Zining Zhu (朱子宁)

I am a PhD candidate at University of Toronto. I am interested in understanding the mechanisms and abilities of neural NLP systems. In the long term, I try to build reliable, interpretable an trustworthy NLP systems. These include developing methods to communicate complex decisions to humans, and empowering real-world applications with the advance of the models.
Following are some topics I am working on:

• Interpretability: develop methods that reveal the mechanisms of complex neural network systems. I work on solidifying probing methods [C8, C4] and exploring the utility of probing analyses [C9, C7, C5].
• Trustworthiness: control the models along multiple linguistic levels. The goal is to allow the models to make the correct decisions with the available knowledge in diverse situations.
• Datasets: understand the datasets that ML and NLP systems are trained on [I4].
• Applications: using ML and NLP methods to address some real-world problems in the healthcare and society contexts [C6, C3].

The publication page contains a complete list of my publications. I happily collaborate with many researchers on the above topics. Please click here if you want to chat about collaboration or related ideas.