Curriculum vitae

Distinguished Engineer NVIDIA

Computational engineering researcher and technical leader working at the intersection of scientific machine learning, high-fidelity simulation, foundation models, and agentic AI for verifiable and traceable engineering workflows. Experience spans research and product direction, open scientific datasets, international benchmarking programmes, high-performance computing, and industrial deployment.

Updated July 2026

Portrait of Neil Ashton

Experience

  1. Jan 2025–present

    Distinguished Engineer

    NVIDIA United Kingdom

    • Set research and product direction for accelerated computational engineering, AI physics, simulation, and engineering AI.
    • Lead work on foundation models, scientific machine learning, and agentic architectures, with emphasis on tool orchestration, physical verification, provenance, uncertainty, failure recovery, and human oversight.
    • Work across research, engineering, product, and external partners to translate advances into dependable computational-engineering systems.

  2. Feb 2020–Jan 2025

    Worldwide Technical Lead for Computer-Aided Engineering

    Amazon Web Services

    • Led worldwide technical strategy for CAE across high-performance computing, machine learning, cloud services, and partner technologies.
    • Founded and led a cross-Amazon machine-learning-for-CAE initiative, connecting product development, research, and industrial adoption.
    • Provided technical leadership for open, high-fidelity datasets including AhmedML, WindsorML, and DrivAerML.

  3. Jul 2016–Jan 2020

    Senior Researcher

    Osney Thermofluids Institute, Department of Engineering Science, University of Oxford

    • Led vehicle-aerodynamics research spanning high-fidelity CFD, turbulence modelling, multiphysics, and high-performance computing.
    • Supervised doctoral research and led collaborations with academic, government, and industrial partners.
    • Founded Oxford University Racing and chaired the 10th International Conference on Computational Fluid Dynamics.

  4. Jul 2017–Dec 2019

    Consultant

    Formula 1 Management

    • Provided CFD and HPC expertise to the motorsport team, co-authored the CFD/HPC sections of proposed FIA Sporting Regulations, reviewed Technical Regulations, and helped establish a cloud-based OpenFOAM process.

  5. Jul 2016–Dec 2019

    Consultant

    British Cycling

    • Conducted the high-fidelity CFD programme for the Great Britain track bicycle developed for the Tokyo 2020 Olympic Games, including more than 1,000 transient simulations.

  6. Jan 2016–Jul 2017

    Consultant

    Williams Advanced Engineering

    • Established CFD and aerodynamic methods for Formula 3, Le Mans, and touring-car programmes.

  7. Jan–Jul 2016

    Visiting Scholar

    Advanced Supercomputing Division, NASA Ames Research Center

    • Conducted large-scale computational aerodynamics research for transonic and supersonic aircraft with the Applied Modeling and Simulation group.

  8. 2013–2015

    EPSRC Impact Acceleration Research Associate

    University of Manchester

    • Developed turbulence models for automotive and aerospace applications and contributed to the EU FP7 Go4Hybrid programme.
    • Undertook a one-month research visit to NASA Ames Research Center in February 2015.

  9. 2012–2013

    CFD Engineer

    Lotus F1 Team

    • Developed computational-fluid-dynamics and high-performance-computing methods within the Formula 1 aerodynamics group.

Research leadership and public scholarship

Automotive CFD Prediction Workshop (AutoCFD)

Founder and organiser, 2019–present

Founded the international workshop series to establish open test cases, reproducible comparisons, and shared research priorities for automotive CFD; currently co-leads its AI/ML Technical Focus Group.

AIAA CFD High-Lift Prediction Workshop (HLPW)

Organising Committee and AI/ML Technical Focus Group Lead

Helps direct the international aerospace benchmarking programme and leads its work on machine-learning datasets, evaluation, and physically meaningful model comparison.

CAE ML Datasets

Research lead and collaborator

Leads and collaborates on AhmedML, WindsorML, DrivAerML, and HiLiftAeroML, an open collection of more than 3,100 high-fidelity automotive and aerospace simulation samples.

The Neil Ashton Podcast

Founder and host, 2024–present

Long-form conversations with researchers and engineering leaders on AI, scientific computing, simulation, aerodynamics, and research practice.

Education

2008–2013

PhD, Aerospace Engineering

University of Manchester

Thesis: Development, Implementation and Testing of an Alternative DDES Formulation Based on Elliptic Relaxation. EPSRC funded.

2004–2008

MEng (Hons), Aerospace Engineering – First Class

University of Manchester

Professional standing

  • Fellow, Institution of Mechanical Engineers Since 2020
  • Chartered Engineer, Institution of Mechanical Engineers Since 2013
  • Senior Member, American Institute of Aeronautics and Astronautics Since 2014
  • Member, NAFEMS CFD Working Group Since 2014
  • Expert reviewer, European Commission Since 2017
  • Member, IMechE Technical Strategy Board and Education Advisory Group 2017–2021

Selected publications

Full publication record

  1. 2026

    Neil Ashton et al. HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics. arXiv preprint.

  2. 2025

    Neil Ashton, Johannes Brandstetter, and Siddhartha Mishra. Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics. arXiv preprint.

  3. 2024

    Neil Ashton et al. WindsorML: High-Fidelity Computational Fluid Dynamics Dataset for Automotive Aerodynamics. Advances in Neural Information Processing Systems 37, 37823–37835.

  4. 2024

    Neil Ashton, Paul Batten, Andrew Cary, and Kevin Holst. Summary of the 4th High-Lift Prediction Workshop Hybrid RANS/LES Technology Focus Group. Journal of Aircraft 61(1), 86–115.

  5. 2023

    Neil Ashton and William Van Noordt. Overview and Summary of the First Automotive CFD Prediction Workshop: DrivAer Model. SAE International Journal of Commercial Vehicles 16(1), 61–85.

  6. 2021

    Jamil Appa, Mike Turner, and Neil Ashton. Performance of CPU and GPU HPC Architectures for Off-Design Aircraft Simulations. AIAA SciTech 2021.

  7. 2016

    Neil Ashton, Alastair West, Sylvain Lardeau, and Alistair Revell. Assessment of RANS and DES Methods for Realistic Automotive Models. Computers & Fluids 128, 1–15.

  8. 2013

    Neil Ashton, Alistair Revell, Robert Prosser, and Juan Uribe. Development of an Alternative Delayed Detached-Eddy Simulation Formulation Based on Elliptic Relaxation. AIAA Journal 51(2), 513–519.

Selected invited talks

Talks archive

  • Jul 2026

    Sponsored keynote

    SIGGRAPH 2026

    NVIDIA - Next Era of Graphics - Neural Rendering, World Models, and Simulation

  • May 2026

    Invited seminar

    University of Oxford Mathematical Institute

    Towards a Foundation Model for Computational Engineering: Opportunities, Challenges, and Novel Scaling Laws

  • Apr 2026

    Invited seminar

    NASA Ames Seminar Series

    Perspectives on Foundation Models and Agentic Architectures for Computational Fluid Dynamics

  • May 2025

    Invited talk

    NASA High-Fidelity CFD Workshop

    Enabling Industrially Relevant High-Fidelity CFD and AI Surrogate Models for External Aerodynamics

  • Apr 2023

    Invited keynote

    22nd International Computational Fluids Conference

    The Role of Cloud Computing, Machine Learning, and HPC in the Advancement of High-Fidelity CFD for Industry

  • Mar 2023

    Invited seminar

    MIT ACDL Seminar Series

    The Role of Cloud Computing, Machine Learning, and HPC in the Advancement of High-Fidelity CFD for Industry

Contact and profiles

contact@neilashton.co.uk Google Scholar ORCID LinkedIn