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

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

/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

T2 25 CPSC 406: Computational Optimization (Bradley)

T1 25 CPSC 536M: Convex Analysis (Friedlander)

T1 25 CPSC 536N: Randomized Algorithms (Harvey)

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.