Dr. Lu Wang is currently a joint Assistant Professor in Biomedical Engineering (Cullen College of Engineering) and Health Systems and Population Health Science (Tilman J. Fertitta Family College of Medicine) at the University of Houston. Prior to UH, she was an Assistant Professor in Computer Science at the Texas State University. She obtained her Industrial Engineering Ph.D. at the University of Toronto and before that she gained her first Ph.D. with a Computer Science major at the Wayne State University. She received her undergraduate degree in Statistics from the University of Minnesota, Twin Cities.
Her primary research interests are developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records, and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer's disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support.
She has been published in several journals, both nationally and internationally, as well as having presented in numerous conferences including IEEE International Conference on Data Mining (IEEE ICDM), Data Mining and Knowledge Discovery (DMKD Journal), ACM Transactions on Computer-Human Interaction (TOCHI), Journal of Medical Internet Research (JMIR) Medical Informatics, American Medical Informatics Association (AMIA) Informatics Summit, IEEE EMBS International Conference on Biomedical and Health Informatics (IEEE EMBS BHI), IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), Alzheimer's Association International Conference (AAIC), Neurocomputing journal, etc.
She serves as the associate editor of Smart Health journal and her research has been funded by NSF and CIHR, etc.
The Wang Research Group (Data-Driven & Human-Centered AI in Healthcare and Medical Research [DHAI] Lab) works on developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on healthcare and (bio)medical data. Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support.
• Health/Biomedical Informatics
• Human-Centered Data Science
• Data-Driven Decision Making (D3M)
• Interactive Machine Learning
• Explainable Artificial Intelligence (XAI)
• Human-Centered AI
• Trustworthy AI
• Human-Centered Interactive Explainable Algorithms
- Mark Chignell and Lu Wang. "The evolution of HCI and human factors: Integrating human and artificial intelligence." ACM Transactions on Computer-Human Interaction 30, no. 2: 1-30. (IF: 5.581), 2023.
- Lu Wang, Mark Chignell, Yilun Zhang, Saeha Shin, Fahad Razak, Kathleen Sheehan, and Amol Verma.“Physician Experience Design (PXD) for Making Machine Learning Prediction More Usable for Clinical Decision Making”. In AMIA Annual Symposium Proceedings (Vol. 2022, p. 476). American Medical Informatics Association, 2022.
- Lu Wang, Zhang, Y., Chignell, M., Shan, B., Sheehan, K. A., Razak, F., & Verma, A. Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study. JMIR Medical Informatics, 10(12), e38161, 2022.
- Lu Wang, Mark Chignell, Haoyan Jiang, Sachinthya Lokuge, Geneva Mason, Kathryn Fotinos and Martin Katzman. “Discovering the Causal Structure of the Hamilton Rating Scale for Depression Using Causal Discovery”. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI 2021), pp. 1-4. IEEE, 2021.
- Lu Wang, Yan Li and Mark Chignell. “Combining Ranking and Point-wise Losses for Training Deep Survival Analysis Models”. In 2021 IEEE International Conference on Data Mining (ICDM 2021), pp. 689-698. IEEE, 2021. (long paper), 2021.
- Lu Wang and Dongxiao Zhu. “Tackling Multiple Ordinal Regression Problems: Sparse and Deep Multi-Task Learning Approaches”. Data Mining and Knowledge Discovery (DMKD), 35.3, pp.1134-1161. (IF: 4.418), 2021.
- Lu Wang and Mark Chignell. “Tackling Alzheimer’s Disease Diagnostic Problem: A Deep Multi-Task Learning Approach.” Alzheimer’s Association International Conference AAIC Neuroscience Next. ALZ, 2020.
- Yan Li, Lu Wang, Jiayu Zhou and Jieping Ye. “Multi-Task Learning based Survival Analysis for Multi-Source Block-wise Missing Data”. Neurocomputing 364: 95-107. (IF: 5.719), 2019.