Denghui Zhang
Assistant Professor
School of Business
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
- International Conference on Learning Representations Area Chair
- Conference on Empirical Methods in Natural Language Processing (EMNLP) Area Chair
- INFORMS Journal on Computing Reviewer
- 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
NSF NAIRR Award
OpenAI Researcher Access Program
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
OpenAI Researcher Access Program
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
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. Pan, Y., Chen, J., Chen, J., Xu, Z., & Zhang, D. (2025, July). Iterative online–offline joint optimization is needed to manage complex LLM copyright risks. In Proceedings of the 42nd International Conference on Machine Learning (ICML 2025).
2. Wang, R., Zhao, W., Li, S., & Zhang, D. (2025, February). AuditBench: Benchmarking large language models in financial statement auditing. In AI for Research and Scalable, Efficient Systems: AI4Research 2025 and SEAS 2025, Proceedings (Vol. 2533, p. 59). Springer Nature.
3. Zhang, D., Xu, Z., & Zhao, W. (2025, June). LLMs and copyright risks: Benchmarks and mitigation approaches. In Proceedings of the 2025 Annual Conference of NAACL: Human Language Technologies, Volume 5 (Tutorial) (pp. 44–50).
4. Atkinson, D., Chen, X., Gao, J., Lin, H., Liu, X., Wang, Q., Wei, B., Xu, Z., & Zhang, D. (2025). NAACL tutorial – LLMs and copyright risks. Retrieved from https://zhangdenghui.site/naacl-copyright-tutorial-web/.
5. Pan, Y., Lin, H., Ran, Y., Chen, J., Yu, X., Zhao, W., Zhang, D., & Xu, Z. (2025, June). ALinFiK: Learning to approximate linearized future influence kernel for scalable third-party LLM data valuation. In Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2025) (Main).
6. Qian, C., Han, P., Luo, Q., He, B., Chen, X., Zhang, Y., Du, H., Yao, J., Yang, X., Zhang, D., & others. (2025, July). EscapeBench: Towards advancing creative intelligence of language model agents. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) (Main).
7. Sun, C., Zhang, D., Zhai, C., & Ji, H. (2025, July). Beyond reactive safety: Risk-aware LLM alignment via long-horizon simulation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025).
8. Han, P., Qian, C., Chen, X., Zhang, Y., Zhang, D., & Ji, H. (2025, November). SafeSwitch: Steering unsafe LLM behavior via internal activation signals. In Findings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025).
9. Wu, Y., Xie, J., Zhang, D., & Xu, Z. (2025, November). DEL-ToM: Inference-time scaling for theory-of-mind reasoning via dynamic epistemic logic. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025) (Main).
10. Wang, R., Liu, J., Qian, C., Shen, Y., Pan, Y., Xu, Z., Abbasi, A., Ji, H., & Zhang, D. (2025, November). Rescorla–Wagner steering of LLMs for undesired behaviors over disproportionate inappropriate context. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025) (Main).
11. Long, J., Liu, M., Chen, X., Xu, J., Li, S., Xu, Z., & Zhang, D. (2025, November). Profiling LLM’s copyright infringement risks under adversarial persuasive prompting. In Findings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025).
12. Zhang, G., Su, Q., Liu, J., Qian, C., Pan, Y., Fu, Y., & Zhang, D. (2025, November). ISACL: Internal state analyzer for copyrighted training data leakage. In Findings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025).
13. Wu, Y., Guo, W., Liu, Z., Ji, H., Xu, Z., & Zhang, D. (2025). How large language models encode theory-of-mind: A study on sparse parameter patterns. npj Artificial Intelligence.
14. Zhao, W., Shao, H., Xu, Z., Duan, S., & Zhang, D. (2024, October). Measuring copyright risks of large language models via partial information probing. In Proceedings of the CIKM Workshop on Data-centric AI.
15. Xu, J., Li, S., Xu, Z., & Zhang, D. (2024, November). Do LLMs know to respect copyright notice? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) (Main).
16. 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).
17. 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.
18. 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.
19. 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).
20. 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).
21. 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).
22. 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).
