Staff Profile

Dr. Zhuang Shao is a Lecturer (Assistant Professor) in Data Engineering and AI in the School of Engineering at Newcastle University. His research focuses on trustworthy and deployable multimodal visual intelligence for physical-world perception, with particular interests in multimodal learning, vision-language models, model reliability and hallucination reduction, and scalable machine learning. He has published in leading venues including ACM Multimedia, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, and IEEE Transactions on Intelligent Transportation Systems.

Qualifications

Ph. D of engineering, University of Warwick, UK

Work Experience

• 2023-present: Lecturer (Assistant Professor) in Data Engineering and AI, Newcastle University.
• 2021-2023: Senior Graduate Teaching Assistant and Project Engineer, University of Warwick.

Selected Awards and Honours

• Academic Excellence 2022, Star Award, Warwick Manufacturing Group, University of Warwick, UK (Winner of 250+ PGRs).
• The Early Career Researcher Award of the 3^rd Annual Conference of Association of British Chinese Professors, 2022 (Winner out of 400+ participants).
• Contribution to Research 2023, Star Award, Warwick Manufacturing Group, University of Warwick, UK (Winner of all 900+ PGRs and academic staff).

Professional Services

• Guest Editor: IEEE Transactions on Circuits and Systems for Video Technology
• Independent Reviewers of impactful peer-reviewed journals: IEEE T-IP, T-MM, T-CSVT, Information Fusion, Knowledge-based Systems, IET Computer Vision, etc.

Dr. Zhuang Shao is a member of Microsystems Research Group. Please view my Google Scholar research profile.

His research focuses on trustworthy and deployable multimodal visual intelligence for physical-world perception. He is particularly interested in improving the reliability of multimodal AI systems, developing visual perception methods for real-world environments, and studying privacy- and safety-aware learning under distributed settings. His recent work spans multimodal visual understanding, hallucination reduction in vision-language models, physical-world sensing and deployable AI, and privacy risks in federated learning. 

Research Interests

• Multimodal Visual Intelligence
• Physical-World Perception and Deployable AI
• Trustworthy, Privacy- and Safety-Aware Learning

Research Fundings

• PI, NNZA: Heat Loss Detection and Management in UK Housing Using Computer Vision, EPSRC Northern Net Zero Accelerator Programme, 2026, £30,000.
• Co-I, EDABench: An Open-Source Benchmarking Framework for Machine Learning in Electronic Design Automation, April Hub Seeds Fund, 2025, £40,000.

PGR Supervision

I welcome applications from excellent and self-motivated PhD students with strong interests in computer vision, multimodal AI, and machine learning. Prospective students are encouraged to send their CV and a brief statement of research interests by email. Current and potential PhD topics include:

Recognise, Localise, and Comment: Evaluate Written Texts in Digital Images with Vision- Language Deep Learning Models

 Improving Reliability in Vision-Language Models: Reducing Hallucination in Multimodal AI Systems.

Undergraduate

• EEE2021: Computer Programming and Organisation

Postgraduate (MSc)

• EEE8166 Robotics and AI (module leader)
• EEE8097 Individual Project
• EEE8165 Research Skills and Development for Engineers

• Articles

  • Mao G, Rahman T, Maheshwari S, Pattison B, Shao Z, Shafik R, Yakovlev A. Dynamic Tsetlin Machine Accelerators for On-Chip Training using FPGAs. IEEE Transactions on Circuits and Systems - I: Regular Papers 2025, 72(11), 6962-6975.