Generative Counterfactual Introspection for Explainable Deep Learning. - Shusen Liu 001; Bhavya Kailkhura; Donald Loveland; Yong Han (GlobalSIP, 2019)

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)

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)

Reliable Graph Neural Network Explanations Through Adversarial Training. - Donald Loveland; Shusen Liu 001; Bhavya Kailkhura; Anna M. Hiszpanski; Yong Han (CoRR, 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)

How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. - Jiong Zhu; Junchen Jin; Donald Loveland; Michael T. Schaub; Danai Koutra (KDD, 2022)

FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing. - Donald Loveland; Jiayi Pan 002; Aaresh Farrokh Bhathena; Yiyang Lu (CoRR, 2022)

Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning. - Ioannis C. Tsaknakis; Bhavya Kailkhura; Sijia Liu 001; Donald Loveland; James Diffenderfer; Anna Maria Hiszpanski; Mingyi Hong 001 (CoRR, 2022)

On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. - Donald Loveland; Jiong Zhu; Mark Heimann; Benjamin Fish; Michael T. Schaub; Danai Koutra (CoRR, 2022)

Network Design Through Graph Neural Networks: Identifying Challenges and Improving Performance. - Donald Loveland; Rajmonda Caceres (COMPLEX NETWORKS (1), 2023)

On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. - Donald Loveland; Jiong Zhu; Mark Heimann; Benjamin Fish; Michael T. Schaub; Danai Koutra (LoG, 2023)

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems. - Xinyi Wu 003; Donald Loveland; Runjin Chen; Yozen Liu; Xin Chen 085; Leonardo Neves; Ali Jadbabaie; Clark Mingxuan Ju; Neil Shah; Tong Zhao 003 (CoRR, 2024)

MAGNET: A Multi-Agent Graph Neural Network for Efficient Bipartite Task Assignment. - Donald Loveland; James Usevitch; Zachary Serlin; Danai Koutra; Rajmonda Caceres (AAMAS, 2025)

Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks. - Donald Loveland; Danai Koutra (SDM, 2025)

Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. - Donald Loveland; Xinyi Wu 003; Tong Zhao 003; Danai Koutra; Neil Shah; Mingxuan Ju (WWW, 2025)

GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems. - Xinyi Wu 003; Donald Loveland; Runjin Chen; Yozen Liu; Xin Chen 085; Leonardo Neves; Ali Jadbabaie; Mingxuan Ju; Neil Shah; Tong Zhao 003 (WWW, 2025)

On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective. - Donald Loveland; Mingxuan Ju; Tong Zhao 003; Neil Shah; Danai Koutra (CoRR, 2025)