Research Interests
- Uncertainty Quantification
- Partial Differential Equations / Integral Equations
- Fast Multi-Level Kriging for large spatial datasets
- Fast Radial Basis Function Interpolation.
- Machine Learning / Big Data
Recent
- J.E. Castrillon-Candas, D. Liu and M. Kon. Stochastic functional analysis with applications to robust machine learning. Submitted to NeurIPS. (2021).
- J.E. Castrillon-Candas, F. Nobile and R.F. Tempone. A hybrid collocation-perturbation approach for PDEs with random domains. Adv Comput Math 47, 40 (2021).
- J. E. Castrillon-Candas and J. Xu. A stochastic collocation approach for parabolic PDEs with random11domain deformations.Computers & Mathematics with Applications, 93:32–49, 2021.
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