Ioannis RammosYannis Rammos is a postdoc at the EPFL Digital & Cognitive Musicology Lab and visiting piano faculty at European University Cyprus. An internationally active pianist and piano pedagogue trained in Russia, during the past two years he has been invited to deliver performances, masterclasses, and lectures at the Estonian Academy for Music & Theater, the Russian Institute for Art History, Conservatorium van Amsterdam, and the Aristotle University School of Music, among other venues. Formerly a researcher in music theory at the Sibelius Academy, he completed his doctoral studies in piano and music theory at the CUNY Graduate Center and New York University, graduating from the latter with a Ph.D. in classical performance. His latest peer-reviewed publications have appeared in
Music & Letters
,
Quodlibet
, and
Music Theory & Analysis
. His research spans Schenker studies, critical theory (especially of French poststructuralist origin), Russian music theory, and piano timbre studies. At EPFL he contributes to the Laboratory's overarching corpus initiative, focusing on the history of Western harmony and formalizations of Schenkerian tonal structures.
Francesco RomanoExpertise
Gyrotrons, Non-Neutral Plasma, Electric Space Propulsion, Helicon Plasma Thrusters, Atmosphere-Breathing Electric Propulsion
Current Work
The TRapped Electrons eXperiment - T-REX
Born in Sassari and grew up in Udine, I studied Aerospace Engineering for Bachelor, Master in Italy and I've earned my Ph.D. in Germany. I also graduated in organ and organ composition at the conservatory in Italy. I Love space, science, music (piano, organ, classical, classic rock), endurance and uphill cycling, classic
Martin SchrimpfExpertise
NeuroAI, computational neuroscience, computer vision, natural language processing, human alignment, startups [CV]
Mission
Modeling the brain: Martin's research focuses on a computational understanding of the neural mechanisms underlying natural intelligence in vision and language. To achieve this goal, he bridges Deep Learning, Neuroscience, and Cognitive Science, building artificial neural network models that match the brain’s neural representations in their internal processing and are aligned to
Federico TerziTeaching & PhD Courses Music, politics and society I HUM-406 This course examines the presence of exotic musical characteristics (popular and folkloric) - in Europe and beyond - in art music (17th-20th centuries). It culminates in a group (or individual) research paper on a subject related to this theme. Music, politics and society II HUM-458 This course examines the presence of exotic musical characteristics (popular and folkloric) - in Europe and beyond - in art music (17th
Alexander MathisAlexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior.
In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.
Olga FinkTeaching & PhD PhD Students Han Sun, Leandro Von Krannichfeldt, Keivan Faghih Niresi, Zepeng Zhang, Vinay Sharma, Chenghao Xu, Raffael Pascal Theiler, Kevin Antonio Steiner, Sergei Garmaev, Amaury Wei Past EPFL PhD Students Ismail Nejjar, Mengjie Zhao Courses Data Science for infrastructure condition monitoring CIVIL-332 The course will cover the relevant steps of data-driven infrastructure condition monitoring, starting from data acquisition, going through the steps pre-processing of real
Johannes HentschelJohannes 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 HarasimI 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.
Christoph FinkensiepSince 2017, Christoph is a doctoral researcher at the Digital andCognitive Musicology Lab. He obtained his Master's degree in CognitiveScience at the University of Osnabrück with a thesis entitled"A Formal Model of Voice Leading" (2017). His Bachelor in ComputerScience at the University of Paderborn was completed with a thesisthat addressed the problem of "Automatic Composition of SemanticWebservices" (2014).His current research focuses on computational modeling of musicalstructure. Further scientific interests include music cognition,probabilistic modeling and machine learning, artificial intelligence,as well as philosophy of mind and philosophy of science. In addition, heis an active trombonist and occassional arranger, composer, and conductor.
Gabriele CecchettiAfter 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.
Steffen Alexander HerffSteffen graduated in Psychology at the Heinrich-Heine University Duesseldorf in Germany before going on to complete his PhD at the MARCS Institute for Brain, Behaviour, and Development focusing on 'Memory for Melody' in 2017, under the supervision of Prof. Roger Dean and Dr. Kirk Olsen.After his PhD, Steffen secured a DAAD-UA grant to conduct the "Noisy Ear" project in Germany, exploring context effects of background noise on memory before accepting a position at the Agency for Science, Technology and Research in Singapore, where he worked on Statistical Learning and memory paradigms in the auditory domain.Steffen's research is situated at the intersection between Memory, Music, and Neuroscience. His diverse interests and background have made him an integral part of many inter-disciplinary projects. These projects range from fundamental memory, learning, and perception research, over social negotiation between musical co-performers and the creation of new compositional tools, to non-invasive and invasive brain imaging in musical rhythm perception as well as brain stimulation to modulate memory encoding. Steffen strongly believes that inter-disciplinary research is the key to tackle complex, multi-facetted research questions.
Petter Harald EricsonPetter Ericson is a formal language and theoretical computing science expert, specifically focused on efficient graph grammars formalisms, who has joined the DCML as a Postdoctoral Researcher in 2019. He achieved his undergraduate degrees in Computing Science, as well as his PhD in the same subject, at Umeå University, investigating various topics within the general area of (parameterized) complexity theory, automata theory and formal languages, specifically looking at more complex structures such as graphs and trees. In particular, his work touched on applications in Natural Language Processing such as mildly context-sensitive syntax analysis and semantic modelling and processing.
An avid trombonist and amateur composer and arranger as well as a sometimes hacker and maker, his musical and research interests have previously intersected at the international Music Tech Fest events, where his presentations has garnered two hack camp wins, and a semi-regular place in the organising team.
Lazar MilikicExpertise
LLMs (Large Language Models), NLP (Natural Language Processing), Automated Reasoning
Mission
My work focuses on developing robust methods for autoformalization and automated theorem proving using large language models. I aim to enhance the reasoning reliability of modern AI systems by reducing hallucinations, improving predictability, and integrating formal verification techniques and tools into LLM-based pipelines.
Before joining EDIC, I completed a Bachelor’s in Mathematics and Computer Science
