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
Team Profile
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Team
Research Theme 1 — Digital Twin
Role
Project Bluebird academic co-lead / Senior Research Associate
Dr. Nick Pepper
Project Bluebird academic co-lead / Senior Research Associate
Biography
Nick is a post-doctoral researcher at the Alan Turing Institute, working on Trajectory Prediction and Uncertainty Quantification. He holds a PhD from Imperial College London, where his research, co-sponsored by the EPSRC and Airbus, focussed on developing Machine Learning methods for accelerating the design process of aeronautical components. More recently, Nick was seconded to the NASA Langley Research Centre where he developed a novel Adaptive Learning algorithm for reliability analysis. Nick also holds a master's degree in Physics from the University of Oxford and an MSc in Computational Methods for Aeronautics from Imperial College London.
Publications
- Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
US-Europe ATRD Symposium, 2026
- 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
- AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models
NeurIPS LAW Workshop, 2025
- A Sector-Specific Probabilistic Approach for 4D Aircraft Trajectory Generation
Transportation Research Part C, 2025
- Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
AIAA AVIATION Forum, 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