Skilled Machine Learning Imaging Scientist with a Ph.D. in Medical Science (Anatomy & Single-molecule Biophysics). Experience developing and optimising ML and data solutions. Extensive expertise in AI-driven medical imaging projects, end-to-end complex data pipelines, large-scale data analysis, and cross-functional collaboration. Proven track record of driving innovation in biomedical research.KEY ACHIEVEMENTSMachine Learning and AI Applications:• Developed and implemented machine learning models achieving 79% accuracy in identifying CT features of silicosis, supported by a custom Power BI dashboard for real-time data monitoring.• Directed validation process for AI-powered algorithms, enhancing accuracy of early detection and risk stratification for coronary artery disease.• Developed a custom dermatology Report Builder Tool.• Evaluating recalibrating AI models for breast cancer diagnosis in a new context: insights from transfer learning, image enhancement and high-quality training data integration (Cancers, 2024)• Hybrid HCNN-KNN model enhances age estimation accuracy in orthopantomography (Frontiers in Public Health, 2022)Image Processing Algorithm Development:• Developed an automated tool for cellular Feature Quantification to quantify immune cell morphology effects on motility and cancer-eliminating function (ICB 2020).• Created automation tool for fluorescence Signals Deconvolution: MATLAB algorithm to quantify fluorophores in diffraction-limited spots (biorxiv 2021).Optical Platform Development:• Built a single-molecule Imaging Platform for quantifying real-time visualisation of protein-protein interaction (published in Cytoskeleton 2016).• Constructed an optical platform combining Confocal Imaging & cell membrane Force Spectroscopy to visualise molecular mechanisms of surface proteins.Cancer Biology Research:• Characterized an anti-cancer drug for lung small carcinoma that reached clinical trial evaluation (NCT02688101, Med Chem 2012, 130 citations).