Student Spotlights

I will be joining the SafeNetIOT research team in April 2026, where my PhD research will revolve around "Internet of Things" devices, among other possible topics. As someone who enjoys self-hosting and home automation, the interests of this team align perfectly with mine. UCL is a great place to be in, and the first few months of the CDT have equipped me with valuable skills that I will use throughout my research journey. It is a privilege to be among the first batch of students in a new CDT programme, and I'm greatly looking forward to what's next!

I graduated in 2013 with a BEng in Electrical and Electronic Engineering from Queen Mary University of London (1st class honours). Owing to my high marks I was one of a handful of students offered a PhD studentship at the time, but circumstances (and destiny) led me towards a career in software development. I started with an international consultancy in Singapore, eventually returning to the UK to work in similar roles across different organisations. While at work I completed a part-time MSc in Software and Systems Security at Oxford University, graduating with Distinction. In a way, the research that I carried out for my MSc dissertation re-ignited my lifelong aspiration of being at the cutting edge of technology. Rather than remaining a passive observer/consumer, I desire to be an active participant in pushing technological boundaries in ways that improve lives. As a result, I eventually made the decision to leave my job and kickstart this journey at UCL.

Apart from doing geeky stuff with computers, I hold a full motorcycle license. My longest trip was a solo journey from Liverpool to Madrid, where I got caught in a violent storm that left me stranded on the motorway. I have also recently taken interest in woodworking and hope to make use of the facilities at UCL's Institute of Making while I'm here.

Critical infrastructure is increasingly digitised and interconnected, and at the same time adversarial behaviour and intent is rapidly evolving. This presents risks to national security, society and the environment. My PhD research focus is on using transfer learning to enhance cyber-physical system security, for example improving the performance of relevant machine learning applications under conditions of distribution shift.

I have previous exposure to applied machine learning, risk and governance academically and through eight years of industry experience. In my undergraduate, I studied International Relations and Philosophy at the University of St Andrews. I then undertook an MA in Global Risk at the Johns Hopkins School of Advanced International Studies (SAIS), and an MSc in Artificial Intelligence for Sustainable Development at UCL. The CDT was a great fit as it emphasises the importance of multi-disciplinary approaches for impact. Given my previous studies at UCL, I was also keen to rejoin its excellent scientific community. 

In my free time I enjoy exploring the food scene in Bloomsbury and do pottery in a local studio.

My research project investigates the relationship between online extremism and violence in the offline world to identify appropriate countermeasures. My background is in the humanities and social sciences and before joining the CDT, I worked in the online harms sector as a researcher for several years before retraining as a data scientist. The CDT at UCL seemed the perfect next step to further develop both my technical skills and theoretical knowledge at two very unique and world-class departments.

I grew up in Norfolk and moved to London about five years ago. In my spare time, I run a book club, enjoy scrapbooking and have started bouldering! 

My research will look into applying generalisation techniques for deep learning based radio frequency (RF) fingerprinting of devices within critical national infrastructure. Modern cyber security practices rely on mac addresses to verify devices connected to networks which is often spoofed by adversaries leading to potential malware deployment or ransomware attacks. RF fingerprints can provide unique identification of device models and potentially even unique device ID's with enough feature representations, leading to correct identification of malicious devices in a network.

The interdisciplinary nature of the CDT really engaged me as I come from a mathematics, physics and computer science background during my undergrad studies of natural sciences at UEA. In my free time I enjoy building audio synthesisers from voltage-controlled oscillators, filters and modulators alongside the use of signal processing techniques for sound engineering and electronic music production.