23. Zhang, D., Liu, Y., Cheng, W., Zong, B., Ni, J., Chen, Z., … & Xiong, H. (2020, November). T²-Net: A semi-supervised deep model for turbulence forecasting. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 1388–1393). IEEE.
24. 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).
25. 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).
26. 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.
27. 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).
28. 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. Wang, R., Zhao, W., Li, S., & Zhang, D. (2025, February). AuditBench: Benchmarking large language models in financial statement auditing. In AI for Research and Scalable, Efficient Systems: AI4Research 2025 and SEAS 2025, Proceedings (Vol. 2533, p. 59). Springer Nature.
3. Zhang, D., Xu, Z., & Zhao, W. (2025, June). LLMs and copyright risks: Benchmarks and mitigation approaches. In Proceedings of the 2025 Annual Conference of NAACL: Human Language Technologies, Volume 5 (Tutorial) (pp. 44–50).
4. Atkinson, D., Chen, X., Gao, J., Lin, H., Liu, X., Wang, Q., Wei, B., Xu, Z., & Zhang, D. (2025). NAACL tutorial – LLMs and copyright risks. Retrieved from https://zhangdenghui.site/naacl-copyright-tutorial-web/.
5. Pan, Y., Lin, H., Ran, Y., Chen, J., Yu, X., Zhao, W., Zhang, D., & Xu, Z. (2025, June). ALinFiK: Learning to approximate linearized future influence kernel for scalable third-party LLM data valuation. In Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2025) (Main).
6. Qian, C., Han, P., Luo, Q., He, B., Chen, X., Zhang, Y., Du, H., Yao, J., Yang, X., Zhang, D., & others. (2025, July). EscapeBench: Towards advancing creative intelligence of language model agents. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) (Main).
7. Sun, C., Zhang, D., Zhai, C., & Ji, H. (2025, July). Beyond reactive safety: Risk-aware LLM alignment via long-horizon simulation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025).
8. Han, P., Qian, C., Chen, X., Zhang, Y., Zhang, D., & Ji, H. (2025, November). SafeSwitch: Steering unsafe LLM behavior via internal activation signals. In Findings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025).
9. Wu, Y., Xie, J., Zhang, D., & Xu, Z. (2025, November). DEL-ToM: Inference-time scaling for theory-of-mind reasoning via dynamic epistemic logic. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025) (Main).
10. Wang, R., Liu, J., Qian, C., Shen, Y., Pan, Y., Xu, Z., Abbasi, A., Ji, H., & Zhang, D. (2025, November). Rescorla–Wagner steering of LLMs for undesired behaviors over disproportionate inappropriate context. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025) (Main).
11. Long, J., Liu, M., Chen, X., Xu, J., Li, S., Xu, Z., & Zhang, D. (2025, November). Profiling LLM’s copyright infringement risks under adversarial persuasive prompting. In Findings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025).
12. Zhang, G., Su, Q., Liu, J., Qian, C., Pan, Y., Fu, Y., & Zhang, D. (2025, November). ISACL: Internal state analyzer for copyrighted training data leakage. In Findings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025).
13. Wu, Y., Guo, W., Liu, Z., Ji, H., Xu, Z., & Zhang, D. (2025). How large language models encode theory-of-mind: A study on sparse parameter patterns. npj Artificial Intelligence.
14. Zhao, W., Shao, H., Xu, Z., Duan, S., & Zhang, D. (2024, October). Measuring copyright risks of large language models via partial information probing. In Proceedings of the CIKM Workshop on Data-centric AI.
15. Xu, J., Li, S., Xu, Z., & Zhang, D. (2024, November). Do LLMs know to respect copyright notice? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) (Main).
16. 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).
17. 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.
18. 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.
19. 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).
20. 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).
21. 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).
22. 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).
23. Zhang, D., Liu, Y., Cheng, W., Zong, B., Ni, J., Chen, Z., … & Xiong, H. (2020, November). T²-Net: A semi-supervised deep model for turbulence forecasting. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 1388–1393). IEEE.
24. 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).
25. 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).
26. 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.
27. 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).
28. 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).
Courses
BIA 678 Big Data Technology
BT466 Data Analytics
BT466 Data Analytics