Publications

Journal

Yu-Neng Chuang, Kwei-Herng Lai, Ruixiang Tang, Mengnan Du, Chia-Yuan Chang, Na Zou, Xia Hu.
“Mitigating Relational Bias on Knowledge Graph.“ 2023.
ACM Transactions on Knowledge Discovery from Data (TKDD).

Can Li, Sirui Ding, Na Zou, Xia Hu, Xiaoqian Jiang, Kai Zhang.
“Multi-task Learning with Dynamic Re-weighting to Achieve Fairness in Healthcare Predictive Modeling.“ 2023.
Journal of Biomedical Informatics 143 (2023): 104399.

Qizhang Feng, Mengnan Du, Na Zou, Xia Hu.
“Fair Machine Learning in Healthcare: A Survey.“ 2022.
arXiv.

Mingyang Wan, Daochen Zha, Ninghao Liu, Na Zou.
Techniques for Modeling “Machine Learning Fairness: A Survey.“ 2022.
ACM Transactions on Knowledge Discovery from Data (TKDD).

Mengnan Du, Fan Yang, Na Zou, Xia Hu.
“Fairness in Deep Learning: A Computational Perspective.“
2020. IEEE Intelligent Systems. PP(99):1-1.

Abhinav Bhardwaj, Joseph Vasselli, Matt Lucht, Zhijian Pei, Brian Shaw, Zachary Grassley, Xingjian Wei1, Na Zou.
“3D Printing of Biomass-fungi Composite Material: A Preliminary Study.” 2020.
Manufacturing Letters. 24(2020):96-99.

Abhinav Bhardwaj, Scott Z Jones, Negar Kalantar, Zhijian Pei, John Vickers, Timothy Wangler, Pablo Zavattieri, Na Zou.
“Additive Manufacturing Processes for Infrastructure Construction: A Review.” 2019.
ASME Journal of Manufacturing Science and Engineering. 141(9).

Na Zou, Xiao Huang.
“Empirical Bayes Transfer Learning for Uncertainty Characterization in Predicting Parkinson’s Disease Severity.” 2018.
IISE Transactions on Healthcare Systems Engineering. 8(3):209-219. (Featured in ISE Magazine).

Xiao Huang, Jundong Li, Na Zou, Xia Hu.
“A General Embedding Framework for Heterogeneous Information Learning in Large-scale Networks.” 2018.
ACM Transactions on Knowledge Discovery from Data. 12 (2018): 70:1-70:24. (INFORMS QSR Best Student Paper Award Finalist).

Hande Cakin, Berk Gorgulu, Mustafa Gokce Baydogan, Na Zou, Sinan Kerem Tuncel, Jing Li.
“A Data Adaptive Biological Sequence Representation for Supervised Learning.” 2018.
Journal of Healthcare Informatics Research. 2(4):448-471.

Na Zou, Jing Li.
“Modeling and Change Detection of Dynamic Networks by a Network State Space Model.” 2017.
IISE Transactions. 49(1):45-57. (Featured in ISE Magazine).

Dmitry Titov, Janine Diehl-Schmid, Kuangyu Shi, Robert Perneczky, Na Zou, Stefan Förster, Timo Grimmer, Jing Li, Alexander Drzezga, Igor Yakushev.
“Metabolic Connectivity for Differential Diagnosis of Dementing Disorders.” 2017.
Journal of Cerebral Blood Flow & Metabolism. 37(1):252-262 (impact factor 5.41, ranked 18 among 335 journals in Neurology).

Na Zou, Mustafa Baydogan, Yun Zhu, Wei Wang, Ji Zhu, Jing Li.
“A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems.” 2015.
Technometrics. 55(3):362-373.

Na Zou, Gael Chetelat, Mustafa Baydogan, Jing Li, Florian Fischer, Dmitry Titov, Juergen Dukart, Andreas Fellgiebel, Mathias Schreckenberger, Igor Yakushev.
“Metabolic Connectivity as Index of Verbal Working Memory.” 2015.
Journal of Cerebral Blood Flow & Metabolism. 35:1122-1126 (impact factor 5.41, ranked 18 among 335 journals in Neurology).

Refereed Conference Papers

Hongyi Ling, Zhimeng Jiang, Youzhi Luo, Shuiwang Ji, Na Zou
“Learning Fair Graph Representations via Automated Data Augmentations.“ 2023.
The International Conference on Learning Representations (ICLR)

Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji, Na Zou
“Graph Mixup with Soft Alignments.“ 2023.
The International Conference on Machine Learning (ICML)

Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu.
“Mitigating Algorithmic Bias with Limited Annotations.“ 2023.
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'23).

Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu.
“Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning." 2022.
The AMIA 2022 Annual Symposium. (Best Student Paper Finalists)

Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu.
“Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution.” 2021.
The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). (Acceptance rate as 15.4%)

Ruixiang Tang, Mengnan Du,Yuening Li, Zirui Liu, Na Zou, Xia Hu.
“Mitigating Gender Bias in Captioning Systems.” 2021.
International World Wide Web Conference (TheWebConf). (Acceptance rate as 20.6%)

Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu.
“A Unified Taylor Framework for Revisiting Attribution Methods.” 2021.
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). (Acceptance rate as 21%).

Kaixiong Zhou, Qingquan Song, Daochen Zha, Na Zou and Xia Hu.
“MultiChannel Graph Convolutional Networks.” 2020.
The 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI). (Acceptance rate as 12.6%)

Zhengyang Wang, Na Zou, Dinggang Shen, and Shuiwang Ji.
“Non-local U-Net for Biomedical Image Segmentation.” 2020.
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). (Acceptance rate as 20.6%)

Hao Yuan, Na Zou, Shaoting Zhang, Hanchuan Peng, and Shuiwang Ji.
“Learning Hierarchical and Shared Features for Improving 3D Neuron Reconstruction.” 2019.
IEEE International Conference on Data Mining (ICDM). (Acceptance rate as 9.08%)

Yuening Li, Xiao Huang, Jundong Li, Mengnan Du and Na Zou.
“SpecAE: Spectral Autoencoder for Anomaly Detection in Attributed Networks.” 2019.
The 28th ACM International Conference on Information and Knowledge Management (CIKM). (Acceptance rate as 21% and INFORMS QSR Best Referred Paper Award Finalist).

Abhinav Bhardwaj, Negar Kalantar, Elmer Molina, Na Zou, Zhijian Pei.
“Extrusion Based 3D Printing of Porcelain: Feasible Regions.” 2019.
Proceedings of the ASME 2019 International Manufacturing Science and Engineering Conference (MSEC).

Presentations

Tutorial on "Data-centric AI". KDD 2023, Aug. 8, 2023.
Workshop on "Fairness in AI/ML: Mitigation Methods and Applications.". AI For Health Equity 2023 Symposium (AIHES2023), Jul. 21, 2023.
Tutorial on "Fairness in Machine Learning for Healthcare..". The 39th Quality and Productivity Research Conference (QPRC), Jun. 5, 2023.
Panel on “Fairness and Interpretability in AI/ML.” INFORMS, Oct. 15, 2022.
“Fairness in Machine Learning: Measurements and Mitigation from a computational perspective.” Virginia Tech, Apr. 8, 2022.
“Fairness in Machine Learning: a computational perspective.” IISE DAIS Webinar, Jan. 19, 2021.
“Empirical Bayes Transfer Learning for Uncertainty Characterization in Predicting Parkinson’s Disease Severity.” IISE Annual Conference, Orlando, May 18-21, 2019.
“A General Embedding Framework for Heterogeneous Information Learning in Large-scale Networks.” INFORMS Annual Conference, Phoenix, Nov. 4-7, 2018.
“Spatial-temporal Transfer Learning: From Brain Disease to Networks.” University of Houston, Houston, Feb. 9, 2018.
“A Data Adaptive Categorical Time Series Representation for Supervised Learning.” SIAM International Conference on Data Mining, Houston, Apr. 27-29, 2017.
“Modeling and Change Detection of Dynamic Networks.” INFORMS (Institute for Operations Research and the Management Sciences) Annual Conference, Nashville, Nov. 13-16, 2016.
“A Probabilistic Framework of Transfer Learning – Theory and Application.” Texas A&M University, College Station, Mar. 8, 2016.
“A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems.” INFORMS Annual Conference, Philadelphia, Nov. 1-4, 2015.
“A Probabilistic Framework of Transfer Learning – Theory and Application.” INFORMS ASU Chapter, Phoenix, Apr. 24, 2015.
“Design of Concrete Guardrail and Vehicle Impact Simulation on City Viaduct Roads.” 2nd National Competition of Transportation Science and Technology for Students, Shanghai, China, May 2007.