Prof Mary He
School of Science, Engineering & Environment
School of Science, Eng. and Environments
Current positions
Professor in A.I. for Robotics
Biography
Professor Hongmei (Mary) He (FHEA, SIEEE) is Chair Professor of Artificial Intelligence (AI) for Robotics and Autonomous Systems at the School of Science, Engineering, and Environment, University of Salford, Manchester, UK. She led Salford’s successful application to join the Turing University Network and now serves as the Salford Turing Liaison Academic Lead.
Professor He earned her PhD in Computer Science from Loughborough University in 2006, with a focus on optimisation. Her research career spans several leading UK institutions, including the University of Bristol, Ulster University, University of Kent, Cranfield University, and De Montfort University. Before entering academia, she worked as a senior embedded systems engineer at the Motorola Design House in China.
Her research tackles global challenges through the development of intelligent, safe, and ethically aligned systems that enhance human health, wellbeing, and quality of life. Aligned with global priorities—particularly the United Nations Sustainable Development Goals (SDG 3: Good Health and Wellbeing and SDG 11: Sustainable Cities and Communities)—her work promotes the responsible adoption of AI to build healthier, safer, and more equitable societies.
Professor He’s research is supported by major funding bodies, including EPSRC, Innovate UK, the EU, the UK Ministry of Defence, and various industry partners. She currently leads pioneering work in human-centred AI for trustworthy robotics and autonomous systems (RAS), with applications across healthcare, critical national infrastructure, and sustainable living environments.
A flagship paper titled “The Challenges and Opportunities of Human-Centred Artificial Intelligence for Trustworthy Robots and Autonomous Systems”, published in IEEE Transactions on Cognitive and Developmental Systems, received the Best AI and Robotics Research Award (Overall) from UK Responsive AI and the EPSRC TAS Hub on 12 March 2025.
To further advance the concept of Trustworthy Robots and Autonomous Systems in safety-critical healthcare settings, she co-organised the Hamlyn Symposium Workshop on Trustworthy Medical Robotics, held on 24 June 2025, in collaboration with leading experts in bioengineering and healthcare robotics.
Her research bridges foundational AI and real-world impact by integrating explainable AI, sensor-driven intelligence, human–machine interaction, and system resilience. This enables autonomous systems that are trustworthy, interpretable, and usable in complex, dynamic environments. In healthcare, she is developing AI-powered robotic assistants and decision-support tools to aid vulnerable populations, such as older adults and individuals with cognitive impairments. In the context of national infrastructure, her work contributes to the development of secure and dependable AI systems for smart environments and digital health platforms. She currently leads the international project “Robotic Caregivers: Recognizing the Needs of Elderly Individuals at Home”, in collaboration with the University of Dar es Salaam, Tanzania, funded by the IEEE RAS SPARX programme.
Deeply committed to ensuring that AI technologies address real societal needs, Professor He leads interdisciplinary research focused on developing transparent, inclusive, and value-driven solutions. While at De Montfort University, she led the PETRAS project “Cognitive and Socio-Technical Cybersecurity for Modern Railway Systems”, in partnership with Keele University and UCL. This work resulted in a holistic cybersecurity automation framework and was awarded a CyberASAP grant to support commercialisation.
Prof He demonstrates strong leadership in supervising and mentoring early-stage researchers and academic staff, supporting their development across research, teaching, and career advancement. She has successfully supervised numerous PhD students and postdoctoral researchers, many of whom have secured positions in academia or industry. Her mentorship style is inclusive and strategic, promoting intellectual independence while offering tailored support in research planning, publishing, grant applications, and professional networking.
She holds several leadership roles in the international AI and robotics community: she is Chair of the IEEE UK & Ireland Robotics and Automation Society Chapter, a member of the IEEE RAS Cognitive Robotics Technical Committee, and Chair of the AI and Edge Computing for Trustworthy RAS Task Force within the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee (ADPRLTC) of the IEEE Computational Intelligence Society.
She is a member of the EPSRC Peer Review College, and in 2021/2022, her contribution was ranked in the top 4% by EPSRC. She has also served as an expert panel member for the EU H2020 ICT Robotics Programme (in 2016 and 2019) and participated as a panel member in the assessment of EPSRC Centres for Doctoral Training (CDTs) in Security and Defense.
Areas of Research
Brain–Computer Interfaces for HRI
Multi-Agent Robotic Swarming
Bio-Inspired Navigation
Lightweight Interpretable AI for Edge Health Devices
Multimodal Emotional Intelligence
System and Performance Optimization
Trustworthy Robotics and Autonomous Systems
AI-driven Systems in Healthcare, Transport and National Infrastructure
Areas of Supervision
Machine Learning for Health Data Analysis
Emotion Analysis and Pattern Recognition
Trusted Human-Robot Interaction
Cognitive Cybersecurity
Cognitive Robotics
Homecare robotics
AI and Robotics Ecosystems
Articial Intelligence, MSc, module lead
Mobile Robotics, MSc, module lead
MSc dissertation coordination for four programmes
Qualifications
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PhD in Computer Science
2003 - 2006 -
MSc in Multimedia and Internet Computing
2002 - 2003
Recognitions
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Chair of IEEE UK and Ireland RAS Chapter
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Chair of Task Force in AI and Edge Computing for Trustworthy Robotics and Autonomous Systems
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Working Group Member of IEEE SA P1912: Standard for Privacy and Security Framework for Consumer Wireless Devices
Publications
- Capsule network with using shifted windows for 3D human pose estimation
- A Novel Fuzzy Logic Framework for Model Reliability Evaluation in Permeability Prediction Using GPR
- Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements
- Machine Learning in Oil and Gas Exploration: A Review
- A Novel Framework for Reservoir Permeability Prediction Using GPR with Grey Relational Grades and Uncertainty Quantification