Postdocs

I am a computational mathematician and my research interests lie in wielding math to design algorithms for real-world problems. Currently, I practice that in developing scalable algorithms to quantify, analyze, and control the uncertainty inherent in mathematical models. Previously, I focused on the uncertainty quantification (UQ) of large-scale inverse problems for physical phenomena governed by partial differential equations (PDEs). At the moment, I’m pursuing new directions in computational science, generally along the lines of problem structure-exploiting neural architectures and algorithms.

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I am an applied mathematician with a focus on forecasting using nonlinear dynamical systems. The main focus of my research is on reduced order modeling (ROM) of nonlinear systems with both linear and nonlinear approximations. In my work, I seek to improve the accuracy of forecasts by incorporating physical information such as observational data or conserved quantities into the reduced-order solution. Currently, I am working with tensor decompositions and developing methods for tensorial reduced-order models.