About. I’m a first-year PhD student in Computational & Applied Mathematics at Stanford University, passionate about AI-for-Science and data-driven discovery.

Research Interests. I am especially interested in how we can leverage modern advances in representation learning, multi-modal learning and foundation models to accelerate and enable scientific discovery in data-intensive disciplines like astronomy, biology, neuroscience, and the social sciences.

Background. I hold a Master's degree in Aerospace Engineering from Imperial College London and Data Intensive Science from the University of Cambridge. I completed one of my Master's theses at the Harvard-Smithsonian Center for Astrophysics, where I developed the first representation learning based anomaly detection approach to search for rare high-energy transients in large astronomical catalogs. As part of this work, I discovered an intriguing new extragalactic fast X-ray transient with features that have never been observed before - a truly anomalous needle-in-the-haystack event. My work experience includes data science and engineering internships at BMW, DLR, ESA, Airbus, Amazon and NASA JPL.