Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science. - Huichen Yang; Alice Lam; Thomas Yong-Jin Han; David Buttler; William H. Hsu; Carlos A. Aguirre; Maria F. De La Torre; Derek Christensen; Luis Bobadilla; Emily Davich; Jordan Roth; Lei Luo 007; et al. (OST@ICDAR, 2019)

Deep Probabilistic Kernels for Sample-Efficient Learning. - Ankur Mallick; Chaitanya Dwivedi; Bhavya Kailkhura; Gauri Joshi; Thomas Yong-Jin Han (CoRR, 2019)

Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. - Jize Zhang; Bhavya Kailkhura; Thomas Yong-Jin Han (ICML, 2020)

Actionable Attribution Maps for Scientific Machine Learning. - Shusen Liu 001; Bhavya Kailkhura; Jize Zhang; Anna M. Hiszpanski; Emily Robertson; Donald Loveland; Thomas Yong-Jin Han (CoRR, 2020)

Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design. - Shusen Liu 001; Bhavya Kailkhura; Jize Zhang; Anna M. Hiszpanski; Emily Robertson; Donald Loveland; Thomas Yong-Jin Han (CoRR, 2020)

Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning. - Ankur Mallick; Chaitanya Dwivedi; Bhavya Kailkhura; Gauri Joshi; Thomas Yong-Jin Han (CoRR, 2020)

Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows. - Jize Zhang; Bhavya Kailkhura; Thomas Yong-Jin Han (CoRR, 2020)

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. - Anna M. Hiszpanski; Brian Gallagher; Karthik Chellappan; Peggy Li; Shusen Liu 001; Hyojin Kim 001; Jinkyu Han; Bhavya Kailkhura; David J. Buttler; Thomas Yong-Jin Han (J. Chem. Inf. Model., 2020)

Automated Identification of Molecular Crystals' Packing Motifs. - Donald Loveland; Bhavya Kailkhura; Piyush Karande; Anna M. Hiszpanski; Thomas Yong-Jin Han (J. Chem. Inf. Model., 2020)

Deep kernels with probabilistic embeddings for small-data learning. - Ankur Mallick; Chaitanya Dwivedi; Bhavya Kailkhura; Gauri Joshi; Thomas Yong-Jin Han (UAI, 2021)

Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning. - Phan Nguyen; Donald Loveland; Joanne Taery Kim; Piyush Karande; Anna M. Hiszpanski; Thomas Yong-Jin Han (J. Chem. Inf. Model., 2021)

MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. - Qunwei Li; Bhavya Kailkhura; Rushil Anirudh; Jize Zhang; Yi Zhou 017; Yingbin Liang; Thomas Yong-Jin Han; Pramod K. Varshney (SIAM J. Math. Data Sci., 2021)