My research interests encompass Theoretical Data Science, Machine Learning, Computer Vision, and Network Science. Specifically, the invention of mathematical and statistical foundation for Machine Learning, Computer Vision, and Network Science. I am interested in developing novel Nonlinear Dimensionality Reduction (NDR) algorithms to ensure the geometry and smoothness of the underlying manifold of high-dimensional data. I use low-rank Matrix Completion (MC) techniques to recover corrupted distance matrices produced by partially observed data. I utilize MC techniques to capture and characterize topology and utilize NDR to learn the temporal dimensionality of large market movements to detect perturbations in the U.S. financial system. I produce Artificial Neural Networks frameworks to reconstruct fragmented trajectories of collective motion and predict U.S. financial markets. I am also interested in formulating patch-based image denoising methods using eigenvectors of the geodesics’ Gramian matrix.

I have been teaching Mathematics, Statistics, and Data Science related classes since 2007. I have been teaching undergraduate and graduate classes at the University of Rhode Island since Fall 2023. I also taught undergraduate and graduate classes at Texas A&M University--Corpus Christi from Fall 2019 to Summer 2023. Before that, I had undergone diverse teaching assignments as follows: 3 years at Worcester Polytechnic Institute (WPI), USA; 3 years at Clarkson University, USA; 4 years at both Uva Wellassa University and University of Peradeniya, in Sri Lanka.

I utilize classroom technology such as PDF Annotator, Echo360, etc, in my teaching and enjoy teaching online classes apart from regular classes. I engaged with curriculum development and decision-making as a Faculty Board Member and Course Committee Member at Uva Wellassa University, Sri Lanka.