Lu Wang
Assistant ProfessorDr. Lu Wang is a joint Assistant Professor in Biomedical Engineering and Health Systems and Population Health Sciences at the University of Houston, leading the Data-Driven & Human-Centered AI (DHAI) Lab. Her work integrates human-centered AI, machine learning, multi-agent AI systems, digital twins, large language models, and biomedical applications to create ethical, transparent, and privacy-preserving AI systems for health.
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 [DHAI] in Healthcare and Biomedical Research Lab) advances intelligent health via human-centered interactive, and explainable AI. Our work spans explainable survival analysis, interactive digital twin enhanced treatment planning with counterfactual reasoning, graph neural networks for complex biomedical data, and agentic AI powered disease management, chronic and cognitive disease prevention, and health promotion, all delivered through a privacy-first, zero-retention inference flow for building ethical and privacy preserving AI systems.
Key Research Areas:
Explainable survival analysis & risk trajectories
Digital twins and reinforcement learning for treatment planning
Counterfactual reasoning & treatment-effect estimation
Graph neural networks for multimodal biomedical data (fMRI, genetics, molecular, longitudinal)
Temporal and longitudinal disease modeling
Agentic AI for patient and caregiver support (visualized chatbots)
Human-in-the-loop interactive machine learning & clinician-facing XAI
Privacy-first, zero-retention AI deployment
Selected Publications
- 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.