A foundation model for electrodermal activity data




Martin Gjoreski is an Ambizione Fellow supported by the Swiss National Science Foundation (SNSF) and a Scientific Collaborator at the People-Centered Computing Lab, within the Faculty of Informatics at Università della Svizzera italiana (USI). His primary research areas are artificial intelligence — specifically machine learning, federated learning, and explainable AI (XAI) — applied to wearable computing, affective computing, and digital healthcare.
Education & recognition
Martin earned his PhD in Computer Science from the Jožef Stefan Institute in Slovenia under the supervision of Profs. Matjaž Gams and Mitja Luštrek. His doctoral thesis received the Jožef Stefan golden emblem, awarded to outstanding PhD theses in Slovenia. In 2021 he was included in the list of the top 2% scientists in the world for single-year impact.
Professional service
- Associate Editor, ACM IMWUT (Interactive, Mobile, Wearable and Ubiquitous Technologies)
- Board Member, Global SNSF Fellows Network
I am a postdoctoral researcher at the Faculty of Informatics at the Università della Svizzera Italiana (USI) in Lugano, Switzerland, where I work in Prof. Silvia Santini and Prof. Marc Langheinrich’s group. I hold a Ph.D. in Computer Science from the University of Liverpool (UK) and a Master’s degree in experimental particle physics from the University of Pisa (IT), having conducted my thesis work within the Mu2e experiment at Fermi National Laboratory (US).
My research centers on interpretability-by-design: building deep learning models whose reasoning is interpretable by construction rather than explained after training, which makes them steerable and verifiable. To this end, I work on standardizing the underlying theory and code interfaces and on adding structure to the interpretable intermediate space.
Among data modalities, I focus particularly on time series, including forecasting, classification, representation learning, and virtual sensing, which I study through problems in the natural sciences and physiological signals. I am also a co-creator of the open-source machine learning library PyTorch Concepts.
