Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation. - Rohit Tripathy 001; Ilias Bilionis; Marcial Gonzalez (J. Comput. Phys., 2016)

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification. - Rohit K. Tripathy 001; Ilias Bilionis (J. Comput. Phys., 2018)

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks. - Sharmila Karumuri; Rohit Tripathy 001; Ilias Bilionis; Jitesh H. Panchal (J. Comput. Phys., 2020)

Selecting deep neural networks that yield consistent attribution-based interpretations for genomics. - Antonio Majdandzic; Chandana Rajesh; Ziqi Tang; Shushan Toneyan; Ethan L. Labelson; Rohit K. Tripathy 001; Peter K. Koo (MLCB, 2022)

Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. - Risa Karakida Kawaguchi; Ziqi Tang; Stephan Fischer; Chandana Rajesh; Rohit Tripathy 001; Peter K. Koo; Jesse A. Gillis (Briefings Bioinform., 2023)