Mingming Liu

Mingming Liu

Associate Professor, School of Electronic Engineering, Dublin City University

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Dr Mingming Liu is a tenured Associate Professor in the School of Electronic Engineering at Dublin City University (DCU). He leads research on applied machine learning and trustworthy AI for sustainable, intelligent and data-driven systems, with applications in smart mobility, cloud systems, healthcare, environmental intelligence and energy systems.

His research group develops machine learning, optimisation, graph-based modelling, privacy-preserving analytics and decision-support methods for real-world cyber-physical and socio-technical systems. The work is strongly application-driven and is carried out in collaboration with academic, public-sector and industrial partners.

Before joining DCU, he was a Data Scientist, Applied Researcher and H2020 Project Lead at IBM Ireland Lab, where he worked on machine learning and applied optimisation for industrial systems. Before IBM, he was a Senior Postdoctoral Research Fellow at University College Dublin, working on EU and SFI-funded projects in control engineering, decision science and intelligent transportation systems.

He received his B.Eng. degree in Electronic Engineering with first-class honours, ranked first in his class, from National University of Ireland Maynooth in 2011, and his PhD in Control Engineering and Decision Science from the Hamilton Institute at the same university in 2015. His PhD thesis, “Topics in Electromobility and Related Applications”, was supervised by Prof Robert Shorten (Imperial College London) and Prof Sean McLoone (Queen’s University Belfast).

He is an IEEE Senior Member and a Fellow of the Higher Education Academy. He has published over 70 peer-reviewed papers in leading journals and conferences across machine learning, control, optimisation, intelligent transportation systems, smart grids, cloud computing, healthcare and sustainable cities. He serves as an academic editor for PLOS ONE and an associate editor for the Journal of Information Systems and Operational Research (INFOR).

Since 2018, he has secured more than €3 million in research funding as Principal Investigator. He is also the Irish national representative for EU COST Actions CA19126, CA20138, CA21131 and CA24121.

Research Themes

  • Trustworthy and Applied AI for Intelligent Systems
    Machine learning, deep learning, graph-based modelling, explainable AI, privacy-preserving learning and human-centred decision support.

  • Smart Mobility and Sustainable Cities
    Intelligent transportation systems, e-mobility, shared mobility, urban sensing, traffic simulation and AI-enabled digital twins.

  • Cloud Systems, SRE and Operational Intelligence
    Anomaly detection, multivariate time-series modelling, graph neural networks, MLOps/SRE and AI for cloud-native systems.

  • Healthcare and Environmental Intelligence
    Multimodal machine learning for medical imaging, air quality, pollen, environmental sensing and citizen-centred digital services.

  • Energy Systems and E-Mobility
    Smart grids, electric vehicles, energy optimisation, distributed decision-making and sustainable infrastructure.

  • Control, Optimisation and Cyber-Physical Systems
    Linear and nonlinear dynamical systems, distributed control, decentralised optimisation and learning-enabled decision systems.

Project Experience

Research Group

I lead the Applied Machine Learning Research Group at DCU. The group works on applied machine learning, trustworthy AI, optimisation and intelligent decision support for real-world systems, with current applications in smart mobility, cloud systems, healthcare, environmental intelligence and energy systems.

I welcome expressions of interest from highly motivated PhD candidates, postdoctoral researchers, visiting scholars and research collaborators. Prospective applicants should send me an email with a CV, academic transcripts, English-language certificate where applicable, and any supporting documents that demonstrate research capability and potential. Due to the volume of enquiries, only applicants whose profiles are a good fit for current opportunities should typically expect a response within one week, excluding holiday periods.

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