I'm currently a PhD AI/ML Resident at Google. I am completing my PhD in the Computational Science, Engineering, and Mathematics program at the University of Texas at Austin with co-adivors Prof. Karen Willcox and Dr. Anirban Chaudhuri. My research has revolved around developing fast and data-efficient multi-fidelity surrogate models with applications in inverse problems and optimization. I have had the great privilege to be funded by and contribute to the following projects during my PhD: DARPA ASKEM & ARPA-E LOADS.
Projection-based multifidelity linear regression for data-poor applications
Developed multifidelity linear regression methods to enhance predictive accuracy in data-poor, high-dimensional applications, showing significant improvement on a hypersonic vehicle surface pressure prediction example.
Read More
Turbomachinery Blade Surrogate Modeling using Deep Learning
Leveraging convolutional neural networks for rapid and efficient aerodynamic performance evaluation, offering a faster alternative to traditional CFD solvers in turbo-machinery blade design in the early design cycle.
Read More
Optimal control of quadcopters and robonauts
Developed real-time control systems for a simulated quadcopter and a two-wheeled robot, using a state-space dynamics representation, LQR, and hill-climbing algorithms to optimize performance
Read More
Development of a nytrox-paraffin hybrid rocket engine
Student-led project to develop a hybrid rocket engine using a novel Nytrox-paraffin oxidizer and fuel combination. (Featured on the news!)
Read More
