Aleksandar Nikolov

Aleksandar Nikolov is an Associate Professor in the Department of Computer Science at the University of Toronto. His research focuses on theoretical computer science, algorithms, differential privacy, combinatorial optimization, computational high-dimensional geometry, and discrepancy theory. He is a member of the Theory Group and an affiliate at the Vector Institute. Nikolov's work explores connections between high-dimensional geometry and computer science, applying geometric tools to private data analysis, discrepancy theory, and experimental design. He has also studied computational questions in high dimensions, such as nearest neighbour search and geometric optimization problems. Nikolov's research interests include approximation algorithms and sublinear and parallel algorithms for analyzing massive data. He is currently teaching the graduate course CSC2420 Algorithm Design, Analysis and Theory.

Aleksandar Nikolov is a researcher in Theoretical Computer Science with a focus on Discrepancy Theory, Private Data Analysis, and Approximation Algorithms. His work explores the intersection of computational complexity and data privacy, particularly in the context of differential privacy. Nikolov has contributed to the development of algorithms for private data release, including methods for privately releasing marginals and handling linear queries on histograms. His research also addresses the geometric and algorithmic challenges in discrepancy minimization and approximation problems, such as the largest simplex problem and optimal design of -Optimal Design. His publications highlight the use of convex relaxations, factorization mechanisms, and spectral methods in achieving privacy and efficiency.