Dr Boyu Kuang

Areas of expertise

• Aerospace Manufacturing
• Carbon, Climate and Risk
• Computing, Simulation & Modelling
• Engines, Powertrains & Alternative Fuels
• Industrial Automation
• Instrumentation, Sensors and Measurement Science
• Process Systems Engineering

Background

Dr Boyu Kuang is a Research Fellow in Computer Vision and Artificial Intelligence at Cranfield University, affiliated with the Centre for Computational Engineering Sciences. He is actively involved in the UKRI, ATI, and Airbus-funded ONEHeart project, focusing on autonomous systems and intelligent inspection technologies for aviation. He has previously contributed to EU-funded projects WINDY and ICECREMO, addressing aircraft wing icing challenges through advanced 3D reconstruction and computer vision techniques. Since 2021, Dr Kuang has published over 30 peer-reviewed papers, including 18 in JCR Q1 journals, with more than 720 citations, an h-index of 12, and an h10-index of 13. His work has appeared in top journals such as IEEE Transactions on Cybernetics (T-Cyb, IF = 9.4), Chemical Engineering Journal (CEJ, IF = 13.4), Expert Systems with Applications (ESWA, IF = 7.5), and the International Journal of Mechanical Sciences (IJMS, IF = 7.1).

Dr Kuang’s research interests span semantic segmentation, object detection, weakly and self-supervised learning, active learning, multi-modal perception, and vision-language foundation models. He focuses on developing intelligent visual systems that operate reliably in low-resource, high-noise industrial environments, emphasising lightweight deployment, cross-domain adaptation, and multi-sensor fusion. His work finds application in aviation maintenance, robotics, and energy infrastructure monitoring, and he collaborates closely with academic and industrial partners across the UK, Europe, and the United States.

Beyond his research, Dr Kuang serves as module lead for Artificial Intelligence and Machine Learning at Cranfield University. He is an Editorial Board Member for Discover Artificial Intelligence (Springer Nature), has delivered keynote speeches, chaired international conference sessions, and served on multiple programme committees. As a peer reviewer, he has assessed nearly 60 manuscripts for leading journals including IEEE Transactions on Image Processing (TIP), IEEE Transactions on Industrial Electronics (TIE) and Elsevier Neural Networks (NNs). Leveraging state-of-the-art facilities such as DARTeC and AIRC, he welcomes collaboration with both academic and industrial partners and invites applications from motivated PhD students interested in cutting-edge AI research.

Research opportunities

1. Visual Intelligence and Multimodal Perception

This theme focuses on robust scene understanding through cutting-edge methods in semantic segmentation, object detection, and multi-modal sensor fusion. We explore perception under challenging industrial conditions using RGB, depth, infrared, and language modalities, aiming to equip autonomous systems with human-level environmental awareness. Research includes cross-domain generalisation, domain adaptation, and lightweight vision models, with applications in aerospace inspection, robotic vision, and smart infrastructure monitoring.

2. Low-Supervision Learning and Adaptive Intelligence

We develop AI systems that can learn effectively with minimal supervision by leveraging weakly-supervised, self-supervised, and active learning strategies. This direction targets data-scarce and label-expensive environments, enabling adaptive intelligence in aviation, manufacturing, and energy systems. Research includes dataset-efficient model training, uncertainty-aware learning, and human-in-the-loop systems, supporting safe, scalable deployment in real-world conditions.

3. Foundation Models and Vision-Language Integration

We investigate the use of vision-language foundation models and generative AI to advance understanding, reasoning, and interaction in intelligent systems. This includes exploring zero-shot/few-shot generalisation, prompt-based adaptation, and multimodal grounding for robotics and automation tasks. The aim is to build next-generation AI agents that seamlessly integrate visual and linguistic information, facilitating more flexible, interpretable, and autonomous decision-making.

Current activities

Dr. Boyu Kuang is actively contributing to several interdisciplinary projects that integrate artificial intelligence, computer vision, and robotics to address complex real-world challenges. As part of the UKRI, ATI, and Airbus-funded ONEHeart project, he is exploring cutting-edge technologies to enhance automation and decision-making processes in aviation. His work focuses on advancing AI-driven systems that improve efficiency, safety, and sustainability across industrial applications.

At Cranfield University, Dr. Kuang leads teaching modules on Artificial Intelligence and Machine Learning, where he combines theoretical insights with hands-on applications to prepare students for real-world challenges. He is also engaged in academic collaborations with institutions such as Stanford University, King’s College London, and the Civil Aviation University of China, as well as industry partners like Airbus and Leidos. These partnerships ensure that his research aligns with both academic advancements and practical industry needs.

Dr. Kuang’s ongoing research emphasises the development of advanced perception systems, data-driven modelling, and sustainable automation solutions. By leveraging Cranfield University’s state-of-the-art facilities, including the Aerospace Integration Research Centre (AIRC) and the Digital Aviation Research and Technology Centre (DARTeC), his work continues to contribute to innovative solutions that support the global transition towards more efficient and environmentally sustainable technologies.

Clients

• Airbus SE
• Renault SA
• BAE Systems PLC
• Turkish Aerospace Industries

Publications

• Articles in journals
• Conference papers