Ph.D., Department of Industrial Engineering and Management, Peking University, 09/2015-07/2020
Joint Ph.D., Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 09/2018-11/2019
B.S., Department of Industrial Engineering, Nankai University, 09/2011-06/2015

Assistant Professor, Department of Industrial Engineering and Management, Shanghai Jiaotong University, 09/2020-Present

Data Analysis and Artificial Intelligence for the Modeling of Complex Engineering and Service Systems:
• Statistical Modeling, Prediction, and Monitoring of Complex Spatiotemporal Dynamic Systems
• Deep Learning and Multisensory Data Fusion for Process Modeling and Prognostics
• Modeling and Monitoring of Internet of Things (IoT) systems

1. National Science Foundation of China under Grant 72101148, PI, State modeling and process monitoring of manufacturing systems based on multivariate spatiotemporal dynamic flow data
2. Shanghai Sailing Program under Grant 21YF1420100, PI, Data fusion and abnormal diagnosis of complex manufacturing processes based on deep learning
3. National Science Foundation of Shanghai under Grant 22ZR1433000, PI, High-dimensional flow data modeling and monitoring of manufacturing systems based on spatiotemporal artificial intelligence
4. Shanghai Chenguang Program under Grant 21CGA12, PI, Spatiotemporal modeling and monitoring of interactive event network systems

Refereed Journals and Transactions
1. Wang, D., Wang, Y., Xian, X., and Cheng, B. (2023) “An adaptation-aware interactive learning approach for multiple operational condition-based degradation modeling,” IEEE Transactions on Neural Networks and Learning Systems, in press.
2. Wang, D., Song, C., and Zhang, X.* (2023) “Multimodal regression and mode recognition via an integrated deep neural network,” IISE Transactions, in press.
3. Wang, D.*, and Liu, K. (2023) “An integrated deep learning-based data fusion and degradation modeling method for improving prognostics,” IEEE Transactions on Automation Science and Engineering, in press, doi: 10.1109/TASE.2023.3242355.
4. Wang, D.*, Xian, X., and Song, C. (2023) “Joint learning of failure mode recognition and prognostics for degradation processes,” IEEE Transactions on Automation Science and Engineering, in press, doi: 10.1109/TASE.2023.3239004.
5. Zhao, C., Liu, F., Du, S., Wang, D., and Shao, Y. (2022) “An earth mover’s distance based multivariate generalized likelihood ratio control chart for effective monitoring of 3D point cloud surface,” Computers and Industrial Engineering, vol. 175, no. 2022, pp. 108911, 1–12.
6. Wang, D., Li, F., Liu, K., and Zhang, X. (2022) “Real-time IoT security solution leveraging an integrated learning-based approach,” ACM Transactions on Sensor Networks, in press.
7. Zan, X., Wang, D., and Xian, X. (2022) “Spatial rank-based augmentation for nonparametric online monitoring and adaptive sampling of big data streams,” Technometrics, in press.
8. Yu, G., Wang, D., Liu, J., and Zhang, X. (Apr. 2023) “Distribution-agnostic few-shot industrial fault diagnosis via adaptation-aware optimal feature transport,” IEEE Transactions on Industrial Informatics, vol. 19, no. 4, pp. 5623–5632.
9. Wang, D.*, Li, F., and Liu, K. (Mar. 2023) “Modeling and monitoring of a multivariate spatio-temporal network system,” IISE Transactions, vol. 55, no. 4, pp. 331–347.
10. Wang, D., Liu, K., and Zhang, X. (2022) “A generic indirect deep learning approach for multisensor degradation modeling,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1924–1940.
11. Wang, D., Liu, K., and Zhang, X. (2022) “A spatiotemporal prediction approach for a 3D thermal field from sensszor network,” Journal of Quality Technology, vol. 54, no. 2, pp. 215–235.
• This paper was selected as the winner of Best Student Paper Award in Data Mining Section of INFORMS Annual Meeting, 2019.
12. An, Y., Wang, D., and Zhang, X. (2020) “A novel local temperature change detection approach in a 3D thermal field,” Quality Technology and Quantitative Management, in press.
13. Wang, D., Liu, K., and Zhang, X. (2020) “Spatiotemporal multitask learning for 3-D dynamic field modeling,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 2, pp. 708–721.
• This paper was selected as the Finalist of the Best Student Paper Award in DAIS Division of IISE Annual Conference, 2019.
• This paper was selected as Honorable Mention in the International Workshop on Reliability Technology and Quality Science, 2018, China.
14. Wang, D., Liu, K., and Zhang, X. (2020) “Spatiotemporal thermal field modeling using partial differential equations with time-varying parameters,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 2, pp. 646–657.
15. Wang, D., Liu, K., and Zhang, X. (2019) “Modeling of a three-dimensional dynamic thermal field under grid-based sensor networks in grain storage,” IISE Transactions, vol. 51, no. 5, pp. 531–546.
• This paper was selected as the Winner of the Best Application Paper Award in IISE Transactions, 2020.
• This paper was selected as the Feature Article in ISE magazine, 2019.
16. Wang, D., and Zhang, X. (2019) “Dynamic field monitoring based on multitask learning in sensor networks,” Sensors, vol. 19, no. 7, 1533, pp. 1–17.
17. Wang, D., and Zhang, X. (2019) “Modeling of a 3D temperature field by integrating a physics-specific model and spatiotemporal stochastic processes,” Applied Sciences, vol. 9, no. 10, 2108, pp. 1–13.

