Mathematician | Noncommutative Analysis, Spectral Theory & Mathematical Machine Learning
I am a mathematician working in noncommutative harmonic analysis, spectral theory, and the mathematical foundations of machine learning. I study how the spectrum of an operator reveals the geometry of a space and governs the dynamics on it.
My research develops analytical tools for groups, quantum groups, and von Neumann algebras. A complementary programme explores how spectral geometry can help explain the training and generalisation of neural networks.
I received my PhD in Pure Mathematics from Imperial College London and held research positions at Imperial and Queen Mary University of London. My industry experience includes machine learning at KCell, Delivery Hero, and HighSky. I now work on AI strategy and modelling at Kaspi Bank.
Fourier multipliers, operator algebras, and spectral methods for differential and evolution equations.
Explore the programme →Spectral geometry, loss landscapes, and the dynamics of neural-network training and generalisation.
Explore the programme →Multiplier estimates connecting spectral growth with analysis on noncommutative spaces.
Bounds for Fourier multipliers through the spectral projections of affiliated operators.
A framework for smooth structure and Fourier analysis on compact quantum groups.