Joseph Lee Hart
Scientific Machine Learning
Scientific Machine Learning
Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1327
Biography
Joseph is interested in quantifying, prioritizing, and mitigating uncertainty in large-scale optimization problems constrained by differential equations. His research spans numerical optimization, sensitivity analysis, inverse problems, optimal experimental design, and scientific machine learning in the service of outer loop analysis. Joseph focuses on finding and exploiting low dimensional structure which arises from taking a holistic perspective on the scientific computing pipeline from model development to decision-making.
Education
Joseph earned a B.S. in Mathematics from North Carolina State University in 2014, a M.S. in Applied Mathematics from North Carolina State University in 2016, and a Ph.D. in Applied Mathematics with an Interdisciplinary Track in Statistics in 2018, with his dissertation “Extensions of Global Sensitivity Analysis: Theory, Computation, and Application.”