Education
- PhD (2023) Rutgers University (Information Systems)
- MS (2018) University of Chinese Academy Sciences (Computer Science)
Research
Data Science, LLM, Business Analytics
Institutional Service
- Stevens Institute for Artificial Intelligence Member
Professional Service
- Nature Communication Reviewer
- MIS Quarterly Reviewer
- ACM Conference on Knowledge Discovery and Data Mining (SIGKDD) Program Committee
- International Conference on Information Systems (ICIS) Reviewer
- Pacific Asia Conference on Information Systems (PACIS) Reviewer
- Workshop on Information Technologies and Systems (WITS) Reviewer
- Electronic Commerce Research and Applications Reviewer
- AAAI Conference on Artificial Intelligence Program Committee
- International Conference on Information and Knowledge Management (CIKM) Program Committee
- IEEE Transactions on Knowledge and Data Engineering (TKDE) Reviewer
Honors and Awards
Best Student Paper Award at International Conference on Information Systems (ICIS) 2023
Dean’s Dissertation Fellowship at Rutgers University, 2022
AAAI-23 Student Scholar, 2023
Student Scholarship from INFORMS Workshop on Data Science, 2022
Dean’s Dissertation Fellowship at Rutgers University, 2022
AAAI-23 Student Scholar, 2023
Student Scholarship from INFORMS Workshop on Data Science, 2022
Professional Societies
- AIS – Association for Information Systems Member Member
- ACM – Association for Computing Machinery Member Member
Patents and Inventions
Zhang, Denghui, et al. "Semi-supervised deep model for turbulence forecasting." U.S. Patent No. 11,650,351. 16 May 2023.
Zhang, Denghui, et al. "Multi-scale multi-granularity spatial-temporal traffic volume prediction." U.S. Patent Application No. 17/003,112.
Zhang, Denghui, et al. "Multi-scale multi-granularity spatial-temporal traffic volume prediction." U.S. Patent Application No. 17/003,112.
Selected Publications
1. Wang, D., Wu, L., Zhang, D., Zhou, J., Sun, L., & Fu, Y. (2023, June). Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 4, pp. 4660-4667).
2. Zhang, D., Liu, Y., Yuan, Z., Fu, Y., Chen, H., & Xiong, H. (2022). Multi-faceted knowledge-driven pre-training for product representation learning. IEEE Transactions on Knowledge and Data Engineering.
3. Qiao, Z., Fu, Y., Wang, P., Xiao, M., Ning, Z., Zhang, D., ... & Zhou, Y. (2022). RPT: toward transferable model on heterogeneous researcher data via pre-training. IEEE Transactions on Big Data, 9(1), 186-199.
4. Li, Y., Chen, Z., Zha, D., Du, M., Ni, J., Zhang, D., ... & Hu, X. (2022, August). Towards learning disentangled representations for time series. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3270-3278).
5. Zhang, D., Yuan, Z., Liu, H., & Xiong, H. (2022, June). Learning to walk with dual agents for knowledge graph reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 5, pp. 5932-5941).
6. Zhang, D., Yuan, Z., Liu, Y., Liu, H., Zhuang, F., Xiong, H., & Chen, H. (2021, August). Domain-oriented language modeling with adaptive hybrid masking and optimal transport alignment. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 2145-2153).
7. Yuan, Z., Liu, H., Hu, R., Zhang, D., & Xiong, H. (2021, May). Self-supervised prototype representation learning for event-based corporate profiling. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4644-4652).
8. Zhang, D., Liu, Y., Cheng, W., Zong, B., Ni, J., Chen, Z., ... & Xiong, H. (2020, November). T^ 2-Net: A Semi-Supervised Deep Model for Turbulence Forecasting. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 1388-1393). IEEE.
9. Yuan, Z., Liu, H., Liu, Y., Zhang, D., Yi, F., Zhu, N., & Xiong, H. (2020, July). Spatio-temporal dual graph attention network for query-poi matching. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 629-638).
10. Zhang, D., Liu, J., Zhu, H., Liu, Y., Wang, L., Wang, P., & Xiong, H. (2019, November). Job2Vec: Job title benchmarking with collective multi-view representation learning. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2763-2771).
11. Li, M., Wang, Y., Zhang, D., Jia, Y., & Cheng, X. (2018). Link prediction in knowledge graphs: A hierarchy-constrained approach. IEEE Transactions on Big Data, 8(3), 630-643.
12. Zhang, D., Li, M., Cai, P., Jia, Y., & Wang, Y. (2018, April). Path-based attention neural model for fine-grained entity typing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
13. Zhang, D., Li, M., Jia, Y., Wang, Y., & Cheng, X. (2017, August). Efficient parallel translating embedding for knowledge graphs. In Proceedings of the International Conference on Web Intelligence (pp. 460-468).
2. Zhang, D., Liu, Y., Yuan, Z., Fu, Y., Chen, H., & Xiong, H. (2022). Multi-faceted knowledge-driven pre-training for product representation learning. IEEE Transactions on Knowledge and Data Engineering.
3. Qiao, Z., Fu, Y., Wang, P., Xiao, M., Ning, Z., Zhang, D., ... & Zhou, Y. (2022). RPT: toward transferable model on heterogeneous researcher data via pre-training. IEEE Transactions on Big Data, 9(1), 186-199.
4. Li, Y., Chen, Z., Zha, D., Du, M., Ni, J., Zhang, D., ... & Hu, X. (2022, August). Towards learning disentangled representations for time series. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3270-3278).
5. Zhang, D., Yuan, Z., Liu, H., & Xiong, H. (2022, June). Learning to walk with dual agents for knowledge graph reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 5, pp. 5932-5941).
6. Zhang, D., Yuan, Z., Liu, Y., Liu, H., Zhuang, F., Xiong, H., & Chen, H. (2021, August). Domain-oriented language modeling with adaptive hybrid masking and optimal transport alignment. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 2145-2153).
7. Yuan, Z., Liu, H., Hu, R., Zhang, D., & Xiong, H. (2021, May). Self-supervised prototype representation learning for event-based corporate profiling. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4644-4652).
8. Zhang, D., Liu, Y., Cheng, W., Zong, B., Ni, J., Chen, Z., ... & Xiong, H. (2020, November). T^ 2-Net: A Semi-Supervised Deep Model for Turbulence Forecasting. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 1388-1393). IEEE.
9. Yuan, Z., Liu, H., Liu, Y., Zhang, D., Yi, F., Zhu, N., & Xiong, H. (2020, July). Spatio-temporal dual graph attention network for query-poi matching. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 629-638).
10. Zhang, D., Liu, J., Zhu, H., Liu, Y., Wang, L., Wang, P., & Xiong, H. (2019, November). Job2Vec: Job title benchmarking with collective multi-view representation learning. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2763-2771).
11. Li, M., Wang, Y., Zhang, D., Jia, Y., & Cheng, X. (2018). Link prediction in knowledge graphs: A hierarchy-constrained approach. IEEE Transactions on Big Data, 8(3), 630-643.
12. Zhang, D., Li, M., Cai, P., Jia, Y., & Wang, Y. (2018, April). Path-based attention neural model for fine-grained entity typing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
13. Zhang, D., Li, M., Jia, Y., Wang, Y., & Cheng, X. (2017, August). Efficient parallel translating embedding for knowledge graphs. In Proceedings of the International Conference on Web Intelligence (pp. 460-468).