Research summary
We are living in the age of analytics, and the need for sophisticated data analysis is only increasing. Dr. Aleksander Nikolov, Canada Research Chair in Algorithms and Private Data Analysis, is contributing ideas to this field by investigating the geometry of data analysis.Nikolov and his research team are applying techniques from high-dimensional convex geometry and probability to fundamental problems in data analysis. One area of focus is private data analysis, where their goal is to develop a theory of optimal differentially private algorithms for optimization and learning. Another focus is near-neighbour search in high dimensions, where they aim to develop optimal algorithms in natural restricted computation models. They are also using geometric techniques to resolve algorithmic problems in discrepancy theory, which measures how far a given distribution deviates from the ideal.