Zining Zhu (zzhu41)

Zining Zhu

Assistant Professor

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

Department of Computer Science

(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 Stevens Institute of Technology. I received Ph.D. degree at the University of Toronto and Vector Institute, advised by Frank Rudzicz. I direct the Explainable and Controllable AI lab. I'm also affiliated with the Stevens Institute for Artificial Intelligence. I am interested in understanding the mechanisms and abilities of neural network AI systems, and incorporating the findings into controlling the AI systems. In the long term, I look forward to empowering real-world applications with safe and trustworthy AIs that can collaborate with humans. I have served as an Area Chair of NeurIPS in 2024 and an Action Editor for ACL Rolling Review in 2024.

Professional Service

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

Honors and Awards

- Top Reviewer Award, NeurIPS 2023

Professional Societies

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

Selected Publications

Conference Proceeding

  1. 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/.
  2. 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.
  3. 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/.
  4. 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)