Joseph Hart
Scholar

Joseph Hart

Google Scholar ID: z2mDOgkAAAAJ
Sandia National Laboratories
PDE-constrained optimizationuncertainty quantificationBayesian Inverse ProblemsScientific Machine Learning
Homepage↗Google Scholar↗
Citations & Impact
All-time
Citations
385
 
H-index
11
 
i10-index
11
 
Publications
20
 
Co-authors
0
 
Contact
Emailjoshart@sandia.gov
Resume
Academic Achievements
  • Published multiple academic papers, see his Google Scholar page for details.
Research Experience
  • Works at Sandia National Laboratories, focusing on Scientific Machine Learning.
Education
  • B.S. in Mathematics from North Carolina State University in 2014, M.S. in Applied Mathematics from North Carolina State University in 2016, and 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.”
Background
  • 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.