Lujia Wang
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Ph.D. candidate, |
About me
I received the B.S. degree in Mathematics and Applied Mathematics from the Nankai University, China, in 2013, and the M.S. degree in Probability and Mathematical Statistics from Chinese Academy of Sciences, China, in 2016. I am currently a Ph.D. student in Industrial & Systems Engineering in Georgia Institute of Technology. My research interests are statistical modeling and machine learning for various applications.
Research Interests
Methodological developments in statistical modeling and machine learning
Deep Learning
Knowledge-infused machine learning
Semi-supervised and weakly-supervised learning
Uncertainty quantification and active learning
Graphical models
Bayesian inference
Application domains
Health care
- Precision medicine of brain cancer
Imaging-based diagnosis and prognosis of neurological diseases (concussion, migraine, Alzheimer’s Disease)
Engineering domains
Monitoring, fault detection, and reliability prediction of software systems
Statistical modeling and inference for queening systems
Selected Publications
Wang, L., D’Angelob, F., ..., Hu, L.S., Li, J. Quantifying Intra-tumoral Genetic Heterogeneity of Glioblastoma toward Precision Medicine using MRI and a Data-inclusive Machine Learning Algorithm. Computer Methods and Programs in Biomedicine. (Under review) [code]
Wang, L., Zheng, Z., Su, Y., Chen, K., Weidman, D.A., Wu, T., Lo, B., Lure, F. and Li, J. (2022) Early prediction of the Alzheimer’s disease risk using Tau-PET and machine learning. In Medical Imaging 2022: Computer-Aided Diagnosis, SPIE, 12033 : 736-740. [paper]
Wang, L., Hawkins-Daarud, A., Swanson, K.R., Hu, L.S., Li, J. (2021) Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine. IEEE Transactions on Automation Science and Engineering, in press. [paper]
Wang, L., Schwedt, T J., Chong, C. D., Wu, T., Li, J. (2021) Discriminant Subgraph Learning from Functional Brain Sensory Data. IISE Transactions, 1-14. [paper] [code]
*Hu, L.S., *Wang, L., Hawkins-Daarud, A., …, *Swanson, K.R., *Li, J. (2021) Uncertainty Quantification in the Radiogenomics Modeling of EGFR Amplification in Glioblastoma. Scientific Reports, 11(1): 1-14. (*Contributed equally) [paper]
Wang, L., Hu, Q., & Liu, J. (2016) Software Reliability Growth Modeling and Analysis with Dual Fault Detection and Correction Processes. IIE transactions, 48(4): 359-370. [paper]
Wang, L., Hu, Q., & Xie, M. (2015). Bayesian Analysis for Software Reliability with Fault Detection and Correction Data. In 2015 IEEE 21st Pacific Rim International Symposium on Dependable Computing (PRDC), 311-312. [paper]
Wang, L., Hu, Q. P., & Xie, M. (2015). Bayesian analysis for NHPP-based software fault detection and correction processes. In 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 1046-1050. [paper]
Mao, L.,Wang, L., Hu, L., Hawkins-Daarud, A., Swanson, K., Li, J., Weakly-Supervised Transfer Learning with Application in Precision Medicine. IEEE Transactions on Automation Science and Engineering. (Under review)
Chong, L., Wang, L., Wang, K., Traub, S., Li, J. (2019) Homotopic Region Connectivity During Concussion Recovery: A Longitudinal fMRI Study. PloS One, 14(10): e0221892. [paper] [code]
Wang, K., Patel, B.K., Wang, L., Wu, T., Li, J. (2019) A Dual-mode Deep Transfer Learning (D2TL) System for Breast Cancer Detection using Contrast Enhanced Digital Mammograms. IISE Transactions on Healthcare Systems Engineering , 9(4): 357-370. [paper]
Full list of publications in Google Scholar.
Patent
Hawkins-Daarud, A., Hu, L.S., Swanson, K.R., Wu, T., Li, J., Wang, L. (2022) Systems and Methods for Uncertainty Quantification in Radiogenomics. U.S. Patent Application, No. 17/524, 591.
HONORS & AWARDS
Best Student Paper Award, Data Analytics and Information Systems Division, IISE Annual Conference, 2022.
George Fellowship, H. Milton Stewart School of Industrial and Systems Engineering (ISyE), Georgia Institute of Technology, 2021.
NSF Scholarship, Quality and Productivity Research Conference (QPRC), 2021.
Runner up for Best Poster Competition, QPRC, 2021
Robert D. Zimmerman Award, Best Scientific Poster, Eastern Neuroradiological Society Annual Meeting, 2021.
Best Student Paper Award, Data Analytics and Information Systems Division, IISE Annual Conference, 2020.
Finalist for Best Paper Award, 15th Data Mining and Decision Analytics (DMDA) Workshop, INFORMS Annual Conference, 2020.
All-round Excellent Student, Chinese Academy of Sciences, 2015.
President's Fellowship, Chinese Academy of Sciences, 2015.