Professor Maozhen Li
Vice-Dean of the NCUT TNE programme/Professor

Research area(s)

• High performance computing - looking at computing technologies such as grid computing, peer-t-peer computing, cloud computing and edge computing with an aim to solve data intensive applications in the cloud or at the edge of the network. 
• Parallel machine learning techniques - improving computation efficiency of traditioanl machine learning techniques with parallel computing techniques, to speed up the training process on large datasets using multiple CPUs and GPUs.
• AI interpretation and robusness - AI models are normally running in a back-box mode and are fragile to real life out-of-distribution data sets. As a result, it is highly risky to deploy AI techniques to life criticial applications such as automonous driving systems. This work aims to develop interpretable and robust AI models which not only provide details on the decision-making process but also have the ability to sense the changes of an external environment.  A focus will be on Causal AI, a branch of AI which models cause-effect relationships, moving beyond correlation-based traditional AI predictions that can explain why things happen.
•  Lightweigh AI models - AI models have continuously growing in sizes having billions of hyperparameters which restrict them from deployment on computing resource constrained devices like robots and drones. This work looks at techniques such as pruning, quantisation, knowledge distillation, and lightweight architectures with an aim to reduce the computation complexity of AI models.

Grants

Project details

• PI, £147K, Industry Funding, Data Driven Digital Twin Modelling for Smart Manufacturing , Oct. 2025 - Sept. 2028, in collaboration with Sanhe Machinery Tools Ltd (A research project looking at data driven digital twin modelling techniques that can be applied by SMEs for productivity enhancement and carbon emission reduction).
• PI, £458K, Industry Funding, Video Data Driven AI for Enhancing Operation Safety in Smart Manufacturing, Oct. 2020 – Sept. 2023, in collaboration with Shandong East Engineering Tools Ltd (A research project looking at high performance real-time deep learning algorithms for abnormal operation detection and enhanced operation safety).
• CI, £195K, Innovate UK, KTP Project in Collaboration with HAES Systems Limited, Real Time Communication for Resilient Fire Alarm Systems, March 2019 – Feb 2021.
• CI, €730K, EU Horizon 2020, Z-BRE4K - Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories (Project ID: 768869), October 2017 – March 2021 (17 partners) (A multidisciplinary project looking at large scale predictive models for smart manufacturing).
• CI, €680K, EU Horizon 2020, IoRL, Internet of Radio-Light (Project ID: 761992), June 2017 – May 2020 (20 partners) (A multidisciplinary project looking at cloud computing and software defined networking for wireless communication in smart buildings empowered by lights).
• CI, €700K, EU Horizon 2020, TDX-ASSIST: Coordination of Transmission and Distribution data eXchanges for renewables integration in the European marketplace through Advanced, Scalable and Secure ICT Systems and Tools (Project ID: 774500), October 2017 – September 2020 (12 partners) (A multidisciplinary project looking at big data analytics mainly on data variety and veracity of smart power networks).