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Dr. Rafeef Garbi
Electrical and Computer Engineering
Faculty of Applied Science
Rafeef Garbi (née Abugharbieh) received her PhD, Technical Licentiate, and MSc (with distinction) from the School of Electrical and Computer Engineering, Chalmers University in Göteborg, Sweden. She is a registered member of the Association of Professional Engineers and Geoscientists of British Columbia (PEng), a UBC Killam Faculty Research Fellow, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an associate founding member of the IEEE Engineering in Medicine and Biology Society (EMBS) Vancouver Section.
Dr. Garbi's research interest focuses on developing artificial intelligence (AI) and computer vision based technologies for biomedical imaging applications. Her team creates innovative methods for computational processing, analysis, quantification, understanding, and visualization of multi-dimensional structural and functional medical imaging data. Their work extends to helping translate their developed technologies such that they can be applied in a clinically-focused, disease-specific manner.
Innovations in AI-based biomedical image computing methods, which are the core focus of Dr. Garbi’s research, are essential for enabling, advancing, accelerating and improving the quality (e.g., accuracy, precision and reproducibility) and efficiency (e.g. speed, cost) of numerous areas in the health sciences field and healthcare sector. Dr. Garbi’s research is relevant to heterogeneous data from different scales and modalities. Furthermore, her focus on embedding her projects within real life clinical applications (e.g. screening, diagnostics, prognostics, surgical interventions, therapy assessment/follow-up, etc.) ensures the developed solutions are practical and have the potential of being translated to stake holders in other disciplines in the short to medium term. Examples of such past work include contributions to biomarker-based study of neurological disease and recovery mechanisms (Parkinson’s), augmented reality robot assisted systems for image-guided interventions (partial nephrectomy), deep learning based techniques for renal cancer analysis from CT (kidney localization, tumor detection, predication of underlying genetic mutation), and machine learning based system for 3D ultrasonic screening of the pediatric hip (developmental dysplasia).
Artificial Intelligence, Medical Image Computing, Computer Vision, Image Analysis, Machine Learning for Medical Imaging, Biomedical Applications