Research projects in Information Technology

This PhD project will develop intelligent, data-driven methods that enable electric vehicles (EVs) to serve as mobile energy storage while remaining readily available for transportation. The research will investigate how EV travel, routing, charging and discharging decisions can be jointly optimised using real-time and predicted information about travel demand, traffic conditions, renewable energy availability, electricity prices, charging infrastructure and battery status.

This PhD project will develop intelligent, data-driven methods for integrating diverse autonomous vehicles, such as buses, shuttles, pods, cars and micromobility vehicles, into public transport networks. The research will investigate how heterogeneous fleets can be dynamically routed, scheduled and repositioned in response to changing passenger demand, traffic conditions and public transport schedules, while accounting for differences in vehicle capacity, speed, energy use and service roles.

This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often fail to consider workload heterogeneity, resulting in suboptimal performance and increased energy costs. This proposal addresses these gaps by introducing dynamic task assignment and workload-aware scheduling algorithms that dynamically adapt to system demands.

Please note that this PhD topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Core PhD Question

How can we prevent AI systems, advertisers, platforms, and malicious actors from constructing psychological profiles of social media users while still allowing meaningful online interaction, personalization, and content discovery?

Please note that this PhD topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Core PhD Question

How can we design autonomous AI security systems that can detect, reason about, respond to, and recover from cyberattacks with minimal human intervention, while remaining safe, explainable, and resistant to manipulation?

Please note that this PhD topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Core PhD Question

How can we design future medical AI systems that remain secure, privacy-preserving, explainable, and clinically reliable when exposed to adversarial attacks, prompt injection, poisoned data, privacy leakage, and unsafe autonomous AI-agent behaviour?

Please note that this PhD topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Core PhD Question

Can Transformer models understand the full lifecycle of a vulnerability; from vulnerable code, to patch, to advisory, to regression risk; and determine whether a security fix is complete, safe, and trustworthy?

So we are not planning to do the following:

In EdgeVLMOpt (EVO): Optimizing Vision-Language Models for Resource-Constrained Edge Devices, we aim to develop efficient and scalable techniques to enable the deployment of advanced vision-language models (VLMs) on edge hardware. While VLMs have demonstrated strong capabilities in multimodal reasoning and understanding, their high computational and memory demands pose significant challenges for real-time, on-device applications.

In EdgeFusionAI (EFAI): Real-Time Multi-Sensor Multi-Modal Intelligence on Edge Devices, we aim to design and develop efficient techniques for fusing heterogeneous sensory data, including vision, LiDAR, radar, and other modalities, to enable robust and real-time decision-making on resource-constrained edge platforms. This project focuses on building intelligent systems capable of integrating diverse data sources while addressing the challenges of limited computation, memory, and energy availability at the edge.

This PhD project focuses on the design and evaluation of hybrid quantum–classical algorithms for large-scale data analytics and optimisation problems.

The research will investigate how quantum computational techniques can be combined with classical systems to improve performance, scalability, and solution quality for tasks such as: