Dr. Na Zou
Biography
Dr. Na Zou is a tenure-track assistant professor in the Department of Industrial Engineering. Her research interests focus on developing effective, efficient and fair machine learning algorithms for tackling data challenges raised by large-scale, dynamic and networked data from various real-world applications, such as health informatics and bioinformatics. Specifically, Dr. Zou’s research focuses on fairness in machine learning, interpretable machine learning, transfer learning, and network modeling and inference. The research projects have resulted in publications at prestigious venues such as Technometrics, IISE Transactions and ACM Transactions, IEEE Transactions, ICLR, ICML, NeurIPS and KDD, including Best Paper Finalists, Best Student Paper Finalists, and Best Paper Awards at INFORMS, ICQSR, AMIA. Her work has been featured twice at ISE Magazine and received one student innovation award at AMIA. She was the recipient of IEEE Irv Kaufman Award, Texas A&M Institute of Data Science Career Initiation Fellow and NSF CAREER Award.
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News
2024/10: Dr. Zou received an NIH AIM AHEAD award, as MPI, on Addressing Health Disparities in Heart Transplant through Fair AI/ML Approaches.
2024/08: Dr. Zou received an NSF CISE MSI award, as PI, on Robust and Human-aligned Deep Learning for Medical-Sensor Time Series.
2024/07: Dr. Zou's paper won best paper award at ICQSR24, Como, Italy.
2023/10: Dr. Zou received an NSF III Medium award, as PI, on Effective Detection and Mitigation for Shortcut Learning.
2023/08: Dr. Zou talked to the Eagle newspaper about how artificial intelligence systems may impact operations in local city government.
2023/08: Dr. Zou was interviewed by KAGS News (NBC) on bias in machine learnig and its potential impact.
2023/05: Dr. Zou received the NSF CAREER award.
2023/04: Dr. Zou was elected as the Data Analytics and Information Systems (DAIS) Division president of the Institute of Industrial and Systems Engineering (IISE).
Honors and Awards
Research Interests
Machine Learning: Fairness in Machine Learning, Interpretable Machine Learning, Transfer Learning, Sparse Learning, Uncertainty Quantification
Network Modeling and Inference: Dynamic Networks, Network Embedding with Heterogeneous Information, Anomaly Detection
Brain Informatics: Connectivity Modeling from Neuroimaging, Cognitive Performance Assessment, Biomarker Identification, Disease Diagnosis and monitoring
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