Marc Andres Carcasona

Marc earned his PhD in Physics from the Autonomous University of Barcelona (UAB) and the Institut de Física d’Altes Energies (IFAE) in 2025, conducting research stays at MIT, Caltech, and the European Gravitational Observatory (EGO). He also holds a BSc in Aerospace Engineering (UPC), a BSc in Economics (UOC), an MSc in High Energy Physics, Astrophysics & Cosmology (UAB), and a MSc in Financial Risk Management (UNIR).

His research focuses on scattered light modeling and mitigation in gravitational-wave detectors, the search for primordial black holes (PBHs), and the application of machine learning (ML) techniques to gravitational-wave detection. During his PhD, Marc played a key role in designing the baffles for the main arms of the Einstein Telescope (ET), and in simulating the instrumented baffles currently being developed for the Virgo experiment. He has also contributed to multiple PBH searches using LIGO-Virgo-KAGRA data, including a Bayesian population analysis of the observed events, the development of a pipeline to search for continuous-wave signals from the early stages of mergers, and a ML pipeline aimed at detecting signals from highly mass-asymmetric systems. At MKI, his primary focus is on scattered light modeling, mitigation strategies, and baffle design for Cosmic Explorer (CE), the next-generation ground-based gravitational-wave observatory in the United States.

In addition to his research, Marc has taught at EADA business school and the Toulouse Business School (TBS) in Barcelona, delivering graduate-level courses in artificial intelligence, data analytics, and quantitative finance, bridging the gap between science and economics.