Dr. Steven Ding
Dr. Ding received his PhD from McGill University in 2019, and he was awarded the FRQNT Doctoral Research Scholarship of Quebec and the Dean’s Graduate Award at McGill University. He leads the L1NNA Artificial Intelligence and Security Lab which is supported in part by Defence Research and Development Canada (DRDC).
As a faculty member in the School of Computing, Dr. Steven Ding’s expertise will bridge the domain of machine learning, data mining, and cybersecurity to promote our Canadian defense excellence and secure the future of AI systems.
Data Mining, Machine Learning, Security.
Dr. Amber Simpson
Dr. Simpson received her PhD in Computer Science from Queen’s University in 2010. After graduating, she worked at the Vanderbilt University in Nashville as a Research Assistant Professor in Biomedical Engineering. Following this, Simpson transferred to the Memorial Sloan Kettering Cancer Center in New York. She is an American Association of Cancer Research award winner and the holder of multiple National Institutes of Health grants.
As a faculty member in the School of Computing and Department of Biomedical and Molecular Sciences, Dr. Amber Simpson’s expertise will contribute to the Queen’s cancer clinical trials. By examining the biomedical data and using machine learning, Simpson will continue to develop new biomarkers that improve patient care.
Machine learning, Medical image analysis, Computer aided surgery.
Dr. Sameh Sorour
Dr. Sourour received his PhD from the University of Toronto in 2011. After graduating, he held a MITACS industrial postdoctoral fellowship with Siradel Canada and the University of Toronto. Following this, he held a postdoctoral fellowship at King Abdullah University of Science and Technology (KAUST), and two assistant professor positions at King Fahd University of Petroleum and Minerals (KFUPM) and University of Idaho.
As a faculty member in the School of Computing, Dr. Sameh Sorour’s expertise will contribute to government agencies and industrial partners to help build intelligent and automated cyber-physical systems for smart cities, and develop new technologies to perform collaborative computing and data analytics on mobile edge devices such as smartphones, laptops, drones, and vehicles.
Mobile Edge Learning, IoT, Edge Computing and Networking, Cyber-Physical and Autonomous Systems, Intelligent Vehicles and Transportation Systems, Network Coding.