Short bio
Professor at the Ecole Normale Supérieure since 2019. BS & PhD in Applied Mathematics (Caltech & University of Washington), followed by a neuroscience postdoc (University College London). FENS-Kavli scholar 2023. ERC consolidator grant 2024.
Topics of interest
Data science, Machine learning, Statistical modelling, Dynamical systems, NeuroAI
Project in Prairie
Alex Cayco Gajic will work at the intersection of machine learning and neuroscience to understand learning dynamics in the brain. She will approach this question from both data-driven and theoretical angles. First, she will develop statistical models to identify how latent dynamics in neural data evolve over the course of learning. Second, she will build neural network models to investigate how neural computations are learned through biologically plausible learning rules.
Quote
In neuroscience, advances in recording technologies have enabled unprecedented access to large populations of neurons over learning. Simultaneously, the surge of progress in AI has inspired new insight into how artificial neural networks learn. However, we still lack the mathematical tools necessary to develop theoretical principles of learning and computation that are squarely rooted in neural data. Bridging this gap will be essential both to uncover how the brain controls complex behaviours, and to inspire new forms of brain-inspired artificial intelligence.