Dr Yuhua Li

: Available for postgraduate supervision

Teams and roles for Yuhua Li

Reader
Computer Science And Informatics

I have conducted fundamental and applied research in machine learning, pattern recognition, data science, semantic similarity analysis and condition monitoring. I lead the Data Analytics and Machine Learning Research Group.

My experience in machine learning and pattern recognition includes statistical, geometrical methods and neural networks for feature/pattern selection and data analysis, knowledge discovery and inference.

My contribution to machine learning includes the development of anomaly/novelty detection methods for safety/mission-critical systems, which have limited or no data/knowledge on rare events, and informative observation selection techniques for sensors/measurements location optimisation for problems such as effective monitoring and process control. My works have motivated other researchers to develop new AI algorithms, use them as benchmarks and adopt them in products.

I have led and carried out research projects funded by the government, charity, and industry. I have collaborated on research projects with national and international companies of varying sizes. My research has been applied to healthcare, digital manufacturing, condition monitoring, financial engineering, and other real-world problems.

External Engagement

Member of the EPSRC Peer Review College
Academic Adviser to the Commonwealth Scholarship Commission UK
Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems

Data Analytics and Machine Learning Research Group

Research interests:

Machine learning, data science
Novelty detection, anomaly detection
Continual learning, adaptive learning
Explainable artificial intelligence
Data reduction, data augmentation
Hyperdimensional computing/vector symbolic architectures
Condition monitoring and signal processing
Machine learning and AI applications, e.g., healthcare technologies, finance, manufacturing

Current research grants:

AI Hub in Generative Models, EPSRC (grant number EP/Y028805/1), 02.2024 - 01.2029
SoundCheck: A Low-Fi, High-Tech Revolution in Respiratory Diagnostics, EPSRC (grant number UKRI3608), 03.2026 - 02.2030
HDC, 07.2026 - ...

Selected publications (more publications on Google Scholar).

Hanzhi Wang, Matthias Treder, Derek Jones, David Marshall, Yuhua Li (2023)
"A skewed loss function for correcting predictive bias in brain age prediction,"
IEEE Transactions on Medical Imaging, vol.42, no. 6, pp. 1577- 1589.
Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Liam Maguire (2018)
"A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks,"
IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, pp. 5394 - 5407.
Yi Cao, Yuhua Li, Sonya Coleman, Ammar Belatreche, Martin McGinnity (2016)
"Detecting wash trade in financial market using digraphs and dynamic programming,"
IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 11, pp. 2351-2363.
Junxiu Liu, Jim Harkin, Yuhua Li, Liam Maguire (2016)
"Fault tolerant networks-on-chip routing with coarse and fine-grained look-ahead"
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 35, no. 2, pp. 260-273.
Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Liam Maguire (2015)
"DL-ReSuMe: A delay learning based remote supervised method for spiking neurons,"
IEEE Transactions on Neural Networks and Learning Systems , vol.26, no.12, pp. 3137- 3149.
Xuemei Ding, Yuhua Li, Ammar Belatreche, Liam Maguire (2015)
"Novelty detection using level set methods,"
IEEE Transactions on Neural Networks and Learning Systems. vol. 26, no. 3, pp. 576-588.
Yi Cao, Yuhua Li, Sonya Coleman, Ammar Belatreche, Martin McGinnity (2015)
"Adaptive hidden Markov model with abnormal states for price manipulation detection,"
IEEE Transactions on Neural Networks and Learning Systems, vol.26, no.2, pp. 318-330.
Haider Raza, Girijesh Prasad, Yuhua Li (2015)
"EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments,"
Pattern Recognition, vol. 48, no. 3, pp. 659-669.
Yuhua Li, Liam Maguire (2011)
"Selecting critical patterns based on local geometrical and statistical information,"
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1189-1201.
Yuhua Li (2011)
"Selecting training points for one-class support vector machines,"
Pattern Recognition Letters, vol. 32, no. 11, pp. 1517-1522.
Yuhua Li, David McLean, Zuhair Bandar, James O'Shea, Keeley Crockett. (2006)
"Sentence similarity using semantic nets and corpus statistics,"
IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 8, pp. 1138-1150.
Yuhua Li, Zuhair Bandar, David McLean. (2003)
"An approach for measuring semantic similarity using multiple information sources,"
IEEE Transactions on Knowledge and Data Engineering, vol. 15, no.4, pp. 871-882.
Yuhua Li, Michael Pont, Barrie Jones (2002)
"Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where "unknown" faults may occur,"
Pattern Recognition Letters, vol.23, no.5, pp. 569-577.

I received a postgraduate certificate in higher education, I am a Fellow of the HEA. To teach:

CMT307 Applied Machine Learning
CMT316 Applications of Machine Learning: Natural Language Processing/Computer Vision

Current PhD students

I am interested in supervising PhD students in the areas of:

Machine learning, pattern recognition
Data science, Big Data, text mining
Neural networks, deep learning
Hyperdimensional computing, vector symbolic architectures
Machine learning and AI applications, e.g., healthcare, cyber security, finance and engineering

If you have a strong academic background and are highly motivated to pursue research excellence at the PhD level, please contact me at [email protected] with your CV and full transcripts. Due to the high volume of emails, I may not be able to respond to every enquiry. If you do not hear from me within two weeks, please assume your background is not a good match for my current research interests.

Listed below are examples of PhD project proposals.

Machine Learning for Health Monitoring and Early Disease Detection
Hyperdimensional Computing for Machine Intelligence in Resource-Constrained Applications
Online classification with emerging new classes
Learning concept evolution in data streams
Explainable machine learning for securing Internet of Things (IoT)