I am currently a DPhil (PhD) student in Computer Science working with Prof. Yarin Gal at the University of Oxford. I’m interested in improving our theoretical understanding of deep learning, and making deep learning safer and more reliable for real-world applications.
I recently wrote my Master’s Thesis on pruning and generalization in deep neural networks in collaboration with Prof. Yarin Gal at the University of Oxford and Prof. Andreas Krause at ETH Zürich. Even more recently, at Cohere, I worked on making large language models faster and more efficient.
Previously, I obtained a MSc in CS from ETH Zürich, and a BSc in CS from ETHZ and Imperial College London. I undertook research on medical recommendation systems at IBM Research and ETHZ, and worked as a data scientist for BCG Gamma and QantEv, an Entrepreneur First-backed InsureTech start up.
In this project, we contributed an implementation of Boosting Black Box Variational Inference to the Pyro probabilistic programming library.
In this project we introduce low cost methods of improving dilated convolutions in an image segmentation application. We achieve comparable results to state-of-the-art segmentation performance while being computationally more efficient than previously proposed methods.
For this project we leverage matrix factorization and neural network methods to build a recommender system for movies.