Tian Yu Yen
Scientific Machine Learning
Scientific Machine Learning
Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1318
Biography
Tian Yu joined Sandia as a postdoc in 2021. His current research focuses on methods of aleotoric and epistemic uncertainty quantification for inverse problems as well as techniques for quantifiying uncertainty in machine learning algorithms. He has a background is in measure-theoretic inversion, Bayesian inference, and statistical density estimation. His personal vision and mission statement is included below.
"Through my career in mathematics, I aim to train and mentor the next generation of STEM professionals–especially those individuals who have faced systematic and historic barriers to the field. By pursuing impactful research and demonstrating inspiring leadership, I work to make rigorous mathematical thinking and advanced statistical tools accessible to all who seek them."
Education
PhD, Applied Mathematics, University of Colorado Denver, July 2021
MS, Statistics, University of Colorado Denver, December 2018
BA, Psychology, Reed College, May 2009
Publications
Tian Yu NMN Yen, (2022). Data-consistent Approaches to Modeling Population Distributions of Parameters for Biomedical Systems ASA Colorado-Wyoming Chapter Meeting 2022 Document ID: 1675836
Tian Yu NMN Yen, Timothy Michael Wildey, (2022). Leveraging Physics-based Surrogates for Efficient Density Estimation of Sparse Observable Data on Low-dimensional Manifolds SIAM Texas-Lousiana Section Meeting Document ID: 1675224
Gianluca Geraci, Timothy Michael Wildey, Tian Yu NMN Yen, Daniele Schiavazzi, Mahammad Motamed, (2022). Multi-fidelity information fusion with data-driven models in computational applications 17th U.S. National Congress on Computational Mechanics Document ID: 1653176
Tian Yu NMN Yen, Timothy Michael Wildey, (2022). Constructing Data-consistent Solutions to Stochastic Inverse Problems with Sparse Observable Data SIAM Conference on Mathematics of Data Science Document ID: 1641439
Tian Yu NMN Yen, Timothy Michael Wildey, (2022). Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data Wccm-apcom 2022 Document ID: 1573846
Timothy Michael Wildey, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Owen Davis, Teresa Portone, Tian Yu NMN Yen, Bryan William Reuter, Alex Gorodetsky, Ahmad Rushdi, Daniele Schiavazzi, Lauren Partin, (2022). Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ Machine Learning and Deep Learning Conference Document ID: 1573362
Timothy Michael Wildey, Troy Butler, Tian Yu NMN Yen, (2022). A Probabilistic Characterization of Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models SIAM Conference on Uncertainty Quantification Document ID: 1504576
Tian Yu NMN Yen, Timothy Michael Wildey, Troy Butler, (2022). Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-consistent Inversion SIAM Conference on Uncertainty Quantification Document ID: 1504683
Tian Yu NMN Yen, Timothy Michael Wildey, (2021). Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data 15th World Congress on Computational Mechanics Document ID: 1392127
Tian Yu NMN Yen, (2021). Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-Consistent Inversion Siam Uq 2022 Document ID: 1370036
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