Education
- PhD (2023) Rutgers University (Business Administration)
- MS (2018) University of Chinese Academy Sciences (Computer Science)
Research
Business analytics, business intelligence
Professional Service
- 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
Dean’s Dissertation Fellowship at Rutgers University, 2022
AAAI-23 Student Scholar, 2023
Student Scholarship from INFORMS Workshop on Data Science, 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).