I’m a PhD researcher at Imperial College London working on machine learning for planetary imagery and remote sensing. My work combines deep learning, scientific image processing, and large-scale data pipelines to build methods for reconstruction, mapping, and analysis across both high-resolution and planetary-scale datasets.
I’m particularly interested in representation learning, image reconstruction, global mapping, and foundation-model-style approaches for scientific and remote sensing data.
Research focus
- HiRISE calibration and processing for ML-ready planetary imagery.
- Computer vision for reconstructing missing image data in high-resolution Mars images.
- Foundation-model representation learning for global Mars mapping from multimodal datasets, supporting target identification with HiRISE and CaSSIS.
Selected projects
A few representative projects. For more detail and additional work, see the projects page.
HiRISE Band Reconstruction and Data Processing
HiPredict is a deep learning and scientific data processing framework for reconstructing missing image data in high-resolution Martian imagery from the HiRISE instrument on Mars Reconnaissance Orbiter, motivated by the intermittent failure of the central RED4 detector since 2023.
Reinforcement Learning for Atmospheric Balloon Station Keeping
Imperial College Space Society project exploring reinforcement learning for atmospheric balloon station keeping, modelled on Google's stratospheric balloon initiative. I serve as AI Lead for the 2025-26 team.
Supervised Water-Ice Detection on the Moon
MSc thesis project on predicting water-ice presence at the lunar poles using supervised learning and multimodal remote sensing data. The work combined Diviner, LOLA, M3, and Mini-RF products to score points across the polar surface, extending beyond permanently shadowed regions alone.
Collaborations & affiliations
I’ve studied and worked with teams at:
