É«ÇéÍøÕ¾

Supervisors: 

Description: Monitoring power line corridors (PLCs) is vital for ensuring the reliability and safety of electrical transmission networks. This involves two critical components: the infrastructure itself—power lines and pylons—and the surrounding environment, particularly vegetation that can pose risks to system stability.

Leveraging point cloud data from airborne LiDAR technology, which captures precise 3D information of both power infrastructure and nearby objects, has revolutionised inspection processes in recent years. LiDAR’s unmatched spatial detail enables faster, more accurate assessments than traditional methods.

However, the sheer volume of LiDAR data, combined with scene noise, diverse environments and irrelevant objects close to power lines or pylons, creates significant challenges for accurate data extraction and analysis.

This study is set to transform PLC monitoring by developing automated, robust solutions that address these challenges head-on. The research focuses on three ambitious objectives:

• Precise extraction of power lines, pylons and surrounding vegetation;
• Accurate 3D reconstruction and modelling of power lines and pylons; and
• Dynamic vegetation monitoring to enhance risk assessment and maintenance strategies.

By pushing the boundaries of airborne LiDAR analytics, this project will deliver smarter, faster and more reliable tools to safeguard critical power infrastructure and ensure uninterrupted energy transmission.

Related publications

N. Munir, M. Awrangjeb and B. Stantic, An iterative graph-based method for constructing gaps in high-voltage bundle conductors using airborne LiDAR point cloud data, IEEE Transactions on Geoscience and Remote Sensing, 2024, https://doi.org/10.1109/TGRS.2023.3341970.

M. Awrangjeb, Extraction of power line pylons and wires using airborne LiDAR data at different height levels. Remote Sensing, 2019, https://doi.org/10.3390/rs11151798.

Supervisors: 

Description: Despite significant advances in automated building extraction from airborne laser scanning (ALS) data, key challenges persist in achieving reliable, large-scale building mapping. Issues such as data redundancy, computational bottlenecks, limited contextual understanding and interference from dense vegetation continue to hamper progress. Additionally, accurately extracting individual building instances from complex urban environments and managing incomplete or noisy data remain formidable obstacles.

While deep learning approaches have recently been applied to this task, their performance—peaking at an Intersection-over-Union (IoU) of 86%—leaves room for substantial improvement.

This research aims to break new ground by developing high-performance algorithms tailored for large-scale building mapping using ALS data. The ultimate goal is to enable precise 3D reconstruction and modelling of buildings, driving innovation in urban planning, smart city development and disaster management.

Related publications

M. Awrangjeb, M. Ravanbakhsh, C. S. Fraser, Automatic detection of residential buildings using LIDAR data and multispectral imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 2010, https://doi.org/10.1016/j.isprsjprs.2010.06.001.

E. K. Dey and M. Awrangjeb, A robust performance evaluation metric for extracted building boundaries from remote sensing data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, https://doi.org/10.1109/JSTARS.2020.3006258.

Supervisors: 

Description: Continual learning (CL) or lifelong learning is the ability of a model to learn continually from a stream of data. The idea of CL is to mimic human’s ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. With CL, we want to use the data that is coming to update the model autonomously based on the new activity. Data are typically discarded after use, and there is no opportunity to re-use the data for model retraining. Continual learning is a challenge for deep neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. Other challenges in CL includes adapting to emerging and disappearing concepts, adapting to concept drift, adapting to nonstationary noise, dealing with highly imbalance classes, etc. This project aims to develop novel (supervised and unsupervised) machine learning algorithms that overcome these challenges.

Related publications

T.T. Nguyen, M.T. Dang, V.A. Luong, A.W.C. Liew, T.C. Liang, J. McCall, “Multi-Label Classification via Incremental Clustering on Evolving Data Stream”, Pattern Recognition, Vol. 95: 96-113, 2019.

T.T. Nguyen, T.T.T. Nguyen, V.A. Luong, N.Q.V. Hung, A.W.C. Liew, B, Stantic, “Multi-label classification via labels correlation and first order feature dependence on data stream”, Pattern Recognition, Vol. 90: 35-51, 2019.

T.T.T. Nguyen, T.T. Nguyen, A.W.C. Liew, S.L. Wang, “Variational Inference based Bayes Online Classifiers with Concept Drift Adaptation”, Pattern Recognition, Vol. 81: 280-293, 2018.

Supervisors: 

Description: Digitising the Deep Past is a collaboration between the School of ICT, the Centre for Social and Cultural Heritage,  and the School of Education. This project brings together cutting-edge machine learning technology and traditional indigenous knowledge to develop AI tools for cultural heritage applications. Current areas of interest include Preservation of Cultural Language, Automated analysis of indigenous rock art, and cultural site condition monitoring.

Supervisors: 

Description: Fine-grained image classification is a challenge in computer vision, which aims at identifying the correct object in a dataset where there is both low between-class variance (different objects appear visually similar) and high intra-class variance (objects of the same class appear different). This work looks at implementing new models and techniques within convolutional neural networks to improve performance in these challenging datasets.

Related publications

Park YJ, Tuxworth G, Zhou J. Insect Classification Using Squeeze-and-Excitation and Attention Modules - a Benchmark Study. IEEE International Conference on Image Processing, 2019.

Supervisors:

Description: Hyperspectral videos contains rich spectral, spatial, and temporal information. Traditional methods treat these domains separately to undertake video analysis tasks, ignoring the intrinsic relationship embedded in the cross-modal data space. In this project, we propose to develop joint spectral-spatial-temporal processing methods to fully explore the abundant information embedded in hyperspectral videos. Fundamental theories and methods will be developed based on physics and statistical models and will be powered by the latest deep learning approaches. A number of applications in environment, agriculture, and medicine will be used to showcase the usefulness of the methods.

Related publications

Fengchao Xiong, Jun Zhou, and Yuntao Qian. Material based object tracking in hyperspectral videos, IEEE Transactions on Image Processing, Vol 29, No. 1, pages 3719-3733, 2020.

Suhad Lateef Al-khafaji, Jun Zhou, Ali Zia and Alan Wee-Chung Liew. Spectral-spatial scale invariant feature transform for hyperspectral images. IEEE Transactions on Image Processing, Vol. 27, No. 2, pages 837-850, 2018.

Supervisors:

Description: Artificial Intelligence (AI)—especially Machine Learning (ML) and Deep Learning (DL)—is rapidly transforming automated defect inspection across diverse industries, from metals and ceramics to collectible cards and textiles. By leveraging high-resolution imagery, these advanced techniques can detect and precisely localise defects that are often invisible to the naked eye.

Cutting-edge supervised deep learning models have recently been applied to assess corner and edge quality in collectible cards. Yet, their impact has been constrained by limited training data and the microscopic scale and subtle nature of defects.

This research is poised to push the boundaries by exploring innovative deep learning paradigms such as self-supervised and few-shot learning—powerful approaches that drastically reduce data requirements while delivering superior performance. These models hold the potential to revolutionise industrial defect inspection, making it more accurate, scalable and adaptable to real-world manufacturing challenges.

Related publications

L. Nahar, M. Awrangjeb and M. S. Islam, AI-enabled defect detection in industrial products: a comprehensive survey, key insights and future research challenges, Advanced Engineering Informatics (under revision), 2025.

L. Nahar, M. S. Islam, M. Awrangjeb and R. Verhoeve, Automated corner grading of trading cards: defect identification and confidence calibration through deep learning, Computers in Industry, 2025, https://doi.org/10.1016/j.compind.2024.104187.

Supervisors:

Description: Industrial Control systems use SCADA protocols to control the electricity grid or water treatment plants or other critical infrastructure.  Many of these systems are being connected to the Internet and are vulnerable to cyber attacks.  This project will employ machine learning and artificial intelligence to automatically detect attacks against these systems and automate the best response for defense.

Related publications

IEEE Transactions on Industrial Informatics, IEEE Transactions on Information Forensics and Security, Computers & Security

Supervisors:

Description: Next generation manufacturing systems use advanced robotic technologies and complex processes to function.  However many of these systems are connected to the Internet and are vulnerable to cyber attacks.  Stealthy cyber attacks are often difficult to detect.  This project will develop algorithms to monitor system processes for anomalies to automatically detect faults and cyber attacks.

