Research interests

  • Mathematical theory of data science and machine learning
  • Structure-preserving (scientific) machine learning
  • Optimization
  • Applied harmonic analysis
  • Applied probability
  • Scientific computing
  • PDE and dynamical systems
  • Computer vision and image processing
I am an applied and computational mathematician. My current research focuses on the mathematical underpinnings of data science and machine learning (ML). One of my primary objectives is to address the critical challenge of quantifying and enhancing the robustness and statistical efficiency of ML models, especially for data-scarce and resource-constrained applications. I am interested in understanding and leveraging the inherent geometric structures of the underlying systems, aiming to develop novel and efficient ML models and computational algorithms with provable guarantees.

My research draws from a diverse array of mathematical disciplines, including applied harmonic analysis, differential geometry, applied probability, PDE, and optimization. Much of my research is driven by a range of scientific and interdisciplinary applications, spanning domains from scientific computing, reduced order modeling, computer vision, to entomology and public health.