Lu Wang
Assistant Professor
Biomedical Engineering, Cullen College of Engineering
Health Systems and Population Health Sciences, Tilman J. Fertitta Family College of Medicine
University of Houston
RM 1309, 5055 Medical Cir, Houston, TX 77204
Recruiting
I am actively recruiting self-motivated graduate students (Ph.D. and master students for Fall 2023, Spring 2024 and Fall 2024) in data mining, machine learning, health informatics, medical data science including NLP and CV.
About me
I am currently an Assistant Professor in Departments of Biomedical Engineering (Cullen College of Engineering) and Health Systems & Population Health Sciences (Tilman J. Fertitta Family College of Medicine) at the University of Houston. Before that, I was an Assistant Professor in Computer Science at the Texas State University since Fall 2022. I obtained my second Ph.D. in Industrial Engineering at the University of Toronto working as the data science team lead and a graduate research assistant directed by Prof. Mark Chignell. I also received my first Ph.D. degree in Computer Science from Wayne State University in 2019. I received Bachelor of Arts in Statistics at the University of Minnesota, Twin Cities.
Research Interests
My primary research interests are developing and proposing Machine Learning (ML), 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 (EHRs/EMRs), 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, I also design and develop 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.
GRANTS
2023
Wang, Lu. CRII:SCH: Interactive Explainable Deep Survival Analysis, NSF, Federal, $174964. (Submitted: September 20, 2022, Funded: 2023 - 2025). Grant.
2022
Metsis, Vangelis (Principal), Ngu, Hee Hiong (Co-Principal), Guirguis, Mina S (Supporting), Qasem, Apan Muhammad (Supporting), Hou, Tao (Supporting), Lee, Chul Ho (Supporting), Rathore, Heena (Supporting), Wang, Lu (Supporting). High Performance Computing GPU Server for Machine Learning Research, Materials Application Research Center (MARC), Texas State University, $49512. (Submitted: September 7, 2022, Funded: 2022 - 2023). Grant.
Yang, Kecheng (Principal), Ngu, Hee Hiong (Co-Principal), Chen, Xiao (Supporting), Islam, Tanzima Z (Supporting), Metsis, Vangelis (Supporting), Hou, Tao (Supporting), Rathore, Heena (Supporting), Wang, Lu (Supporting). REU Site: Research Experiences for Undergraduates in Edge Computing, National Science Foundation (NSF), Federal, $389995. (Submitted: August 2021, Funded: March 1, 2022 - February 28, 2025). Grant.
Research Publications
Journal Papers:
1. Lu Wang, Mark Chignell, Yilun Zhang, Baizun Shan, Chenjie Zhao, Timothy Chan, Saeha Shin, Arjumand Siddiqi, Andrew Pinto, Fahad Razak, Kathleen Sheehan, and Amol Verma.“Can Physician Expertise Improve Machine Learning Identification of Delirium?: Towards Trustworthy AI in Healthcare”. Under minor revision in IEEE Journal of Biomedical Informatics (JBI). (IF: 7.364)
2. 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 (2023): 1-30. (IF: 5.581)
3. Chung, Mu-Huan, Yuhong Yang, Lu Wang, Greg Cento, Khilan Jerath, Abhay Raman, David Lie, and Mark H. Chignell. "Implementing Data Exfiltration Defense in Situ: A Survey of Countermeasures and Human Involvement." ACM Computing Surveys (2023).
4. Lu Wang, Haoyan Jiang, Yan Li and Mark Chignell.“Better explanation of Feature Importance in XAI with Multi-Task Learning: A Behavioral Risk Factors Surveillance System Case Study”. Under minor revision in ACM Transactions on Interactive Intelligent Systems (ACM TIIS).
5. Chung, Mu-Huan Miles, Yuhong Alisha Yang, Lu Wang, Greg Cento, Khilan Jerath, Parwinder Taank, Abhay Raman, Jonathan H. Chan, and Mark H. Chignell. "Enhancing cybersecurity situation awareness through visualization: A USB data exfiltration case study." Heliyon 9, no. 1 (2023).
6. Lu Wang, Zhang, Y., Chignell, M., Shan, B., Sheehan, K. A., Razak, F., & Verma, A. (2022). Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study. JMIR Medical Informatics, 10(12), e38161.
