Meet the Team
A multidisciplinary team of researchers, engineers, and domain experts working at the intersection of AI and air traffic management — spanning NATS, The Alan Turing Institute, and the University of Exeter.
Research Theme 1 — Digital Twin
Research Theme 3 — Assurance
Data Team
Previous Collaborators
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Team
Research Theme 1 — Digital Twin
Role
Project Bluebird industry co-lead / Research Software Engineer, R&D
Dr. Marc Thomas
Project Bluebird industry co-lead / Research Software Engineer, R&D
Biography
Marc is the industrial Digital Twin lead for Project Bluebird. He is a researcher and data scientist, and has worked in healthcare, academia and industry. Marc first trained as a medical doctor and worked for 8 years in the NHS before studying physics, completing a PhD and postdoctoral research in Theoretical Physics at Southampton University, where he studied the signatures of Higgs Physics, Supersymmetry and Dark matter at the Large Hadron Collider in CERN. In 2016 he moved to a start-up as the company's first data scientist, where he used signal processing and machine learning to develop real-time fault detection and predictive maintenance algorithms for the rail industry using novel IoT technology. At NATS, he has previously conducted research into biometrics, eye-tracking and speech-to-text. Dr. Thomas holds degrees in Medicine (MBBS), Theoretical Physics (BSc, PhD) and Data Science and Artificial Intelligence (MSc) as well as professional medical qualifications, and is currently undertaking a part-time PhD in Digital Twinning and AI at Cambridge with Professor Mark Girolami as part of Project Bluebird.
Publications
- Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach
AIAA SciTech Forum, 2026
- Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace
AIAA SciTech Forum, 2026
- A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace
AIAA SciTech Forum, 2026
- Human-in-the-Loop Testing of AI Agents for Air Traffic Control with a Regulated Assessment Framework
AIAA SciTech Forum, 2026
- A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control
AIAA SciTech Forum, 2026
- A Sector-Specific Probabilistic Approach for 4D Aircraft Trajectory Generation
Transportation Research Part C, 2025
- Learning Generative Models for Climbing Aircraft from Radar Data
Journal of Aerospace Information Systems, 2024
- A probabilistic model for aircraft in climb using monotonic functional gaussian process emulators
Proceedings of the Royal Society A, 2023