Research projects in Information Technology

Security Operations Centres (SOCs) play a central role in organisational defence and are responsible for continuous monitoring, detecting, investigating and responding to cyber attacks. Organisations increasingly depend on security tools to flag suspicious activity. These tools generate alerts that analysts must examine to determine whether they represent real attacks or false positives. However, the volume of alerts continues to grow at a pace that far exceeds what human analysts can realistically review.

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This project will investigate the security and privacy challenges emerging from the adoption of digital education credentials, such as W3C Verifiable Credentials. As universities and employers increasingly rely on digital systems to issue, store, and verify qualifications, new risks arise—ranging from data breaches and identity fraud to profiling and surveillance through credential verification logs.

In this project, we will develop automated approach to detect the defects in AI systems, including LLMs, auto-driving systems, etc.

The objective of this project is to design automated approach to detect bugs in various software, e.g., compilers, data libraries and so on.

The project may involve LLMs.

Development of a GIS-Based Model for Active Citizenry

Street-Level Environment Recognition On Moving Resource-Constrained Devices

In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed infrastructures, improving system performance, scalability, and efficiency by optimizing resource usage (e.g., GPUs, CPUs, energy consumption).

Public transportation is vital for sustainable urban mobility, yet challenges like inefficient first- and last-mile connectivity, and over-reliance on private cars hinder its effectiveness. Autonomous vehicles (AVs) offer transformative potential by enabling diverse, on-demand mobility solutions tailored to specific trip needs, enhancing connectivity, and reducing emissions. However, current research often overlooks the complexities of mixed-vehicle environments, and the development of optimal deployment, routing, and charging strategies.

In SmartScaleSys (S3), we aim to design and build resource management solutions to learn from usage patterns, predict future needs, and allocate resources to minimize latency, energy consumption, and costs of running diverse applications in large-scale distributed systems. This project offers researchers and students a chance to explore cutting-edge concepts in AI-driven infrastructure management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research.