7. 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 (2021), pp.1134-1161. (IF: 4.418)
8. Yan Li, Lu Wang, Jiayu Zhou and Jieping Ye. “Multi-Task Learning based Survival Analysis for Multi-Source Block-wise Missing Data”. Neurocomputing 364 (2019): 95-107. (IF: 5.719)
9. Lu Wang, Zhu Dongxiao, and Dong Ming. “Clustering over-dispersed data with mixed feature types”. Statistical Analysis and Data Mining: The ASA Data Science Journal 11.2 (2018): 55-65.
10. Lu Wang, Lipi Acharya, Changxin Bai and Dongxiao Zhu.“Transcriptome Assembly Strategies for Precision Medicine”. Quantitative Biology, vol. 5, issue 4, pp. 280-290, December 2017.
11. L. Zhang, X. Cao, Lu Wang, X. Zhao, S. Zhang, P. Wang. “Printed microwells with highly stable thin-film enzyme coatings for point-of-care multiplex bioassay of blood samples”. Analyst, Royal Society of Chemistry, vol. 140, issue 12, pp. 4105-4113, June 2015.
Conference Papers:
1. 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..
2. Lu Wang, Hao Liu, Mark Chignell, Haoyan Jiang, Sachinthya Lokuge, Geneva Mason, Kathryn Fotinos and Martin Katzman. “Impact of SNRIs vs. SSRIs drugs on Different Depression Symptoms”. Submitted to 2022 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI 2022).
3. Lu Wang, Mark Chignell, Yilun Zhang, Fahad Razak, Kathleen Sheehan, and Amol Verma. “Identifying Delirium Risk Factors: Engineered Features Dominate”. Submitted to 2022 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI 2022).
4. 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).
5. 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.
6. Lu Wang, Mark Chignell, Haoyan Jiang, Sachinthya Lokuge, Geneva Mason, Kathryn Fotinos and Martin Katzman. “Prioritization of Multi-level Risk Factors, and Predicting Changes in Depression Ratings after Treatment Using Multi-Task Learning”. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021), pp.3239-3244. IEEE, 2021.
7. Lu Wang, Mark Chignell, Haoyan Jiang, Sachinthya Lokuge, Geneva Mason, Kathryn Fotinos and Martin Katzman. “Hierarchical Clusters of Multi-Study Depression Data Yields Four Symptom Clusters”. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021), pp. 3245-3250. IEEE, 2021.
8. Sachinthya Lokuge, Lu Wang, Kathryn Fotinos, Geneva Mason, Haoyan Jiang, Mark Chignell, Martin A. Katzman. “Exploring the Relationship Between Heterogeneous Symptoms of Major Depressive Disorder and Antidepressant Response: Preliminary Findings”. 2021 American Society of Clinical Psychopharmacology (ASCP) Annual Meeting.
9. 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.
10. Mu-Huan Chung, Mark Chignell, Lu Wang, Alexandra Jovicic and Abhay Raman. “Interactive Machine Learning for Data Exfiltration Detection”. The 2020 IEEE International Conference on Systems, Man and Cybernetics (SMC 2020), pp. 280-287.
11. Lu Wang, Mark Chignell, Haoyan Jiang and Nipon Charoenkitkarn.“Cluster-Boosted Multi-Task Learning Framework for Survival Analysis”. 2020 IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2020), pp. 255-262.
12. Lu Wang, Dongxiao Zhu, Elizabeth K. Towner and Ming Dong. “Prioritization of Multi-Level Risk Factors for Obesity”. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019), San Diego, CA, USA, 2019, pp. 1065-1072.
13. Mahsa Rouzbahman, Lu Wang, Mark Chignell and Lisa Barbera. “Predicting Emergency Department Visits Based on Cancer Patient Types”. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019), San Diego, CA, USA, 2019, pp.1605-1612.
14. Mahsa Rouzbahman, Alexandra Jovicic, Lu Wang, Leon Zucherman, Nipon Charoenkitkarn, Zahid Abul-Basher and Mark Chignell. “Tuning a Cancer Patient Typology Based on Emergency Department Visits”. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019), San Diego, CA, USA, 2019, pp. 1613-1619.
15. Mahsa Rouzbahman, Alexandra Jovicic, Lu Wang, Leon Zucherman, Nipon Charoenkitkarn, Zahid Abul-Basher and Mark Chignell. “Data Mining Methods for Real-Time Decision Support: Optimizing Feature Extraction and Model Prediction”. Proceedings of the 11th International Conference on Advances in Information Technology. 2020.
