Welcome

Zining Zhu (朱子宁)
Assistant Professor, Department of Computer Science
Office: Gateway North 414
Email: [email protected]
Book a meeting with me

Bio
Zining is 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. He directs the Explainable and Controllable AI lab. He is affiliated with the Stevens Institute for Artificial Intelligence (SIAI) and the Center for Research Toward Advancing Financial Technologies (CRAFT). Prior to joining Stevens, Zining received Ph.D. degree at the University of Toronto and Vector Institute, advised by Dr. Frank Rudzicz. His research is in Natural Language Processing and Explainable AI including understanding the mechanisms and abilities of AIs, and incorporating the findings into controlling the AIs. Zining looks forward to building safe, trustworthy and efficient agentic AIs that can assist humans discover knowledge and better perform high-stake tasks. Zining has received paper award at NAACL. He has served as an Area Chair for NeurIPS, ICML, and an Action Editor for ACL Rolling Review.

News

  • 2026/03: We receive an NVIDIA Academic Grant Program award. Thanks Nvidia!
  • 2026/02: Our project Scholera wins an New Jersey PlugNPlay AI Innovation Challenge award, and is spinning towards a startup. Congrats Kevin!
  • 2025/06: We receive an NSF NAIRR Pilot award for our AI4Research project.
  • 2025/05: We receive an NSF NAIRR Pilot award for our Explainable AI Acceleration project.
  • 2025/05: We receive a Google Cloud Research Credits award. Thanks Google!
  • 2025/04: Our paper "ACCORD: Closing the Commonsense Measurability Gap" receives an Outstanding Paper Award at NAACL.
  • 2025/01: We receive a Stevens Teaching Learning Center award for the AI4Education project.
  • 2024/10: I will teach a new seminar course, Topics in Explainable Natural Language Processing, in the 2025 Spring semester.
  • 2024/06: We present a tutorial at NAACL: Explanation in the Era of Large Language Models.
  • 2024/02: We organize a workshop at AAAI: Multimodal Machine Learning for Mental Healthcare.
  • 2023/12: I receive a Top Reviewer Award at NeurIPS 2023.