Adilet is a third-year doctoral student at Oxford University. He graduated from Nazarbayev University with a BSc in mathematics and then pursued a master's degree at the University of Manchester. His research interests include nonconvex optimization.
Adilet’s PhD research will be dedicated to finding more efficient algorithms for the most difficult of optimisation problem classes, namely, that of global optimisation, where the best optima are desired amongst possibly-many local solutions in a highly nonlinear landscape, and often in large dimensions. The grand challenge is scalability of algorithms for global optimisation, as the state of the art software can generally solve global optimisation problems in the order of ten parameters. This project plans to explore modern techniques from compressed sensing, signal processing and machine learning - where techniques have been devised to find structure/important information in large data sets - to effectively reduce the dimension of global optimisation problems so that they can be solved efficiently. This is a little-explored direction of research with great potential for impact in terms of algorithm development and transformative applications.