Mohamed Hefny is a PhD candidate at Queen’s University School of Computing; working under supervision of Professor Randy Ellis. He received a BSc in Computer Science with double minor in Electronics and Mathematics (1998) and a MSc in Computer Science with a focus in Artificial Intelligence (2001), both from The American University in Cairo. He is also a certified Project Management Professional (PMP) by the Project Management Institute (PMI) and a licensed Professional Engineer (PEng) by Professional Engineers Ontario (PEO).

Mr. Hefny earned several prestigious awards and scholarships including HMRC-CREATE Program in Bone and Joint Health Technologies Scholarship from the Natural Sciences and Engineering Research Council (2012–2013), the SPIE Scholarship in Optics and Photonics from the International Society for Optics and Photonics (2011–2012), the Ontario Graduate Scholarship from the government of Ontario (2011-2012), the SPIE Scholarship in Optical Science and Engineering from the International Society for Optics and Photonics (2010-2011), the Alexander Graham Bell Canada Graduate Scholarship from the Natural Sciences and Engineering Research Council (2009-2011), the Ontario Graduate Scholarship in Science and Technology from the government of Ontario and Queen’s University (2008-2009), the Excellence in Teaching Assistantship Award from Queen’s University School of Computing (2008-2009), and the Graduate Merit Fellowship Award from The American University in Cairo (1999-2000).

Mr. Hefny has over 10 years experience in technical consulting and project management in healthcare and education technology projects, and over 5 years experience in interdisciplinary academic teaching and research. He is interested in applying computer science to enhance health care services by collaborating with medicine, engineering, and health sciences researchers. His doctoral thesis examines the use of differential geometry for analysing complex shapes in computational anatomy. He is developing fundamental algorithms and core technologies to assist surgeons in the operating room by increasing the efficiency and the quality of surgeries.