Nick Harvey

Dr. Nick Harvey is a Full Professor in the Department of Computer Science at the University of British Columbia (UBC), with an affiliation to the Department of Mathematics. He leads the Mathematics of Information, Learning and Data (MILD) research cluster. His research focuses on randomized algorithms, machine learning theory, and convex optimization, with notable contributions to algorithm design and analysis. Harvey has taught courses such as Randomized Algorithms (CPSC 436R/536N), Theory of Computing (CPSC 421), and Advanced Algorithms (CPSC 420). He has received prestigious awards including the NeurIPS 2018 Best Paper Award and the CS-Can/Info-Can Outstanding Young Research Prize (2014).

His work emphasizes probabilistic techniques in algorithms, including applications in graph theory, streaming algorithms, and privacy-preserving methods. Harvey supervises graduate and undergraduate students, contributing to their academic and professional development. His research group explores cutting-edge topics in algorithmic theory and machine learning, with ongoing projects in algorithmic fairness, optimization, and data science.

Key achievements include advancing the Lovász Local Lemma, developing efficient graph sparsification techniques, and pioneering work on the complexity of matrix completion. Harvey's teaching philosophy prioritizes clarity and rigor, reflected in his award-winning pedagogy. He actively engages in academic service, including curriculum development and conference organization.