Zhijian Zhou

Hi, there! I am a second-year PhD candidate at TMLR Group in the Faculty of Engineering and Information Technology, the University of Melbourne, advised by Dr. Feng Liu and Dr. Liuhua Peng. I received my Master’s degree from the School of Artificial Intelligence at Nanjing University in 2024, where I was supervised by Dr. Wei Gao and was a member of the LAMDA Group, led by Professor Zhihua Zhou. Prior to that, I received my B.Eng. degree from Dalian University of Technology in 2021.

My research interests lie at the intersection of statistics and trustworthy AI. Generally, I am interested in two research directions: data-adaptive statistical hypothesis testing, which provides a foundation for theoretical guarantees, and statistically rigorous methods for evaluation and trustworthiness assessment in modern AI systems, which bring these ideas into practice. Specifically, my current research focuses on:

LLM evaluation with fewer labeled samples while maintaining finite-sample validity and provable error control.
End-to-end statistical hypothesis testing frameworks that derive both test statistics and testing procedures from data, while circumventing the need to fit complex AI tasks into restrictive classical hypothesis testing formulations.

As AI systems are increasingly used in real-world settings, building trustworthy AI is both important and challenging. Traditional machine learning theory provides important foundations, but many of its classical paradigms rely on simplified assumptions that do not fully match how modern AI systems are used in practice. This motivates my interest in data-driven statistical methods, which rely directly on observed data rather than simplified assumptions about how AI systems behave, and therefore can provide more realistic and reliable guarantees for evaluating and trusting AI systems.

Selected Publications

DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing

Z.-J. Zhou, X.-Y. Tian, L.-H. Peng, C. Lei, A. Schrab, D. J. Sutherland, and F. Liu

In Advances in Neural Information Processing Systems 38, 2025

Bib PDF Code

@incollection{Zhou:Tian:Peng:Lei:Schrab:Sutherland:Liu2025,
  author = {Zhou, Z.-J. and Tian, X.-Y. and Peng, L.-H. and Lei, C. and Schrab, A. and Sutherland, D.~J. and Liu, F.},
  title = {{DUAL:} Learning Diverse Kernels for Aggregated Two-sample and Independence
                    Testing},
  booktitle = {Advances in Neural Information Processing Systems 38},
  publisher = {Curran Associates},
  address = {Dutchess, NY},
  year = {2025}
}

Anchor-based Maximum Discrepancy for Relative Similarity Testing

Z.-J. Zhou, L.-H. Peng, X.-Y. Tian, and F. Liu

In Advances in Neural Information Processing Systems 38, 2025

Bib PDF Code

@incollection{Zhou:Peng:Tian:Liu2025,
  author = {Zhou, Z.-J. and Peng, L.-H. and Tian, X.-Y. and Liu, F.},
  title = {Anchor-based Maximum Discrepancy for Relative Similarity Testing},
  booktitle = {Advances in Neural Information Processing Systems 38},
  publisher = {Curran Associates},
  address = {Dutchess, NY},
  year = {2025}
}

On the Exploration of Local Significant Differences for Two-Sample Test

Z.-J. Zhou, J. Ni, J.-H. Yao, and W. Gao

In Advances in Neural Information Processing Systems 36, 2023

Bib PDF Code

@incollection{Zhou:Ni:Yao:Gao2023,
  author = {Zhou, Z.-J. and Ni, J. and Yao, J.-H. and Gao, W.},
  title = {On the Exploration of Local Significant Differences for Two-Sample Test},
  booktitle = {Advances in Neural Information Processing Systems 36},
  publisher = {Curran Associates},
  address = {Dutchess, NY},
  year = {2023}
}