Michael P. Friedlander

Michael P. Friedlander

Professor of Computational Mathematics
Department of Computer Science
Department of Mathematics
Mathematical Programming @ UBC
University of British Columbia

location_onICCS 221, 2366 Main Mall
Vancouver, BC, V6T 1Z4, Canada

ORCID iD0000-0003-0222-5222
Google Scholar GitHub arXiv

Research & Teaching

Optimization sits at the heart of modern computation: training neural networks, reconstructing medical images, and allocating resources across networks. The scale of these problems demands algorithms that are fast and whose outputs we can understand, verify, and trust.

My research develops mathematical foundations and algorithms for fitting models to data. This work draws on convex analysis, duality theory, and geometry to design methods that scale to massive datasets. Our algorithms and open-source software, including SPGL1 for sparse recovery, are used in signal processing, geophysical imaging, machine learning, and quantum computing.

Scientific and Engineering Applications Convex and Variational Analysis Algorithms for Machine Learning Nonlinear Programming Quantum Algorithms Statistical Signal Processing Structured Optimization

Research Group

The Mathematical Optimization Laboratory develops theory, algorithms, and software for large-scale optimization. Our alumni hold positions at leading research labs and universities worldwide.

LA
Layth Al-Hellawi PhD Student
KV
Kevin Kurian Thomas Vaidyan MSc Student
SY
Shiwei (Michael) Yang PhD Student

Background

  • Professor, University of British Columbia (2014–present)
  • Professor, University of California, Davis (2014–2016)
  • Assistant ⤳ Associate Professor, University of British Columbia (2004–2014)
  • Wilkinson Fellow, Argonne National Laboratory (2002–2004)
  • PhD, Stanford University, Operations Research (2002)
  • BA, Cornell University, Physics (1993)

Appointments

Current Positions

Past Leadership

Conference Organization

Past Editorial Service

Selected Publications