Rustem Takhanov

My research focuses on the interplay between machine learning, optimization, and mathematics. I develop algorithmic and computational methods for solving challenging problems in machine learning theory, discrete mathematics, and theoretical computer science. A recurring theme of my work is the use of large-scale optimization and high-performance computing as tools for scientific discovery. My contributions span machine learning theory, kernel methods, natural language processing, graph algorithms, combinatorial optimization, and high-dimensional geometry. In recent years, I have become particularly interested in computational approaches to mathematical research, where modern optimization techniques and GPU-based computation can uncover structures that are difficult to access through traditional analytical methods alone. More broadly, I aim to build bridges between theoretical foundations and practical computation, developing methods that are both mathematically rigorous and computationally effective.