20th-21st MAY 2025 – LONDON, UK

Mark Roboff

Mark Roboff

  • Chief Vision Officer | G34 Aerospace AI Implementation and Certification Committee
  • SkyThread | SAE International

Mark Roboff is a co-founder of SkyThread.aero, a new venture building the future of digital connectivity for the aviation industry, as well as founder and board member of the Independent Data Consortium for Aviation. Previously, Mark was General Manager for Digital Transformation, Aerospace & Defense at DXC and prior to that, he served as Global Solutions Leader, Aerospace and Defense (A&D,) at IBM.

Mark has over 15 years’ experience in aviation digital transformation—both as a software engineer and as a business and technology executive. Mark is a recognized thought leader on Artificial Intelligence and Machine Learning as well as MRO data, processes, and analytics. Mark has worked on and contributed to many of the industry’s largest digital MRO platforms, and he has also worked with dozens of airlines across the globe on prognostics, predictive maintenance, and the optimization of maintenance execution.

Mark is also the chair of the SAE-G34/EUROCAE WG-114 Joint International Committee for AI in Aviation, and is leading 500+ aerospace engineers, software developers, data scientists, safety experts, and regulators to define a means of compliance for AI certification.

Sessions

  • AI and ML in Testing

    AI and ML have provided some great opportunities for assisting speed and quality of testing, when such large amounts of data require analysis, and highlighted the importance of a robust testing strategy for AI systems. While AI can effectively analyze and digest large datasets, ensuring the quality of the AI algorithm and the accuracy of its model is paramount. Factors such as avoiding biases, balancing accuracy with computational costs, and considering on-board processing limitations must be carefully addressed. How can AI assist when an aircraft is in flight, no longer ‘connected’? Qualifying an AI tool involves defining parameters, setting targets, and identifying patterns within the data. How can accuracy be achieve with several AI algorithms running concurrently?

  • Digitalisation and New Tools for Testing and Certification

    The evolving landscape and digitalisation of avionics hardware and software testing and certification brings opportunities, but also challenges. Staff shortages and the need for skilled engineers with digital capabilities are significant concerns, as well as there is a growing interest in model-based approaches and alternative languages. Addressing these challenges requires effective training programs and strategies to attract new talent. The digital transformation of avionics testing necessitates a proactive approach to workforce development and the adoption of advanced testing methodologies, such as testing for data leaks and exploring the potential of virtual health monitoring or formalising Human Machine Interface (HMI) certification in the context of avionics systems. It is essential to invest in innovative testing tools, methodologies and digitisation to ensure the safety and reliability of software systems.