People
Principal Investigator
James Le Houx
Senior Lecturer, University of Greenwich · Faraday Institution / STFC Emerging Leader Fellow
James founded the BASE Lab in 2025. His research develops autonomous X-ray imaging platforms for energy materials, combining synchrotron experiments with AI and high-performance simulation. He holds a PhD from the University of Southampton and is a Chartered Engineer (CEng, IMechE). Before academia, he was a design engineer in top-tier motorsport (Xtrac Ltd.) and a beamline scientist at Diamond Light Source.
He leads the UK’s Battery Imaging BAG at Diamond Light Source, serves on the ISIS Science Council, and advises DSIT on AI data standards for materials science. He developed the Faraday Engine, a national initiative connecting UK Exascale computing to national facilities.
PhD Students
Calum Green
Applications of Deep Learning for Super-Resolution and Data Fusion of 3D X-ray Imaging Datasets for Materials Characterisation
Imperial College London · Co-supervised with Prof. Daniele Dini · Completing July 2026
Calum develops generative super-resolution methods and multi-modal data fusion pipelines for synchrotron X-ray imaging. His work includes EXG-GAN, a generative adversarial network for upsampling spatially resolved 3D tomographic data, and methods for fusing XRD-CT and XCT datasets for autonomous materials segmentation. He is building OpenLSR-X, an open-source PyTorch package for learnable super-resolution of XCT data.
Available for postdoctoral or industry positions from July 2026. Contact Calum or James to discuss.
Key publication: Three-dimensional, multimodal synchrotron data for machine learning applications — Scientific Data (2025)
Jon Valijonov
Data-Driven Image Mechanics (D2IM): A Deep Learning Approach to Predict Displacement and Strain Fields in Biological Tissues from X-ray Tomography
University of Greenwich · Co-supervised with Prof. Gianluca Tozzi and Dr Peter Soar
Jon is developing D2IM, a deep learning framework that predicts full-field displacement and strain directly from undeformed X-ray tomography images. His initial results show that direct strain prediction significantly outperforms displacement-derived approaches, reducing false-positive high-strain classifications by 75%. The project is expanding D2IM to a range of hard and soft biological tissues and loading scenarios, using Greenwich’s InCiTe 3D X-ray microscope for sub-micron resolution phase-contrast imaging.
First preprint: D2IM-Strain (bioRxiv, 2026)
Isabella Florez
Quantum-AI Synergy for Next-Generation Imaging of Biological Tissues
University of Greenwich
Isabella is investigating quantum-native representations of biological tissue images to enable AI operations — classification, segmentation, measurement, and multi-modal data fusion — that are intractable with classical approaches alone. Her work surveys hybrid quantum-classical architectures for biomedical imaging and is developing toward Quantum Image Correlation (QuIC), a quantum extension of digital volume correlation for high-dimensional volumetric data.
MSc Students (2025–26)
Asif Ali
Generative AI for Digital Twins of Battery Electrodes
University of Greenwich
Asif is training diffusion models on 3D X-ray tomograms of anisotropic battery electrodes (carbon felt) to generate synthetic microstructures that are statistically indistinguishable from real data. The generated volumes will serve as inputs for physics-based simulation with OpenImpala, addressing the data scarcity bottleneck in computational materials science. Running on Isambard-AI.
Ali Butt
The Battery Explorer: Interactive Web-Based Dashboard for 4D Scientific Data
University of Greenwich
Ali is building an interactive web application for exploring 4D battery tomography data — scrubbing through time, rotating 3D volumes, and viewing synchronised derived quantities (e.g., crack volume vs. cycle number). The tool is designed to make large-scale operando datasets accessible to experimentalists without specialist visualisation software.
Insha Amreen
Guided Super-Resolution for Multimodal Scientific Imaging
University of Greenwich
Insha is developing guided super-resolution methods that use high-resolution structural data (XCT) to intelligently upsample low-resolution chemical maps (XRD-CT), producing unified high-resolution datasets containing both structural and chemical information. The work builds on the FeatUp framework and our group’s multimodal synchrotron dataset.