Michael P. Friedlander

Michael P. Friedlander is a Professor of Computational Mathematics at the University of British Columbia (UBC), holding joint appointments in the Department of Computer Science and the Department of Mathematics. He also serves as the Director of the UBC Institute of Applied Mathematics and holds the title of Sauder School Distinguished Scholar. His research focuses on optimization, machine learning, and computational methods, with notable contributions to convex optimization, federated learning, and large-scale data analysis.

He earned a PhD in Operations Research from Stanford University (2002) and a BA in Physics from Cornell University (1993). Friedlander has held academic positions at UBC since 2004 and previously at the University of California, Davis (2014–2016) and Argonne National Laboratory (2002–2004).

His work spans theoretical and applied aspects of optimization, including development of algorithms for sparse optimization, gauge duality, and federated learning. He has authored numerous influential papers in top journals and conferences, such as SIAM Journal on Optimization and IEEE Transactions on Signal Processing. His contributions were recognized with the SIAM Fellowship (2024).

Friedlander has held leadership roles, including Board membership at the Pacific Institute for Mathematical Sciences (2017–2023) and Program Director for SIAM’s Optimization Group (2014–2020). He is an active editor for journals like the Open Journal of Mathematical Optimization and Mathematics of Operations Research.

His research lab focuses on advancing optimization theory and its applications to real-world problems, such as geochronology and signal processing. He has developed open-source software tools for optimization, including SPGL1 and TFOCS.