Qing Li

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Publications

Peer-Reviewed Journals (Students in Bold, Corresponding Author *)

1.  Noh, J. M., Liu, L. J. ⁺, Tekeste, M., Li, Q., Hatfield J., Eisenmann D., “Digitized Soil SeedBed Soil Tilth Quality Comparison from Worn and Edge hardened Cultivator Sweeps” (First two authors contributed equally), Sensors (IF 3.9), 24(21), 6951 (2024). https://doi.org/10.3390/s24216951
2.  Chen, J. D., Li, Q., Liu, F., Wen, Y. X, “M3T-LM: Multi-modal multi-task learning model for jointly predicting patient length of stay and mortality”, Computers in Biology and Medicine (IF 7.7), 183:109237 (2024). https://doi.org/10.1016/j.compbiomed.2024.109237
3. Wang, S. D. +, Jiang, Y. Q. +, Li, Q., and Zhang, W. L. (2024). “Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data”, IEEE Journal of Biomedical and Health Informatics (IF 7.7, Q1), in press (one of the top 3 out of 47 Health Information Management Journals according to H-index). https://doi.org/10.1109/JBHI.2024.3416039
4. Liu, L. J. +, Li, B. W., Qin, H. T., and Li, Q.* (2024). “Uncertainty quantification utilizing similarity evaluation between 3D surface topography measurements”, Special Issue: Advances in Data Analytics for Manufacturing Quality Assurance, Mathematics (IF 2.592, Q1), 12(5):669.  https://doi.org/10.3390/math12050669
5. Safaei, N., Seyedekrami, S., Talafidaryani, M., Masoud, A., Wang, S. D., Moqri, M., Li, Q., and Zhang, W. L. (2022). E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database, PLOS ONE, 17(5): e0262895.
6. Li, Q.*, Liu, L. J., Li, T. Q., and Yao, K. H. (2021). Bayesian change-points detection assuming power-law process in the recurrent-event context, Communications in Statistics Part B: Simulation and Computation, 1–23
7. Jiang, Y. Q., Wang, S. D., Qin, H. T., Li, B. W., and Li, Q.*. Similarity quantification of 3D surface topography measurements via Fourier transform, Measurement, 110207. 
8. Wang, S.D., Zhang, X., Zheng, Y., Li, B.W., Qin, H.T., and Li, Q.* (2019). Similarity evaluation of 3D surface topography measurements, Measurement Science and Technology, 32:125003.
9. Jiang, Y. Q., Li, Q.*, Trevisan, G, Linhares, D., and MacKenzie, C. (2021). Investigating the relationship of porcine reproductive and respiratory syndrome virus RNA detection between adult/sow farm and wean-to-market age categories, PLOS ONE, 16:e0253429.  
10. Zhang, X., Shen, W. J., Suresh, V., Hamilton, J., Yeh, L. H., Jiang, X. P., Zhang, Z., Li, Q., Li, B. W., Rivero, I. V., and Qin, H. T. (2021). In-situ monitoring of direct energy deposition via the structured light system and its application in remanufacturing, The International Journal of Advanced Manufacturing Technology, 116: 959–974. 
11. Zheng Y., Wang, S. D., Li, Q., and Li, B. W. (2020). Fringe projection profilometry by conducting deep learning from its digital twin, Optics Express, 28(24): 36568-36583 (The first two authors contributed equally).
12. Allen, M.L., Wang, S.D., Olson L.O., and Li, Q. (2020). Miha Krofel Counting cats for conservation: seasonal estimates of leopard density and drivers of distribution in the Serengeti, Biodiversity and Conservation, 29:3591–3608. 
13. Li, Q., Guo, F., and Inyoung, K. (2020). A non-parametric Bayesian change-point detection method in the recurrent-event context, Journal of Statistical Computation and Simulation, 90:2949–2968.
14. Zhang, X., Zheng, Y., Wang, S.D., Li, Q.*, Li, B.W., and Qin, H.T. (2020).  Correlation approaches for quality assurance of additive manufactured parts based on optical metrology, Journal of Manufacturing Processes, 53:310-317. 
15. Li, Q.*, Yao, K.H., and Zhang, X.Y. (2020). A change-point detection and clustering method in the recurrent-event context, Journal of Statistical Computation and Simulation, 90 (6):1131-1149.
16. Zheng, Y., Zhang, X., Wang, S.D., Li, Q., Qin, H.T., and Li, B.W. (2020). Similarity evaluation of topography measurement results by different optical metrology technologies for additive manufactured parts, Optics and Lasers in Engineering, 126: 105920.
17. Allen, M.L., Norton, A.S., Stauffer, G., Roberts, N., Luo, Y.S., Li, Q., MacFarland, D., and Van Deelen, T.R. (2018). A Bayesian state-space model using age-at-harvest data for estimating the population of black bears (Ursus americanus) in Wisconsin, Scientific Reports, 8(1):12440.
18. Li, Q., Guo, F., Inyoung, K., Klauer, S., and Simons-Morton, B. (2018). A Bayesian finite mixture change-points model for novice teenage driving risk, Journal of Applied Statistics, 45:604-625.
19. Li, Q., Guo, F., Klauer, S., and Simons-Morton, B. (2017). Evaluation of risk change-point for novice teenage drivers, Accident Analysis & Prevention, 108:139-146.
20. Gibbons, R., Guo, F., Du, J.H., Medina, A., Terry, T., Lutkevich, P., and Li, Q. (2015). Approaches to adaptive lighting on roadways, Transportation Research Record: Journal of the Transportation Research Board, 2485:26-32.
21. Prussin, A.J., Li, Q., Malla, R., Ross, S.D., and Schmale, D.G. (2014). Monitoring the long distance transport of fusarium graminearum from field-scale sources of inoculum, Plant Disease, 98(4):504-511.
22. Guo, F., Li, Q., and Rakha, H. (2012). Multi-state travel time reliability models with skewed component distributions, Transportation Research Record: Journal of the Transportation Research Board, 2315:47-53.

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