Related publications

IEEE Transactions on Industrial Informatics, IEEE Transactions on Information Forensics and Security, Computers & Security, IEEE Access

Supervisors:

Description: Driver-less vehicles and Intelligent Transport Systems need to use wireless communications to function with safety.  However these communications may be vulnerable to cyber attacks that allow attackers to manipulate traffic and cause accidents. This project will explore new ways to ensure efficient authentication to detect and prevent attacks against vehicle communication systems.

Related publications

IEEE Transactions on Industrial Informatics, Vehicular Communications, IEEE Transactions on Vehicular Technology

Supervisors:

Description: Our daily digital life is protected by public-key cryptosystems like public-key encryption and digital signature systems. The security of most public-key cryptosystems have been deployed is ultimately based on the difficulties of solving number-theoretic problems (e.g., integer factoring problem and discrete logarithm problem) using classic computers. It turns out these number-theoretic problems can be efficiently solved by large-scale quantum computers which have been theorised about for decades. There has been substantial progress towards making quantum computing practical. To protect our communication in the long-term, we need a new generation of cryptosystems to defeat quantum computers. Cryptography based on decoding problems (e.g., decoding random linear codes) is a very promising candidate. In this project, you will explore the field of post-quantum cryptography and conduct research on one the two directions: 1) designing advanced post-quantum cryptosystems e.g., attributed-based encryption, functional encryption, fully homomorphic encryption, ring/group signatures and apply them to the real-world problems, e.g., fine-grained access control on encrypted data for cloud computing, efficient search and query on the encrypted database, smart contract and cryptocurrency 2) designing and implementing (in software or hardware) practical public-key encryption and digital signature systems with strong practical security (i.e., secure against various side-channel attacks) and high practicality (i.e., can be used for the Internet security protocols or computing-resource-restricted devices like IoT devices).

Related publications

Xavier Boyen, Malika Izabachene, Qinyi Li (Corresponding Author): An Efficient Lattice CCA-Secure KEM in the Standard Model. The 12th International Conference on Security and Cryptography for Networks (SCN 2020). Accepted on 14 June, 2020.

Xavier Boyen, Qinyi Li (Corresponding Author): Direct CCA-Secure KEM and Deterministic PKE from Plain LWE. The 10th International Conference on Post-Quantum Cryptography (PQCrypto 2019). LNCS 11505, pp.116-130. Springer 2019.

Xavier Boyen, Qinyi Li (Corresponding Author): All-but-Many Lossy Trapdoor Functions from Lattices and Applications. The 37th International Cryptology Conference (Crypto 2017). LNCS 10403, pp. 298-331, Springer 2017.

Xavier Boyen, Qinyi Li (Corresponding Author): Towards Tightly Secure Lattice Short Signature and Id-Based Encryption. The 22nd International Conference on Theory and Applications of Cryptography and Information Security (AsiaCrypt 2016). LNCS 10032, pp. 404-434. Springer 2016.

Supervisors:

Description: There is great potential in applying machine learning techniques to design self-organising, self-aware, intelligent wireless networks. Machine learning enables network nodes to actively learn the state of the wireless environment, detect correlations in the data, and take actions to optimise network operations and make efficient use of the limited wireless spectrum resources.

The first project will develop methods to parse the massive amount of wireless network statistics/data (e.g. channel state information, signal strength, interference, noise, traffic load/patterns, etc.) in order to analyse and predict the context of the wireless environment. Using these data, we will develop machine learning-guided techniques to address a variety of challenges in wireless networks such as power control, user traffic scheduling, spectrum management, rate selection, etc.

A major challenge of machine learning is its vulnerability to adversarial attacks. Adversarial machine learning attacks in wireless networks can cause network nodes to make incorrect decisions or interfere with data transmissions. For example, network nodes can train a classifier on various wireless statistics and use it to predict future channel availability status and adapt their transmission decisions to the spectrum dynamics. An adversary can train its classifier to be functionally equivalent to the one at the transmitter, and launch attacks (e.g. sends jamming signals) when it predicts that the transmitter will transmit data to the receiver. These attacks can significantly affect network performance, e.g. reduced spectral efficiency and increased node energy consumption.

Therefore, a second project is to investigate the impact of different machine learning vulnerabilities in wireless networks and develop techniques to detect and mitigate these attacks in highly dynamic wireless networks.

