Hi there! I am a Senior Researcher at Microsoft Research in the AI4Science team where I am working at intersection of deep learning and partial differential equations (PDEs). On the ML side, my research spans a range of topics such as geometric & topological deep learning, graph neural networks and neural differential equations.
Previously, I finished my PhD at the University of Cambridge, supervised by Prof Pietro Liò and supported by a Microsoft Research PhD Fellowship (2021). I’ve also spent significant time in industry as a research intern at Microsoft Research (2022), Twitter Cortex (2021), Google Brain (2020), and as an AI Resident at Google X (2019). In 2019, I graduated with distinction the MPhil in Advanced Computer Science at Cambridge with a Best MPhil Student Award.
|Mar 2023||I have joined the AI4Science team at Microsoft Research as a Senior Researcher to work on hard problems at the intersection of machine learning and the natural sciences.|
|Feb 2023||Successfully defended my PhD thesis on Topological Deep Learning. My examiners were Prof Max Welling and Prof José Miguel Hernández-Lobato.|
|Jan 2023||In August, I will be giving an invited talk at the Mathematics of Geometric Deep Learning Minisymposium at the 10th International Congress on Industrial and Applied Mathematics.|
|Sep 2022||I mentored a research project on Sheaf Neural Networks at the London Geometry and Machine Learning Summer School. The paper was accepted to the NeurReps workshop at NeurIPS 2022.|
|Jul 2022||I was a lecturer at the First Italian Summer School of Geometric Deep Learning alongside Michael Bronstein, Francesco Di Giovanni, Pim de Haan and Maurice Weiler. My lectures cover a large part of my upcoming PhD thesis on Topological Deep Learning and argue why topology should play a central role in GDL. The recordings and the slides are available online.|
- Topological Deep Learning: Graphs, Complexes, SheavesPhD Thesis, University of Cambridge, 2022
- Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNsIn Advances in Neural Information Processing Systems, 2022
- Weisfeiler and Lehman Go Cellular: CW NetworksAdvances in Neural Information Processing Systems, 2021
- Weisfeiler and Lehman Go Topological: Message Passing Simplicial NetworksIn International Conference on Machine Learning, 2021
- Neural ODE ProcessesIn International Conference on Learning Representations, 2021
- On Second Order Behaviour in Augmented Neural ODEsAdvances in Neural Information Processing Systems, 2020
- Quantile QT-Opt for Risk-Aware Vision-Based Robotic GraspingIn Robotics: Science and Systems (Best Systems Paper Finalist), 2020