16. Elizabeth K. Towner, Lu Wang, Alex B. Hil, Ming Dong, and Dongxiao Zhu. “ Using Multi-Task Learning To Develop Risk Profiles For Reducing Preschool Obesity Health Inequities”. In Annals Of Behavioral Medicine, vol. 52, pp. S85-S85. Journals Dept, Oxford University Press INC, April 2018.
17. Lu Wang, Dongxiao Zhu, Elizabeth K. Towner and Ming Dong. “Obesity Risk Factors Ranking Using Multi-Task Learning”. In Biomedical and Health Informatics (BHI 2018), 2018 IEEE EMBS International Conference on, pp. 385−388. Las Vegas, USA, March 2018.
18. Lu Wang, Yan Li, Jiayu Zhou, Dongxiao Zhu and Jieping Ye. “Multi-task Survival Analysis”. In the 17th IEEE International Conference on Data Mining (ICDM 2017), pp. 485-494. New Orleans, USA, November 2017. (long paper)
19. Lu Wang, Dongxiao Zhu, Yan Li and Ming Dong. “Modeling Over-dispersion for Network Data Clustering”. In the 16th IEEE International Conference on Machine Learning and Applications (ICMLA 2017), pp. 42-49. Cancun, Mexico, December 2017. (The Best Paper Top 3 Finalist.)
20. Yan Li, Lu Wang, Jie Wang, Jieping Ye and Chandan K. Reddy. “Transfer Learning for Survival Analysis via Efficient L2,1-norm Regularized Cox Regression”. In the 16th IEEE International Conference on Data Mining (ICDM 2016), pp. 231-240. Barcelona, Spain, December 2016. (long paper)
21. Lu Wang, Dongxiao Zhu, Yan Li and Ming Dong. “Poisson-Markov Mixture Model and Parallel Algorithm for Binning Massive and Heterogenous DNA Sequencing Reads”. In International Symposium on Bioinformatics Research and Applications (ISBRA 2016), pp. 15-26. Springer, Cham. Minsk, Belarus, June 2016.
Posters & Preprints:
1. Lu Wang, Haoyan Jiang and Mark Chignell.“MD-MTL: An Ensemble Med-Multi-Task Learning Package for DiseaseScores Prediction and Multi-Level Risk Factor Analysis.” arXiv preprint arXiv:2103.03436 (2021).
2. Lu Wang, Dongxiao Zhu, Elizabeth K. Towner and Ming Dong. “Obesity Risk Factors Ranking Using Multi-Task Learning”. 5th Annual Big Data and Business Analytics Symposium. Detroit, USA, March 2018.
Teaching Experiences
Instructor
Dept. of Biomedical Engineering, University of Houston
• BIOE 6301 - Statistical Methods in Biomedical Engineering Fall 2023
Dept. of Computer Science, Texas State University
• CS 7389G - Human-Centered Data Science Summer 2023
• CS 4326 - Human Factors of Computer System Fall 2022, Spring 2023
• CS 5326 - Advanced Human Factors of Computer Science Fall 2022, Spring 2023
Co-Instructor
Dept. of Mechanical & Industrial Engineering, University of Toronto
• MIE 1402 - Experimental Methods in Human Factors Research Fall 2021
• MIE 1402 - Experimental Methods in Human Factors Research Fall 2020
Graduate Teaching Assistant
Dept. of Mechanical & Industrial Engineering, University of Toronto
• MIE 253 - Data Modeling Winter 2020
• MIE 1402 - Experimental Methods in Human Factors Research Fall 2019
• MIE 1501 - Knowledge Modeling and Management Fall 2019
Graduate Teaching Assistant
Computer Science, Wayne State University
• CSC5825 - Introduction to Machine Learning and Applications Winter 2017
• CSC7825 - Machine Learning Fall 2016
• CSC3110 - Algorithm Design and Analysis Fall 2016
• CSC5991 - Special Topic on Machine Learning Winter 2016
• CSC6580 - Design and Analysis of Algorithms Winter 2016
• CSC4500 - Theory of Languages and Automata Fall 2015
Research Supervising of Graduate Students
Dept. of Mechanical & Industrial Engineering, University of Toronto
• Willian Ferrie Fall 2022 - Present
• Shihao Hu Summer 2022 - Present
• Chenhao Zhu Summer 2022 - Present
• Hao Liu Winter 2022 - Fall 2022
• Yilun Zhang Winter 2020 - 2021
• Baizun Shan Winter 2020 - 2021
• Chenjie Zhao Winter 2020 - 2021
Research Supervising of Undergraduate Students
Engineering Science, University of Toronto
• Muhammad Ahsan Kaleem Summer 2021
Research Experiences
Research Assistant/Data Science Team Lead May 2019- Present
Interactive Media Lab
Dept. of Mechanical and Industrial Engineering, University of Toronto
• Health/(Bio)medical Informatics and Bioinformatics
• Interactive Machine Learning
• Human-center Artificial Intelligence
Machine Learning Expert May 2019 - Present
GEMINI
Machine learning models development in health informatics.