Supervisors:

Description: The core of our proposal involves harnessing the advanced natural language understanding, pattern recognition, and generative capabilities of LLMs to augment the power of traditional automated reasoning systems like PAT, Event B, Lean and Isabelle/HOL. By creating these AI-enhanced rigorous reasoning agents, this project will not only advance the field of automated reasoning but also showcase novel applications of LLMs in areas demanding rigorous logical integrity. This contributes directly to the broader understanding of how AI can be applied to solve intricate problems in science, engineering, and mathematics, pushing the boundaries of current AI capabilities. The development of such AI applications is crucial for building more trustworthy and capable intelligent systems for the future.

Supervisors:

Description: Machine learning (ML) allows computer systems to train themselves to improve their performance. It is pervasive and plays a key role in a wide range of applications. At a high level, ML consists of two phases. In the first phase, it applies a learning algorithm to a set of training data drawn from some unknown distribution to generate a model (hypothesis). In the second phase, the model can be used to explain new data (e.g., classify new data from the unknown distribution, or generate new data from a distribution that is close to the unknown distribution). In many applications of ML, sensitive data is needed and therefore data privacy becomes a concern. For example, when comes to Machine Learning As a Service, remote entities (usually untrusted) provide access to machine learning algorithms using the Internet to user’s data and return the results. User’s data might be completely exposed to the remote entities if security/privacy mechanisms are not imposed. Also, even with the best privacy on the training data, output (in cleartext form) of the second phase of ML may reveal information on training data. Therefore, with ML is being applied ubiquitously, a set of techniques that protect data privacy in ML is desirable and important. In this project, you will closely analyse the data privacy issues in the context of ML and explore advanced cryptographic and privacy techniques (e.g., fully homomorphic encryption, secure multi-party computation and differential privacy) to provide innovative and practical solutions.

Supervisors:

Description: This project aims to develop novel stream learning algorithms for continuous patient outcome monitoring and prognosis by taking into account patient's data collected during hospital admission. The algorithms are expected to integrate high frequency time series data with patient's demographic data, lab test data, diagnosis data, prescription data, etc. as exemplified in MIMIC-III, for accurate patient outcome monitoring and prognosis. This will in turn used to inform hospital resource planning and allocation using for example, our highly efficient binary QP solver [1]. Practical issues such as data sparsity, noisy and missing data, data non-stationarity, data leakage, prediction bias, model explainability, etc. will be investigated.

Related publications

B.S.Y. Lam, A.W.C. Liew, “A Fast Binary Quadratic Programming Solver based on Stochastic Neighborhood Search”, IEEE Trans on Pattern Analysis and Machine Intelligence, 2020. DOI: 10.1109/TPAMI.2020.3010811

Supervisors:

Description: Along with the advances of computing and network technologies, applying AI and machine learning techniques to analyse various types of big data from heterogeneous sources has become a major form of data processing and analysis. However, privacy leakage in accessing, processing and analysing shared (published) data is a major concern that obstacles the development of big data analytics. There have been numerous example of shocking damages and losses - both political and financial - caused by privacy breaches in different scales.

In order to safeguard data sharing for the purpose of big data analytics required by our industry and business, in the project we will investigate effective models, methods and techniques for privacy protection in data publishing, processing and analysis. For data publishing, we will study both cryptographic and non-cryptographic techniques including block cypher, randomization and anonymization to achieve effective protection of different type of data. For data processing, we will study effective privacy-preserving computing techniques including secure multi-party computation (SMC) and differential privacy. We will apply them in a cloud environment on virtualized network and computing resources. For data analysis, we will embed privacy-preserving techniques into machine learning models to achieve secure machine learning on big data.

Project outcomes will benefit both researchers and practitioners in big data analytics, machine learning, cloud computing and social network analysis, and potentially result significant economic gain for É«ÇéÍøÕ¾'s network-centric industry and business.

Related publications

Hui Tian, Wenwen Sheng, Hong Shen, Can Wang. Truth Finding by Reliability Estimation on Inconsistent Entities for Heterogeneous Data Sets. Knowledge-Based Systems, Jul. 2019. (CORE B, IF 5.921)

Hui Tian, Jingtian Liu and Hong Shen. Diffusion Wavelet-based Privacy Preserving in Social Networks. Computers & Electrical Engineering, Feb. 2018. (CORE B, IF 2.663)

Ruoxuan Wei, Hui Tian and Hong Shen. Improving k-Anonymity Based Privacy Preservation for Collaborative Filtering. Computers & Electrical Engineering, Mar. 2018. (CORE B, IF 2.663)

Supervisors:

Description: This project aims at building a series of efficient recommender systems with high accuracy from social networks, such as Twitter, Facebook, Instagram, Netflix, and so on. The research questions may include how to quantify the coupling relationships in recommender systems from different levels, how to enhance the interpretability of recommender systems, how to involve the trend information and how to model trust in various recommendation problems, how to speed up the recommendation process but with acceptable accuracy, and etc.

