Research

Journal publications

• “Disjunctive rule lists” INFORMS Journal on Computing, 2022, with Tong Wang. pdf doi data

• “Dental anomaly detection using intraoral photos via deep learning” Scientific Reports, 2022, with Tong Wang, Carmencita Padilla, Jacqueline T Hecht, Fernando A Poletta, Iêda M Orioli, Carmen J Buxó, Azeez Butali, Consuelo Valencia-Ramirez, Claudia Restrepo Muñeton, George L Wehby, Seth M Weinberg, Mary L Marazita, Lina M Moreno Uribe, and Brian J Howe.

  pdf doi data

Conferences

• “ProtoX: Explaining a Reinforcement Learning Agent via Prototyping” NeurIPS, 2022, with Tong Wang, Qihang Lin, and Xun Zhou. pdf doi data
• “On the Incorrectness and Inconsistency of Post Hoc Explanations for Business Research” POMS, 2024, with Tong Wang, Jeffrey Hu, and Feng Lu
• ” The Illusion of Interpretation: Post Hoc Explanations Aren’t a Silver Bullet for Business Research” *MAdAiLab Biz AI”, 2024, with Tong Wang, Jeffrey Hu, and Feng Lu

Workshops

• “SRRL: Statistically Relevant Rule Lists” INFORMS DMDA, 2024, with Marvin Nukunu-Attachey, and Nick Street
• “On the Incorectness and Inconsistency of Post Hoc Explanations for Business Research” WITS, 2023, with Tong Wang, Jeffrey Hu, and Feng Lu
• “On the Incorectness and Inconsistency of Post Hoc Explanations for Business Research” INFORMS DMDA, 2023, with Tong Wang, Jeffrey Hu, and Feng Lu

Preprints and Work in progress

• “From Model Explanation to Data Misinterpretation: Uncovering the Pitfalls of Post Hoc Explainers in Business Research” with Tong Wang, Yu (Jeffrey) Hu, and Feng Lu. (Under review + on SSRN).
• “GeoPro-Net: Learning interpretable spatiotemporal prediction models through statistically-guided geo-prototyping” by Bang An, Xun Zhou, Zirui Zhou, R. Ragodos, Zenglin Xu, and Jun Luo
• “ProtoGAIL: Interpretable Policy Learning via Prototyping for Human Decision Understanding” with Xun Zhou and Tong Wang.