Stephen Vavasis

Vavasis received a Bachelors in Mathematics from Princeton in 1984, a Masters (i.e., Part III of the Tripos) in Mathematics from Cambridge in 1985, and PhD in Computer Science from Stanford in 1989. He was an assistant, then associate, then full professor of computer science at Cornell University from 1989 to 2006. Since 2006 he has been a professor in the Department of Combinatorics and Optimization at University of Waterloo. He served as Associate Dean for Computing for the Faculty of Mathematics 2017-2020. He has held summer or sabbatical positions at Argonne, Sandia, Bell Labs, Xerox PARC, NASA Ames and elsewhere. He is a past winner of the Hertz Graduate Fellowship, Churchill Scholarship, Presidential Young Investigator award, and Guggenheim Fellowship.

He is currently the director of graduate Data Science programs.

Vavasis's primary research areas are continuous optimization and application of continuous optimization to data science. He is also interested in scientific computing, numerical linear algebra, computational mechanics, and computational geometry. His research receives support from NSERC.

Current research interests

Convex optimization for data science: Convex optimization is powerful tool for solving problems in data science. Efficient algorithms can be brought to bear, and duality opens the door to analysis of the optimizer to establish that it solves the original application. Vavasis and his students have applied convex optimization to unsupervised machine learning problems of clustering, nonnegative matrix factorization, and the disjoint communities problem.

First-order methods: First order methods solve optimization problems using first-derivative information. Several "optimal" methods such as conjugate gradient, accelerated gradient, and geometric descent have been proposed. Recent work shows these methods have a unifying analysis. This leads to the development of new and more efficient algorithms.

Optimization in computational mechanics: With colleagues and students, Vavasis applies modern and powerful optimization techniques to fracture mechanics of solids. The main point is that fracture can be regarded as an energy minimization process, and hence is amenable to optimization. He is currently investigating new methods of machine learning for this class of problem.

• 1984, A.B. in Mathematics, Princeton University

• 1985, Cert. Adv. Study Mathematics, University of Cambridge

• 1989, PhD in Computer Science, Stanford University

• 2017-2020 Associate Dean of Computing (Faculty of Mathematics)

• 2021 Interim Director of Graduate Data Science Programs

• 2026-2027 Director of Graduate Data Science Programs

• CO 255 - Introduction to Optimization (Advanced Level)
  • Taught in 2025
• CO 372 - Portfolio Optimization Models
  • Taught in 2021, 2023, 2024, 2025, 2026
• CO 463 - Convex Optimization and Analysis
  • Taught in 2024
• CO 663 - Convex Optimization and Analysis
  • Taught in 2024
• CO 673 - Optimization for Data Science
  • Taught in 2023, 2024, 2025
• CO 769 - Topics in Continuous Optimization
  • Taught in 2026
• CS 794 - Optimization for Data Science
  • Taught in 2023, 2024, 2025

* Only courses taught in the past 5 years are displayed.