Research Direction 1: Large Language Model for NLP-Assisted Risk-Assessment and Mitigation

This research direction has been funded by

1. National Science Foundation (NSF): Secure and Trustworthy Cyberspace (SaTC): Opinion Spam in Digital Rulemaking: Techniques, Effects, and Interventions

2. United States Department of Agriculture (USDA): Responding to Opinion Spam in US Forest Service Rulemaking

3. Department of Defense (DoD): The Internet-of-Supplier (IoS) Engine for Supply Chain Risk Management (SCRM)

Research Direction 2: Graph Convolution in Supply Chain Management and Optimization

This research direction has been funded by

1. Defense Advanced Research Projects Agency (DARPA): Novel Analytical and Visual Analytics Components for Resilient Supply-Demand Networks (RSDN)

Research Direction 3: Deep Learning-assisted Product Design and Production Planning

This research direction has been funded by

1. National Science Foundation (NSF): Manufacturing Systems Integration (MSI): Fostering Democratized Manufacturing with Artificial Intelligence (AI)-Assisted Product Design and Production Planning (AI-PDPP)

Publications

1. Kim, J.N., Andreu Perez, L., Lee, H., Hollenczer, J., Jensen, M.L., Bessarabova, E., Talbert, N. and Li, Y., 2025, Managing Opinion Spamming in Regulatory Public Engagement: AI-Enhanced Strategic Communication for Ethical and Effective e-Rulemaking, International Journal of Strategic Communication, Just Accepted

2. Huang, Z., *Dong, W., Kim, J.N., Hollenczer, J., Lee, H., Jensen, M., Bessarabova, E., Talbert, N., Zhu, R. and Li, Y., 2025. Efficient Duplicate Comment Detection for Rulemaking Agencies With Unsupervised Deep Learning: A Cost-Effective and High-Accuracy Approach, IEEE Transactions on Computational Social Systems. Just Accepted.

3. Perez, L. A., Jensen, M., Bessarabova, E., Talbert, N., Li, Y., and Zhu, R., “Public Segmentation and the Impact of Artificial Intelligence Use in E-rulemaking,” Media and Communication, Just accepted.

4. *Cui, Y., *Dong, W., Li, Y., A. E. Janitz, H. R. Pokala, and Zhu, R., 2025, Explainable Transformer-based Deep Survival Analysis in Childhood Acute Lymphoblastic Leukemia. Computers in Biology and Medicine, 191, 110-118.

5. Li, Z., Segura, L.J., Li, Y., Zhou, C. and Sun, H., 2023. Multiclass Reinforced Active Learning for Droplet Pinch-off Behaviors Identification in Inkjet Printing. Journal of Manufacturing Science and Engineering, 145 (7), 485-496

6. Li, Y., Wang, L., Chen, X. and Jin, R., 2024. Distributed Data Filtering and Modeling for Fog and Networked Manufacturing. IISE Transactions, 56 (5) 485-496.

7. *Cui, Y., Li, Y., Pan, C., Brown, S.R., Gallant, R.E. and Zhu, R., 2023. Bayesian Inference for Survival Prediction of Childhood Leukemia. Computers in Biology and Medicine, 156, 106-713.

8. *Huang, Z., Derin, Y., Kirstetter, P., Li, Y., 2022, “Multigraph Convolutional Networks for Rainfall Estimation in Complex Terrain” IEEE Geoscience and Remote Sensing Letters (GRSL), 19, 1-5

9. Li, Y., Yan, H., Jin, R., 2022 “Multi-task Learning with Latent Variation Decomposition for Multivariate Responses in a Manufacturing Network”, IEEE Transactions on Automation Science and Engineering (TASE), 20 (1), 285-295

10. Li, Y., Wang, L., Lee, D., & Jin, R. 2022. Monitoring Runtime Metrics of Fog Manufacturing via a Qualitative and Quantitative (QQ) Control Chart. ACM Transactions on Internet of Things, 3 (2), 1-19.

11. Li, Y., Deng, X., Ba, S., Myers, W.R., 2021, Brenneman, W.A., Lange, S.J., Zink, R. and Jin, R., Cluster-based Data Filtering for Manufacturing Big Data Systems. Journal of Quality Technology, 54 (3), 290-302.

12. Lan, Q., Li, Y., Robertson, J., & Jin, R. 2021, Modeling of pre-transplantation liver viability with spatial-temporal smooth variable selection. Computer Methods and Programs in Biomedicine, 208, 106264.

13. Li, Y., Sun, H., Deng, X., Zhang, C., Wang, H. P., Jin, R., 2019, “Manufacturing Quality Prediction Using Smooth Spatial Variable Selection Estimator with Applications in Aerosol Jet® Printed Electronics Manufacturing.” IISE Transactions, 52 (3), 321-333.

14. Li, Y., Jin, R., Yuan L., 2018, “Classifying Relations in Clinical Narratives using Segment Graph Convolutional and Recurrent Neural Networks (Seg-GCRNs)” Journal of the American Medical Informatics Association 26 (3), 262-268.

15. Li, Y., Mohan, K., Sun, H., Jin, R., 2017, “Ensemble Modeling of In Situ Features for Printed Electronics Manufacturing With In Situ Process Control Potential”, IEEE Robotics and Automation Letters 2 (4), 1864-1870.

16. Sun, H., Wang, K., Li, Y., Zhang, C., Jin, R., 2017, “Quality Modeling of Printed Electronics in Aerosol Jet Printing Based on Microscopic Images”, ASME Transactions Journal of Manufacturing Science and Engineering 139 (7), 071-082.