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Assistant Professor
Department of Mathematics
University of Maryland, College Park

Math Office : 4410, Kirwan Hall
IPST Office : 4105, Atlantic Building
       (301) 405-2054

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I am an Assistant Professor of Mathematics holding a joint position at the Department of Mathematics and the Institute for Physical Science and Technology. My research lies at the interface of conventional numerical analysis and machine learning. A few key topics of interest are listed below.

  • Scientific machine learning:

    I use deep learning tools to overcome computational bottlenecks in existing numerical methods. This is particularly relevant for techniques that require the specification of problem-dependent parameters, or ailed by computationally expensive sub-algorithms. Some areas of application I work on include

    • Shock-capturing algorithms for conservation laws.
    • Reduced order modelling for flow problems.
    • Acceleration of Monte-Carlo algorithms using deep surrogates.
    • PDE constrained optimization.
    • Physics-based deep Bayesian inference.
    • Operator learning for surrogate modelling.

  • Hyperbolic conservation laws:

    I develop numerical methods for conservation laws, which satisfy important physical model properties, such as entropy stability and kinetic energy preservation. In particular, I have developed high-order entropy-stable finite volume schemes for the compressible Euler equations, and extensions accommodate the viscous terms of the Navier-Stokes model.

“In theory, there is no difference between theory and practice. But in practice, there is.”
-- Anonymous