IMPRS-IS PhD scholar at the Prof. Hennig Lab (Methods of Machine learning, University of Tübingen, Germany), focused on phase-aware deep learning optimizers.

Co-authored the IAMúsica project, an artistic research grant supported by the Institut d’Estudis Baleàrics aimed to apply real-time AI technologies for musical analysis. More info here.

Contract with the Max-Planck Institut for Empirical Aesthetics (ArtLab) to develop a lightweight, wearable, precise heartbeat timestamping and counting system. F1 detection score above 99.7% (based on Pan-Tompkins) and AUC-ROC >80% for signal quality estimation (custom Kalman filter).

Work as machine learning research engineer at the AI for Sound research fellowship led by Prof. Mark Plumbley at CVSSP (University of Surrey, UK). Work included participatory studies, supervision of M.Sc. theses, and covering the full lifecycle of AI software on a broad variety of settings.

Member of the research team led by Dr. Julia F. Christensen, focused on the role and effects of dance in different cultures and from different points of view such as emotional, aesthetic or perceptual. My role involves using computers to generate synthetic stimuli and extract kinematic features from video and MoCap sequences (see this post for more details).

Work on the RESIST research grant (ERC) for resilient transport infrastructure to extreme events. Work involved R&D, profiling and maintenance of models for visual detection and measurement of structural defects, as well as interfacing with structural and system engineers.

Assisting with machine learning research at the Systems Engineering and Computer Vision department.

Leading weekly tutorials for 60+ undergraduate students as part of the Python Programming course led by Prof. Krömker. Work included introducing good software engineering practices, grading, and giving constructive feedback on theoretical and practical exercises.