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Professor
James Bremer is a Professor in the Department of Mathematics and holds a cross-appointment in the Department of Computer and Mathematical Sciences at the University of Toronto's Scarborough campus. His research focuses on developing efficient numerical algorithms for solving elliptic boundary value problems, integral equations, and special function transforms.
Professor
Nilima Nigam is a Professor of Mathematics at Simon Fraser University (SFU), part of the Department of Mathematics within the Faculty of Science. She holds a Ph.D. in Applied Mathematics from the University of Delaware (1999). Her research focuses on numerical analysis of partial differential equations (PDEs), computational electromagnetics, mathematical physiology, and biomechanics. She is also actively involved in interdisciplinary projects, such as modeling muscle-tendon complexes and spectral geometry problems. Nigam serves on editorial boards for journals like the SIAM Journal on Scientific Computing and Mathematics-in-Industry Case Studies. She supervises graduate and undergraduate students in applied mathematics, with a focus on structure-preserving discretizations, computational biology, and spectral problems. Her work includes collaborations with researchers in biomechanics, notably with James Wakeling in studying muscle mechanics and energy dynamics. She has mentored numerous students through programs like the SFU Department of Mathematics USRA competition. Her teaching spans advanced courses in numerical analysis, PDEs, and mathematical physics.
Associate Professor
Dr. Ian Jeffrey is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Manitoba and leads research in the Electromagnetic Imaging Lab (EIL), focusing on wavefield imaging algorithms for agricultural and biomedical applications. His work integrates computational electromagnetics, optimization, and machine learning. Research interests include time-domain imaging, generative adversarial networks for calibration, and Bayesian machine learning for uncertainty quantification. Dr. Jeffrey's publications emphasize machine learning-enhanced microwave imaging, computational methods, and industrial applications like grain storage safety. He is actively seeking MSc and PhD students with C++ experience. No scientific awards were listed. Graduate opportunities focus on microwave imaging, computational electromagnetics, and GPU computing.