Machine Learning Expert Feb. 2020 - Present
Sun Life Financial
Machine learning models development in cyber security.
Research Intern May 2018 - July 2018
Chinese University of Hong Kong, Shenzhen, China Big data analysis on healthcare.
Research Intern May 2017 - Aug. 2017
Karmanos Cancer Institute, Detroit, Michigan, USA
Data analysis on gene mutation based cancer drug development using the data from Caris Life Sciences.
Research Intern May 2016 - Aug. 2016
Stowers Institute for Medical Research, Kansas City, Missouri, USA
Single cell RNA seq data analysis exploring different methods for gene differential expression analysis and spatial analysis.
Research Assistant May 2015- April 2019
Data Mining and Knowledge Discovery Lab
Dept. of Computer Science, Wayne State University
• Bioinformatics: Develop novel methods in data mining and machine learning including identifying unknown resources of short DNA sequencing reads data and assigning these sequencing reads to the known resources.
• Health Informatics: Propose novel joint mixture models to estimate cluster size distribution together with cluster compactness on mixed feature data and network data.
Research Assistant Jan. 2013-May 2014
Nano Technology Lab
Dept. of Bioproducts and Biosystems Engineering, University of Minnesota-Twin Cities
• Enzyme engineering : High performance nanostructured biocatalysts for bioproducts, functional materials, energy, biomedical and environmental protection applications.
• Biosensor : Thin-film paper sensor based on coloration to show the color intensity related to the amounts of detected levels for various medical detections.
Research Intern Feb. 2012 - Dec. 2012
Chinese Academy of Sciences (Xi’An Institute of Optics and Precision Mechanics of CAS), Xi’An, China
Prepare experimental materials, design experiments and translate English papers; Conduct experiments and record experimental results; Data analysis.
Honors & Awards
• Winter 2022 SGS Conference Grant, University of Toronto May 2022
• MIE Winter 2022 Travel Grant, University of Toronto Feb. 2022
• 2019-2020 Recipient of the Glynn Williams Fellowship, University of Toronto December 2019
• MIE Fall 2019 Travel Grant, University of Toronto December 2019
• Winner of Competitive Graduate Research Assistant (CGRA) Award (worth US $23,472), Wayne State University 2018-2019
• Honorable mention of Ralph H. Kummler Award for Distinguished Achievement in Graduate Student Research, Wayne State University March 2019
• NSF Student Travel Award, IEEE Conference on Biomedical and Health Informatics (BHI) March 2018
• Honorable mention of Ralph H. Kummler Award for Distinguished Achievement in Graduate Student Research, Wayne State University March 2018
• The Best Paper Top 3 Finalist, 16th IEEE International Conference on Machine Learning and Applications (ICMLA) December 2017
• NSF Student Travel Award, 17th IEEE International Conference on Data Mining (ICDM) November 2017
Services
Associate Editor:
• Smart Health Journal
Journal/conference reviewer for:
• Bioinformatics - Oxford Academic
• Scientific Reports - Nature
• IEEE Transaction on Knowledge Discovery from Data
• IEEE Transactions on Emerging Topics in Computational Intelligence
• IEEE Access
• IEEE International Conference on Machine Learning and Applications
• IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (IEEE&ACM CHASE)
Miscellaneous
My hometown is Baoji, Shaanxi, China and my Chinese name is 王璐.
I come from a four-generation christian family (my mom's), so certainly I am a christian from very young age.
Whenever I am not doing research, I love chatting with friends and family, reading and eating (extensively).