This research develops nanobubble-enhanced ultrasound imaging as an accessible alternative to MRI for cancer diagnosis. Tiny gas-filled nanoparticles amplify ultrasound signals and improve image quality, particularly in prostate cancer. The technology could reduce diagnostic delays, lower costs, and provide high-quality medical imaging to more patients worldwide.

This research examined how COVID-19 viral loads change over time across saliva, throat, and nasal samples. The study found that different sample types detect infection at different stages, demonstrating that testing method matters. These findings could improve diagnostic strategies for COVID-19, influenza, RSV, and future emerging respiratory viruses.

This research develops engineered ultrasonic reporters that allow ultrasound imaging to detect molecular activity rather than only anatomical structure. By targeting biological signals associated with cancer progression and cellular communication, the work aims to distinguish aggressive disease earlier and improve precision medicine through real-time, noninvasive monitoring of underlying cellular behavior.

This research uses artificial intelligence to predict the progression of Alzheimer’s disease and cancer using medical imaging data. By analyzing brain scans, tumor scans, and treatment responses, AI models can forecast disease development and treatment outcomes, enabling earlier intervention, more personalized care, and improved quality of life for aging populations.

This research uses wearable data and AI to detect disease earlier by analyzing continuous health signals rather than isolated clinical snapshots. By personalizing models to individual baselines, the system identifies subtle changes linked to conditions like infections, heart issues, and mental health crises, enabling earlier intervention and potentially saving lives.

This research develops a nanoparticle-based diagnostic test for thrombotic thrombocytopenic purpura (TTP), a rare and deadly blood disorder. By enabling fast, affordable detection of the ADAMTS13 enzyme, the system could allow earlier diagnosis, timely treatment, and improved survival while inspiring new approaches to rare disease diagnostics.

Hip dysplasia is often diagnosed too late or too inconsistently, leading to lifelong pain. The speaker’s research builds the first open-access AI tool for detecting and studying the condition, enabling portable automated diagnosis and global collaboration. By sharing tools instead of guessing, researchers can reduce unnecessary surgeries and improve outcomes worldwide.

Prion diseases like CJD are extremely hard to detect early because harmful prions resemble normal brain proteins. This research introduces a new “flashbody” detection tool that binds only disease-causing prions, providing rapid, accurate, equipment-free diagnosis. Early lab results and patient-screening trials are promising, with potential applications to Alzheimer’s and other dementias.

Bowel cancer kills thousands each year, and current stool-based screening misses many cases. This PhD develops a new non-invasive method that analyzes human cells shed into stool, aiming to detect normal, pre-cancerous, and cancerous changes more accurately. The goal is a more reliable, higher-participation screening tool that could replace the existing national test.

My research uses AI and wearable technology to track brain and body signals such as brain waves (EEG), heart rate, and movement. The goal?  Spotting early signs of Alzheimer's and Parkinson's before symptoms show up. Catching these subtle changes could mean helping people sooner, letting them enjoy the everyday moments that matter most