Peer-reviewed Conference Proceedings
1. Li, H., Wang, L., Peng, Y., and Wang, D. (2023) “Kernel density estimation with efficient bandwidth selection,” Proceedings of the Winter Simulation Conference, accepted.
2. Wang, Y. and Wang, D.* (2023) “An entropy- and attention-based feature extraction and selection network for multi-target coupling scenarios,” Proceedings of IEEE International Conference of Automation Science and Engineering, accepted.
3. Wang, X. and Wang, D.* (2023) “A control chart for monitoring multivariate spatiotemporal correlated data during grain storage,” Proceedings of IEEE International Conference of Automation Science and Engineering, accepted.
4. Wang, Y. and Wang, D.* (2022) “A data fusion-based LSTM network for degradation modeling under multiple operational conditions,” Proceedings of IEEE International Conference of Automation Science and Engineering, pp. 16–21.
5. Wang, D. and Zhang, X. (2017) “Modeling grain quality characteristics via dynamic models using sensing data,” Proceedings of IEEE/SICE International Symposium on System Integration, pp. 336–341.
• This paper was selected as the Winner of the Best Paper Award in IEEE/SICE International Symposium on System Integration (SII), 2017.
6. Wang, D., and Zhang, X. (2015) “A prediction method for interior temperature of grain storage via dynamics model: a simulation study,” Proceedings of IEEE International Conference of Automation Science and Engineering, pp. 1477–1483.

Quality and Reliability Engineering, 2021, 2022, 2023
Big Data Analysis, 2021, 2022

Patents
1. Wang, D., and Zhang, X., “A transfer learning method for estimating grain temperature field during storage,” Chinese invention patent, ZL 201810042592.1, 2020.
2. Wang, D., and Zhang, X., “A three-dimensional temperature sensor data analysis method based on spatiotemporal dynamic modeling,” Chinese invention patent, ZL 201710188585.8, 2020.
3. An, Y., Wang, D., Zhang, X., and Lan, X. “A three-dimensional temperature field monitoring method based on spatiotemporal dynamic modeling,” Chinese invention patent, ZL 201910149975.3, 2020.
4. Wang, D., Cheng, B., and An, Y., “A modeling and monitoring method for IoT systems based on multivariate spatiotemporal data fusion,” Chinese invention patent, ZL 202110166192.3, 2021.
5. Wang, D., and Zhang, X., “A thermal field prediction method based on sensor data fusion,” Chinese invention patent, ZL 201811066070.1, 2023.

Software Copyright
1. Wang, D., and Zhang, X., “Platform of grain quality monitoring during storage”, Chinese software copyright, CN Software NO. 2018SR229405, 2018.
2. Teng, B. L., Wang, D., Jin, D. and Mao, Z., “Grain storage thermal management system”, Chinese software copyright, CN Software NO. 2023SR0796911, 2023.

Conference Organizing Activities
1. Organizer and chair, sessions on “Data analytics for engineering and service system improvement”, INFORMS Annual Meeting, 2019.
2. Chair, sessions on “Process analytics and optimization for system and service improvement”, IEEE/SICE International Symposium on System Integration (SII), 2017.

Academic Paper Referee
IEEE International Conference on Automation Science and Engineering (CASE), IEEE/SICE International Symposium on System Integration (SII), Winter Simulation Conference (WSC), IEEE Transactions on Automation Science and Engineering, IISE Transactions, IEEE Transactions on Reliability, IEEE Transactions on Industrial Informatics, INFORMS Journal on Data Science, International Journal of Production Research, Journal of Intelligent Manufacturing, Computers and Industrial Engineering, Journal of Industrial and Production Engineering

• Outstanding Doctoral Dissertation from the Society of Management Science and Engineering, 2022
• Shanghai Chenguang Program, 2021
• Shanghai Sailing Program, 2021
• Winner of the Best Application Paper Award in IISE Transactions, 2020.
• Winner of the Best Student Paper Award in Data Mining Section of INFORMS Annual Meeting, 2019.
• Finalist of the Best Student Paper Award in DAIS Division of IISE Annual Conference, 2019.
• Feature Article of ISE Magazine, 2019.
• Honorable Mention in International Workshop on Reliability Technology and Quality Science, China, 2018.
• First Prize in National College Innovation Competition of Industrial Engineering and Lean Management, China, 2018.
• Winner of the Best Paper Award in IEEE/SICE International Symposium on System Integration (SII), 2017.
• Outstanding Graduate Award in Beijing, Beijing Municipal Education Commission, 2020.
• National Scholarship, Ministry of Education of China, 2014, 2018, 2019.