Zining Zhu (zzhu41)

Zining Zhu

Assistant Professor

Charles V. Schaefer, Jr. School of Engineering and Science

Gateway Center N414
(201) 216-3573
Website

Education

  • PhD (2024) University of Toronto (Computer Science)
  • BS (2019) University of Toronto (Engineering Science, Robotics)

Research

I direct the Explainable and Controllable AI Lab, where we research the foundations and application of approaches that make AI explainable and controllable. The areas of research include:
- Model interpretability
- Natural language explanation
- Model intervention
- Societal implications and safe deployments

General Information

I am an Assistant Professor at the Department of Computer Science at the Charles V. Schaefer Jr. School of Engineering and Science at the Stevens Institute of Technology. I direct the Explainable and Controllable AI lab. I’m also affiliated with the Stevens Institute for Artificial Intelligence (SIAI) and the Center for Research Toward Advancing Financial Technologies (CRAFT). Prior to joining Stevens, I received Ph.D. degree at the University of Toronto and Vector Institute, advised by Dr. Frank Rudzicz. I am broadly interested in Natural Language Processing and Explainable AI. My research involves understanding the mechanisms and abilities of AIs, explaining them in natural languages, and incorporating the findings into controlling the AIs. I look forward to building safe, trustworthy agentic AIs that can assist humans discover knowledge and better perform high-stake tasks. My research has received paper award at NAACL. I have served as an Area Chair or an Action Editor for NeurIPS, ICML, ACL Rolling Review, EMNLP and NAACL.

Professional Service

  • NeurIPS Area Chair
  • NSF Review Panelist
  • ICML Area Chair
  • ACL Rolling Review Action Editor
  • NeurIPS Area Chair
  • COLM Reviewer
  • ICLR Reviewer

Professional Societies

  • AAAI – Association for the Advancement of Artificial Intelligence Member
  • ACL – Association for Computational Linguistics Member

Selected Publications

Conference Proceeding

  1. Roewer-Després, F.; Feng, J.; Zhu, Z.; Rudzicz, F. (2025). ACCORD: Closing the Commonsense Measurability Gap. NAACL. Association for Computational LInguistics.
  2. Zhu, Z.; Chen, H.; Ye, X.; Lyu, Q.; Tan, C.; Marasovic, A.; Wiegreffe, S. (2024). Tutorial: Explanation in the Era of Large Language Models. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (vol. Volume 5: Tutorial Abstracts, pp. 19-25). Mexico City: Association for Computational Linguistics.
    https://aclanthology.org/2024.naacl-tutorials.3/.
  3. Niu, J.; Liu, A.; Zhu, Z.; Penn, G. (2024). What does the Knowledge Neuron Thesis Have to do with Knowledge?. ICLR.
    https://arxiv.org/abs/2405.02421.
  4. Sahak, E.; Zhu, Z.; Rudzicz, F. (2023). A State-Vector Framework For Dataset Effects (vol. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15231-15245). Singapore: EMNLP.
    https://aclanthology.org/2023.emnlp-main.942/.
  5. Zhu, Z.; Shahtalebi, S.; Rudzicz, F. (2022). Predicting fine-tuning performance with probing. EMNLP. Association for Computational Linguistics.
    https://aclanthology.org/2022.emnlp-main.793.

Courses

CS 584 Natural Language Processing (2024 fall)
CS 810 Explainable Natural Language Processing (2025 spring)