Here is a list of my publications, separated by subject area.

* Denotes equal contribution.

Machine Learning


  • Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski, Yong Han. "Reliable Graph Neural Network Explanations Through Adversarial Training", ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI, 2021
  • Phan Nguyen*, Donald Loveland*, Joanne T Kim, Piyush Karande, Anna M Hiszpanski, Yong Han. "Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning", Journal of Chemical Information and Modeling, 2021


  • Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M Hiszpanski, Yong Han. "Automated Identification of Molecular Crystals’ Packing Motifs", Journal of Chemical Information and Modeling, 2020
  • Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M Hiszpanski, Emily Robertson, Donald Loveland, Yong Han. "Actionable attribution maps for scientific machine learning", ICML Workshop on ML Interpretability for Scientific Discovery, 2020
  • Brian Gallagher, Matthew Rever, Donald Loveland, T Nathan Mundhenk, Brock Beauchamp, Emily Robertson, Golam G Jaman, Anna M. Hiszpanski, Yong Han. "Predicting Compressive Strength of Consolidated Molecular Solids using Computer Vision and Deep Learning", Materials & Design, 2020


  • Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han. "Generative Counterfactual Introspection for Explainable Deep Learning", IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019



  • Vardha N Bennert, Donald Loveland, Edward Donohue, Maren Cosens, Sean Lewis, S Komossa, Tommaso Treu, Matthew A Malkan, Nathan Milgram, Kelsi Flatland, Matthew W Auger, Daeseong Park, Mariana S Lazarova, "Studying the [OIII]λ 5007Å emission-line width in a sample of ∼ 80 local active galaxies: a surrogate for σ⋆?", Monthly Notices of the Royal Astronomical Society, 2018