Prof Wai Lok Woo received the BEng and MSc degrees in Electrical and Electronics Engineering, and the PhD degree in Statistics and Machine Learning from the Newcastle University, UK. He is the recipient of the IEE Prize and the British Commonwealth Scholarship.

Prof Woo currently holds the Chair in Machine Learning with Northumbria University, UK. He is the Faculty Postgraduate Research Director (Engineering and Environment), and Head of Research Cluster for Artificial Intelligence and Digital Technology. He was previously the Director of Research for Newcastle Research and Innovation Institute, and Director of Operations of Newcastle University. His major research is in the mathematical theory and algorithms for data science and analytics. This includes areas of artificial intelligence, machine learning, data mining, latent component analysis, multidimensional signal and image processing. He has an extensive portfolio of relevant research supported by a variety of funding agencies. He has published over 450 papers on these topics on various journals and international conference proceedings. He serves as Associate Editor to several international signal processing journals including IET Signal Processing, Journal of Acoustics, Journal of Electrical and Computer Engineering. He actively participate in international conferences and workshops, and serves on their organizing and technical committees. He is a Fellow of the Institution Engineering Technology (IET) and Institution of Electrical and Electronic Engineering (IEEE). Prof Woo has delivered more than 20 keynote addresses in Singapore, China, Malaysia, Indonesia, Ireland, Latvia,  Portugal, Spain and the United Kingdom. He provides expert evaluation to several funding bodies in the UK, the EU (Horizon2020) and North America.

Global Question

I am interested to answer the question of how artificial intelligence and machine learning advances humanity, fuels the economy and sustains the ecosystem in the current digital transformation era.

Research Approach

• Machine Learning for Sensor Signal, Image and Information Processing
• Mathematical Theory and Algorithm Development of Machine Learning 
• D-I-K-I (Data mining, Information Analytics, Knowledge discovery, Intelligence integration)

The core values of my research are discovery and impact. My vision is to develop new theories and demonstrable algorithms that behave with intelligence and to enable user system to yield optimum performance which is not possible without computational intelligence signal processing.

Current Work

The focus is on the mathematical foundation of machine learning and artificial intelligence computing algorithms. Many signal and image processing algorithms have incorporated some forms of computational intelligence as part of its core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learning to learn new information whenever unseen data is captured. Statistics is organised information but intelligence is more than that. We believe that it is just not enough to solve a problem by giving the most “frequent” answer. The goal is to develop new theories and demonstrable algorithms for deep learning machines that truly integrate signal processing and computational intelligence. These algorithms will have the ability to discover knowledge for themselves and learning to learn new information whenever unseen data is captured. While many researchers discount the need for human intervention in data analysis, our work reserves the freedom to enable collaborative syncretisation between human intuition and machine intelligence.

Research interest includes:

1. Machine Learning and Artificial Intelligence Computing

The focus is on the mathematical foundation of machine learning and artificial intelligence computing algorithms. Many signal processing and computer vision algorithms have incorporated some forms of computational intelligence as part of its core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learning to learn new information whenever unseen data is captured. Statistics is organised information but intelligence is more than that. We believe that it is just not enough to solve a problem by giving the most “frequent” answer. The goal is to develop new theories and demonstrable algorithms for deep learning machines that truly integrate signal processing and computational intelligence. These algorithms will have the ability to discover knowledge for themselves and learning to learn new information whenever unseen data is captured. While many researchers discount the need for human intervention in data analysis, our work reserves the freedom to enable collaborative syncretisation between human intuition and machine intelligence. This includes:

• Exemplar-aided Machine Learning
• Hierarchical Deep Wide Artificial Neural Networks such as Convolutional LSTM Network
• Genetically-enabled Globally Optimized Ensemble Deep Learning Neural Networks
• Lifelong Learning Knowledge-based System
• DIKI-enabled Human-centric Self-insight AI
• Generative Adversarial and Collaborative Deep Nets
• Deep Reinforcement Learning, Multi-Agent Machine Learning
• Deep Neural Decision Trees
• Ethical AI

 2. Data Science, Signal Processing and Information Analytics

Data mining is the process of discovering patterns in large datasets involving methods at the intersection of machine learning, statistics, and database systems such as GIS. It is an essential process where intelligent methods are applied to extract data patterns. Current research involves data pre-processing methods, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. This further includes:

• Latent Variable Analysis (LVA) which includes robust principal component analysis (PCA), independent component analysis (ICA), nonnegative matrix factorization (NMF), sparse coding (SC), compressive sensing (CS), low-rank sparse tensor decomposition, hybrid GMM-HMM models, time-varying switching models, nonlinear LVA models such as RBM, DBM and DBN
• Blind Signal Separation (BSS) where the mixing system is characterized as underdetermined, convolutive, noisy and possibly nonlinear. One of the earlier successes is the development of a mathematical framework based on Addition Theorem leading to a statistical multilayer neural network as the universal machine for solving the complex problem. Solutions to single-channel signal separation problems have also been developed by using hybrid LVA models with deep neural networks. Extraction of target signal buried in non-stationary narrowband background noise using exemplar-guided LVA models.
• Biometrics Pattern Recognition: It has been shown that biometrics data will vary depending on the how and when the data is captured and the environment it is subject to. By considering both pillars of nonlinearity and non-Gaussianity behaviours, research in biometrics has led to a new paradigm of thinking about pattern recognition problem. Multimodality biometrics include iris, sclera, face, palmprint, fingers texture, and physiological-based biometric such as heartwave captured at rest and during duress.
• Intelligent Computer Vision: Photometric stereo 3D depth estimation, image inference for object measurement in a photo, estimating depth of images based on single photo using deep neural network

3. Smart Sensing, Diagnostic and Data Monitoring

This theme focuses on creative and smart signal processing theories and methods to be applied for various engineering and IT applications including non-destructive testing (NDT), big-data analytics, wearable sensors, anomaly detection, and condition monitoring. The strategic trend in the development of smart sensing has changed towards the issue of safety in the broadest sense, to the protection of the population and the environment against man-made and natural disasters. There has been an increased realization of the potential benefits of applying advanced machine learning techniques to the signals captured from multimodal sensors. This includes:

• Intelligent Sensing & Computing for Non-Destructive Testing: Automatic defect detection, location, sizing & identification with ECPT using machine learning techniques with enhanced resolution (Robust PCA, ICA, NMF, Morphological Component Analysis, Low-Rank Sparse Decomposition, GA-enabled ICA-Images Fusion). Anomalies detection include surface defects on steel and plastic, and debonding defects in composite materials. Advanced statistical process control techniques (PCA with T² statistics, 2D contribution map, multiscale ICA-PCA) have been developed.

• Technology for Disaster Resilience: Building the next generation of disaster resilience for weather-related hazards such as severe floods and landslides. This includes development of novel Spatio-Temporal Deep Learning models for higher accuracy of prediction and recommend effective nature based solutions with Digitial Twin technology, hence improving the overall resilience for the urban and rural communities. The acquisition of accurate environmental is the result of novel Internet of Things (IoT) sensors combined with high-resolution remote sensing data and GIS. These solutions will add to the existing suite of solutions for forecasting, warning and asset management solutions improving accessibility to these across the UK.

• Predictive Maintenance and Condition Monitoring: Sound event-based detection for railway fault, measuring and quantifying the impact of hard disk drive (HDD) noise on human, separating airborne & structural noises in ship structure for better noise-propagation reduction, reliability index estimation for ship control and operations.

• Wearable Sensing: Wearable audio meter, continuous and non-invasive monitoring of anxiety and stress level in real-time situation, estimating autism quotient from empathizing quotient and systemizing quotient through game technology and artificial intelligence.

4. Emerging Topics in United Nation Sustainable Development Goals

• Urban Energy Smart Grid for Affordable Clean Energy and Sustainable Cities: Energy flow is now very much a two-way process. Low carbon technologies generated by solar panels and wind farms are feeding into national power networks bringing new challenges to be overcome. Research is on-going to find a way of managing that power in real time, so that the low-carbon transition can be achieved at reasonable cost and without degrading power system reliability. The goal is to match supply to demand in real time and within network constraints, and that means making the grid more intelligent. This intelligence allows demand response, the involvement of customers, and energy storage to be integrated into existing networks.

Smart Energy Forecasting: Prediction of solar energy harvesting in PV and heat energy generation in TeG accounting for environment factors using artificial intelligence methods
Smart Energy Monitoring: Non-intrusive load monitoring using computational intelligence techniques, temperature-dependent lithium-ion battery model and state of charge estimation using Kalman filtering algorithms, Internet-of-Things (IoT) for home energy detection and home energy theft
Energy Storage Control: Fuzzy logic control of energy storage in grid-connected microgrid with renewable energies

• Precision Farming for Zero Hunger: Research in precision agriculture concentrates on farming management concept based on observing, measuring and responding to inter and intra-field variability in crops. Crop variability typically has both a spatial and temporal component which makes statistical/computational treatments quite involved. The holy grail of our research has been the ability to define a Decision Support System for whole farm management with the goal of optimizing returns on inputs while preserving resources. Presently, we are developing big data analytic tools to estimate the nutrient status of the plants to improve the efficiency of the fertilizer use.

• Digital Health and Well-Being: Designing new information analytic algorithms to tackle societal challenges related to mental health issues. This includes developing lightweight AI models for detecting mental health conditions such as Schizophrenia, and well-being status like anxeity and depression through embeded AI in wearables. AI-based chatbots are developed to engage users with text-based conversations that deliver coping strategies based on the emotional needs of the users.