Faculty

Ahmet Alacaoglu Continuous optimization, first-order methods

Michael Friedlander Continuous optimization, numerical methods, convex and variational analysis

Nick Harvey Randomization, algorithms, learning theory, combinatorial optimization, convex optimization

Zhuan Khye Koh Combinatorial optimization, linear programming, game theory

Jiajin Li Continuous optimization, algorithm theory

Philip Loewen Variational analysis, optimal control

Joe Paat Discrete optimization, algorithms

Akshay Ramachandran Optimization, algorithms

Mark Schmidt Machine learning, continuous optimization

Bruce Shepherd Discrete and combinatorial optimization, polyhedral combinatorics

Christos Thrampoulidis Machine learning theory, high-dimensional statistics, deep learning

Julia Yan Data-driven optimization, transportation, urban mobility

/ˌmæθ.əˈmæt.ɪ.kəl ˈproʊ.ɡræm.ɪŋ/ n.

The science of decision-making under constraints.

A research community at the University of British Columbia working on optimization, algorithms, variational analysis, stochastic processes, and the mathematical foundations of decision-making. We span computer science, mathematics, engineering, and the Sauder School of Business.

Current Courses

2026W1 CPSC 536C: Algorithms for Convex Optimization (Ramachandran)

2026W1 CPSC 536S: Submodularity and Optimization (Shepherd)

2026W1 MATH 604: Optimization for Data Science (Alacaoglu)

2026W1 EECE 571Z: Convex Optimization (Thrampoulidis)

2026W1 BAMS 506: Optimal Decision Making I (Paat)

2026W1 BAMS 508: Optimal Decision Making II (Paat)

Prospective Students

We seek mathematically inclined graduate students and offer in-depth training in optimization theory, algorithm design, and computational methods.

Current students come from Computer Science, Mathematics, Statistics, and Electrical and Computer Engineering—a mix that enriches our work through diverse perspectives on optimization and computation.

Students may apply through any of these departments and can be co-supervised across units. If you are drawn to problems that blend theory with computation and application, we encourage you to apply.