I am a PhD Student in the Computational Science, Engineering, and Mathematics program at the University of Texas at Austin with co-advisors 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.
Multifidelity linear regression for scientific machine learning from scarce data
Proposed a multifidelity linear regression approach that significantly reduces model variance and improves robustness in data-scarce scenarios.
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Automating Scientific Knowledge Extraction and Modeling (ASKEM)
DARPA’s Automating Scientific Knowledge Extraction and Modeling (ASKEM) program uses AI approaches and tools to create, sustain, and enhance complex models and simulators.
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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.
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Improving Neural Network Efficiency With Multifidelity and Dimensionality Reduction Techniques
Developed projection-enabled multifidelity neural networks to reduce computational costs for a 2D aerodynamic airfoil inverse design problem.
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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.
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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
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