Zining Zhu (朱子宁)


I will join the Stevens Institute of Technology as an Assistant Professor in January 2024. I am currently a PhD candidate at the University of Toronto and Vector Institute advised by Frank Rudzicz. 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.
Openings: I'm looking for multiple PhD/master/internship students to work on explainable and safe NLP and AI systems. If you are interested in working on these directions, please send me an email.
Following are some topics I am working on:
  • Interpretation: Develop methods to probe deep neural networks -- unveiling what happens among the structures and the neurons, and query the sources of their strong capabilities [W4, C5]. Then, we can incorporate the learned insights into developing better models [C9]. We should also make these probing methods solid -- reliable and valid [C8, C4].
  • Explanation: AIs can be more helpful if they can explain to humans, and they have promising potential. Along this topic, we solidify the problem setting of "machine explanation": How to evaluate the explanations? How can we make explanations faithful and useful? We try to leverage the reasoning abilities and the stored commonsense of large language models and facilitate "machine teaching", to diverse audiences.
  • Safety, control, alignment: To harness the powerful abilities of AIs, we need better approaches to control their behavior. AIs make errors, hallucinate, and demonstrate biases. We try to quantify these improvement area, and develop methods to address them.
  • Applications: Use AI tools to address societal challenges, for example, healthcare and fairness, ideally following a human-AI collaboration paradigm. [C6, C3].

The publication page contains a complete list of my publications.


University of Toronto 2019 - present
Ph.D. in Computer Science
Advisor: Frank Rudzicz
University of Toronto 2014 - 2019
Bachelor in Engineering Science, Robotics option.

Employment history

Amazon, Applied Scientist Intern, 2022
Search - Query Understanding. Advisors: Haoming Jiang, Jingfeng Yang, Sreyashi Nag, and Chao Zhang
Tencent Jarvis Lab, Machine Learning Engineering Intern, 2019
Neural language models and pre-training techniques. Advisor: Ruihui Zhao.
Winterlight Labs, Research Software Engineer, 2017 - 2018
Automatic detection of dementia from narrative speeches. Advisor: Jekaterina Novikova.
TripAdvisor, Software Engineering Intern, 2017
Android applications and Java API.
Dynamic Systems Lab at UTIAS, Research Assistant, 2016
Enhancing drone controllers using deep neural networks. Advisor: Angela Schoellig.