Related publications

Can Wang, Chi-Hung Chi, Zhong She, Longbing Cao, Bela Stantic. Coupled Clustering Ensemble by Exploring Data Interdependence. ACM Transactions on Knowledge Discovery from Data, Vol. 12, No. 6, Article 63, pages 1-38, 2018. [Impact Factor 2.538, Q1]

Can Wang, Xiangjun Dong, Fei Zhou, Longbing Cao, Chi-Hung Chi. Coupled Attribute Similarity Learning on Categorical Data. IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 4, pages 781-797, 2015. [Impact Factor: 11.683, Q1]

, , , , . Spectrum-Guided Adversarial Disparity Learning. The 2020 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Accepted by KDD 2020). [CORE Ranking: A*]

Ye Tao, Can Wang, Lina Yao, Weimin Li, Yonghong Yu. TRec: Sequential Recommender Based On Latent Item Trend Information. International Joint Conference on Neural Networks (IJCNN 2020), pp. 1-8, 2020. [CORE Ranking: A]

Yunwei Zhao, Can Wang, Chi-Hung Chi, Kwok-Yan Lam, Sen Wang. A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter, The 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), pages 1212-1218, 2018 [CORE Ranking: A*]

Supervisors:

Description: Graphs are increasingly being used to model complex data, and collections of graphs are getting very large, which brings big challenges to query processing. On one hand, many queries in graph databases are expensive by nature, and computing their exact answers can be infeasible when the graph size is large. On the other, in many applications an error-bounded estimate will suffice.  These motivates the work on approximate query answering in large graphs.

This project will investigate approximate query answering in large dynamic graphs where nodes and edges can be frequently updated. We will focus on property graphs where the nodes (and/or edges) are associated with key-value pairs, and queries that may involve simple aggregation (e.g., counting the number of occurrences of substructures), and develop novel techniques to efficiently find high-quality approximate answers.

The approaches will generally involve offline pre-processing (e.g., summarization, smart indexing), algorithm design, and experimental evaluation.  Due to the dynamic nature of the graphs, any auxiliary data structures need to be efficiently maintainable, and ideally incremental computation of query answers will be explored.  We are particularly interested in summarization-based techniques and applying machine-learning in auxiliary structure construction.

Related publications

Xuguang Ren and Junhu Wang: Exploiting Vertex Relationships in Speeding up Subgraph Isomorphism over Large Graphs. VLDB 2015.

Xuguang Ren and Junhu Wang: Multi-Query Optimization for Subgraph Isomorphism Search. VLDB 2017.

Supervisors:

Description: Knowledge graphs are tremendously popular nowadays because its ability to model diverse information. A knowledge graph can be regarded as a repository of facts about objects and their relationships, represented as labelled edges of a directed graph.  Over the last few years there have been growing interest in industry and academia to develop natural language question-answering (NLQL) systems over large knowledge graphs.  Such systems typically consists of two parts: question understanding and answer searching. Question understanding is to figure out the precise intention of the question, and answer searching is to actually find the answers based on the search intention.  Both tasks are challenging because of the ambiguity of natural language sentences and the fact that the same question an be raised in multiple ways in natural languages, and large size of knowledge graphs.

Existing approaches, whether based on question templates, machine-learning and graph embedding, or subgraph matching, suffer from limited capability in terms of the question types they can handle (i.e., they are limited to simple questions), accuracy, and efficiency. This PhD project will investigate NLQA over large knowledge graphs, with the aim of developing novel techniques to address the above limitations.

Related publications

Xiangnan Ren, Neha Sengupta, Xuguang Ren, Junhu Wang, Olivier Cur. Finding Structurally Compact Subgraphs with Ontology Exploration in Large RDF data (under review by PVLDB).