I believe in the power of nature and exercise. While I'm passionate about my work, swimming in the refreshing waters of Newcastle Beach and the university pool, along with daily walks through the tranquil Blackbutt Nature Reserve, help me stay centered and energised.

My journey into multiple sclerosis (MS) research began during my PhD at the University of Newcastle, where I was captivated by the complexities of neurological diseases and the potential of advanced magnetic resonance imaging (MRI) techniques to reveal new insights. I saw firsthand the gaps in existing treatments and the profound impact MS has on patients’ lives. This motivated me to focus on developing predictive models that could better track disease progression and support clinicians in making informed decisions. The opportunity to contribute to improving care for people with MS, combining cutting-edge research with real-world impact, continues to inspire my work every day.

One of the most exciting developments in MS research in recent years has been the increasing focus on understanding the underlying mechanisms of the disease, especially driving the ongoing progression. The development of new disease-modifying therapies (DMTs) has significantly reduced the number of relapses for people with MS. Research into genetics, epigenetics and novel MRI techniques has allowed us to dive deeper into the ongoing processes in the damaged brain. Together, these advancements are paving the way for personalised treatments and offering renewed hope for improved quality of life for people with MS.

The current research project is focused on developing and validating a predictive model for cognitive decline as a sign of disease progression in people with MS using a combination of novel machine learning approaches and multimodal MRI data. This model aims to inform treatment decisions and optimise care by accurately tracking the progression of MS. The research leverages an existing longitudinal cohort with comprehensive MRI and clinical data, utilising advanced techniques like diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS) to enhance predictive accuracy. The ultimate goal is to improve the understanding of MS progression and to aid in the development of targeted therapies and management strategies for those affected by MS. This project offers a unique opportunity to follow connectivity and neurometabolic changes over a long period of time.

My research is pivotal in improving the understanding and management of MS. Cognitive decline significantly impacts quality of life for people with MS and serves as an indicator of disease progression. By leveraging advanced MRI techniques and machine learning, my project aims to create a predictive model for MS progression, focusing on cognitive decline—a critical aspect of disease burden. Utilising a unique 10-year longitudinal dataset, the study combines multi-modal MRI and clinical data to identify neuroimaging and clinical biomarkers that reliably predict disease trajectory. This approach not only enhances personalised care for people with MS but also lays the groundwork for evaluating novel therapies, ultimately helping mitigate the societal and personal impact of MS.

Working in our research institution allows me to combine cutting-edge technology with creative problem-solving. This is incredibly fulfilling. I enjoy utilising advanced MRI techniques and machine learning to uncover patterns in MS progression, treating each research question like a complex puzzle. Collaborating in a research team with clinical and analytical expertise well known on the national and international level further enriches the experience, fostering innovation through shared knowledge. However, the work comes with challenges, including the time-intensive nature of data processing, the complexity of multi-modal analyses, and the competitive environment of securing funding and publishing. Despite these hurdles, the impact of our discoveries on improving lives makes the effort worthwhile.