Rate My Professor Faisal Beg

Faisal Beg is a Professor and Graduate Program Chair in Engineering. He earned his Ph.D. in Biomedical Engineering from The Johns Hopkins University, an M.S. in Biomedical Engineering from Boston University, and a B.Tech. (Hons.) in Instrumentation Engineering from the Indian Institute of Technology, Kharagpur. Registered as a Professional Engineer (P.Eng.), Beg has established himself as a leader in biomedical engineering research, focusing on computational methods for medical imaging analysis.

His academic interests center on deep learning and machine learning applications in health, including MRI and PET-based biomarkers for neurodegenerative diseases such as Alzheimer’s, CT-based biomarkers of body composition for cancer assessment, and OCT-based biomarkers for retinal diseases. Additional specializations encompass computational anatomy, non-rigid registration of medical images, shape analysis, statistical atlases of anatomical shapes, and parallel computing in medical image processing. As Co-Director of the Medical Image Analysis Laboratory, he advances algorithms for image segmentation, non-rigid registration, and statistical shape analysis applied to brain, eye, and cancer imaging. Beg holds the distinction of Michael Smith Foundation for Health Research Scholar and has been honored with the Meritorious Achievement Award from APEGBC at the 2012 President's Award Ceremony, as well as recognition for excellence in teaching from the Faculty of Applied Science. He has obtained major funding through CIHR Project Grants, including awards of $956,250 and $100,000 in 2023.

Beg's influence in the field is demonstrated by his prolific publications. Key works include "Computing large deformation metric mappings via geodesic flows of diffeomorphisms" (2005, over 2,300 citations), a foundational paper on diffeomorphic mappings; "Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images" (2018, 502 citations); "Measuring and mapping cardiac fiber and laminar architecture using diffusion tensor MR imaging" (2005, 369 citations); "Effects of aging on motor-unit control properties" (1999, 369 citations); and "Neonatal pain-related stress predicts cortical thickness at age 7 years in children born very preterm" (2013, 366 citations). These contributions have profoundly shaped medical image computing, enhancing diagnostic capabilities for neurological and other diseases.