Nicholas Harvey

Nicholas Harvey is an Assistant Professor at the University of British Columbia in the Department of Computer Science. He also serves as the Director of the Mathematics of Information, Learning and Data (MILD) research cluster and is an Associate Member of the Department of Mathematics.

• Education:
  • PhD in Theoretical Computer Science from the Massachusetts Institute of Technology (2008)
  • Postdoctoral researcher at Microsoft Research New England (2008–2009)

Harvey's research focuses on the theory of machine learning, randomized algorithms, and convex optimization. His work bridges theoretical computer science with practical algorithm design, particularly for combinatorial optimization problems. He is currently drafting a textbook on randomized algorithms for undergraduate and graduate courses.

Key trends in his publications include graph sparsification, random matrix theory, network algorithms, and dependent rounding techniques. These works often intersect with high-dimensional data analysis, streaming algorithms, and approximation methods.

• Scientific Awards:
  • 2013 Alfred P. Sloan Research Fellowship
  • Best Student Paper Award at FOCS 2006
  • Best Paper Award at USITS 2003

Harvey has held the Canada Research Chair since 2011 and has contributed to programs like Bridging Continuous and Discrete Optimization (2017) and Algorithmic Spectral Graph Theory (2014) as a Visiting Scientist and Organizer. His teaching portfolio includes CPSC 536N: Randomized Algorithms (graduate) and CPSC 436R: Introduction to Randomized Algorithms (undergraduate), both focusing on probabilistic tools, streaming algorithms, and applications to graph theory.