Tian Yu Yen

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

Selected Publications

• Butler, Troy, Timothy Wildey, and Tian Yu Yen. (2020-08-20) 2020. “Data-Consistent Inversion For Stochastic Input-To-Output Maps”. Inverse Problems 36. United States: IOPscience: Medium: ED; Size: Article No. 085015. doi:https://doi.org/10.1088/1361-6420/ab8f83.
• Butler, T., Tim Wildey, and Tian Yu Yen. (2020-08-01) 2020. “Data-Consistent Inversion For Stochastic Input-To-Output Maps”. Inverse Problems 36. doi:10.1088/1361-6420/ab8f83.
• Yen, Tian Yu Nmn. (10-2021AD) 2021. “Quantifying Aleatoric And Epistemic Uncertainties In Rlc Circuits With Data-Consistent Inversion”. Siam Uq 2022.
• Yen, Tian Yu Nmn, and Timothy Michael Wildey. (11-2021AD) 2021. “Using Manifold Learning To Enable Computationally Efficient Stochastic Inversion With High-Dimensional Data”. 15th World Congress On Computational Mechanics.