I am a fourth-year PhD candidate in computational neuroscience at the M-Lab of Adaptive Intelligence (EPFL, Switzerland). My research lies at the intersection of machine learning and neuroscience, where I develop data-driven models to study and replicate how biological and artificial agents actively seek information in complex and uncertain environments. I work on representation learning, contrastive methods, and time-series analysis of high-dimensional behavioral and neural data. A key focus is understanding how vision and movement interact to support adaptive decision-making.

Beyond neuroscience, I am broadly interested in applications of machine learning to real-world problems — from intelligent systems and robotics to health and brain–computer interfaces. As my PhD progresses, I am extending my work to reinforcement learning, combining behavioral modeling and neural representations to study active information-seeking and adaptive strategies.