Daniel Sharp

Daniel Sharp is a graduate student in aeronautics and astronautics, where he is breaking new ground in uncertainty quantification for complex scientific and engineering systems. His work focuses on transport-based approaches to quantization and Bayesian inference, which provide scalable, mathematically grounded methods for enhancing prediction accuracy in high-dimensional models with sparse data. As a MathWorks Fellow, Daniel will exploit the capabilities of MParT—the Monotone Parameterization Toolkit he codeveloped—by using robust MATLAB integrations that support flexible modeling pipelines and provide an intuitive user interface. He utilizes MATLAB and the Partial Differential Equation Toolbox in conjunction with custom MATLAB executable functions to accelerate computations, test new algorithms, and facilitate adoption by researchers and practitioners. Daniel’s approach could streamline the use of advanced inference techniques, supporting faster, more reliable decision-making in areas such as aerospace simulation, energy systems, and environmental modeling.