PhD researchers

I grew up in Hubei, China – a region with one of the highest rates of cancer incidence and mortality in the country. Witnessing the impact of cancer on my family and community motivated me to become a biomedical scientist focused on improving cancer care.

I’m now a second-year PhD student in Oncology at Trinity College, working to understand how cancer evolves over time and how we can track that evolution non-invasively. Just like species evolve through natural selection, the 30 trillion cells in our bodies accumulate mutations and undergo clonal selection as we age. Some of these mutations are harmless, but others occur in cancer-driver genes and can lead to disease.

My research focuses on lung and oesophageal cancers. I use non-invasive samples, such as blood
(circulating tumour DNA), and a novel device called the capsule sponge – to study how cancer evolves from the earliest stages. I’m developing novel computational methods to sensitively detect traces of tumour evolution in these samples. By doing so, I hope to uncover when and how harmful mutations arise and ultimately develop biomarkers to identify individuals at high risk of cancer before symptoms appear. My aim is to make early detection more accessible, giving people the chance to treat cancer while it’s still curable.

My research explores how artificial intelligence (AI) can help us better understand and manage musculoskeletal conditions, especially osteoarthritis (OA).

I use AI techniques, specifically machine learning,
to analyse large amounts of health data. This includes information from clinic appointments, medical images like X-rays or MRIs, and data
from wearable sensors that track movement. By processing this complex information, we aim to train algorithms to spot the earliest signs of OA, predict how the disease might worsen for an individual patient, and even identify different types of OA that might need different treatments.

The main idea is to move away from simply reacting to symptoms towards a more proactive approach. We want to identify those at high risk or in the very early stages of OA so we can intervene before serious joint damage happens, tailoring care to each person’s specific needs.

Ultimately, my goal is to create practical AI tools that clinicians can easily use in their daily practice. This could lead to earlier diagnoses, more effective treatments, improved patient quality of life, and potentially reduce the substantial impact OA and similar conditions have on individuals and healthcare systems.

This work, supervised by Professor Andrew McCaskie, would not have been possible without the generous funding from the LV Freedman Studentship in Medical Sciences and the ORUK/Versus Arthritis AI in MSK Research Fellowship.

I’m a theoretical physicist, so my goal is to write down mathematical rules describing the universe. We have a really good way of describing how small, energetic things like electrons work, called quantum theory. We also have a really good way of describing how big, low energy things stars work due to gravity, called general relativity. The problem is that when you try and make these theories meet at an energy somewhere in the middle, they each start spitting out infinities that we can’t really control.

But is there a time where big things, like stars, start to behave like energetic things, like electrons? Well, it turns out as a star gets heavier and its gravity
gets stronger, it pulls in more stuff, until it eventually collapses under its own weight to become this really dense object that sucks in even light. You’ve probably heard of these ‘black holes’. Stephen Hawking figured out that a lot of energy is thrown in (and out) of these black holes, and so we can use our rules of quantum theory to talk about them!

n my research, I’m looking at quantum effects
in black holes in two different ways. Firstly, if you appropriately tune properties of these black holes like mass and charge, the surrounding space and time can be deformed in various ways, which I’m trying to count (this is called their entropy). Secondly, all the possible ways of configuring the inside of the black hole can be encoded on a slice of distant space (this is called holography). Better understanding each of these quantities will take us closer to building a bigger theory that includes both quantum theory and gravity.

My academic interests lie at the intersection of photonics, nanotechnology, and Micro-Electro-Mechanical Systems/Nano-Electro-Mechanical-Systems, with a focus on advancing diagnostic technologies. My research is centred on the development of photoacoustic ultrasound sensors for high-resolution biomedical imaging.

The core innovation lies in combining photoacoustic imaging - a modality that leverages the optical absorption properties of tissue to generate ultrasound waves. By tailoring the mechanical, electrical, and acoustic properties of the Piezoelectric Micromachine Ultrasource Transducers (PMUTS) through geometric, material, and electrode-level optimisation, I aim
to overcome longstanding limitations in existing imaging systems.

My current work includes the design, fabrication and optimisation of multi-generation PMUT prototypes. The broader goal is to realise a fully integrated photoacoustic imaging sensing ensemble. Such a system has the potential to transform early-stage cancer diagnostics, vascular and neurological imaging, and point-of-care screening, especially in resource-limited settings where conventional imaging infrastructure is unavailable or impractical. It aspires to deliver both foundational insight and translational impact in next-generation medical diagnostics.