, he focused on a small-scale information-theoretic model of tonal affinity based on psychoacoustic and neuroscientific insight. Within the ERC-funded project ‘Principles of Musical Structure Building’ he will engage with matching theoretical and computational approaches with empirical cognitive and psychological frameworks. He is also an experienced musician with a particular devotion to chamber music, and has been extensively involved in music teaching over several years of collaboration with the educational programs of the National Academy ‘S. Cecilia’ in Rome.

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