Lijie Hu

Biography

Lijie Hu is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST), with a Master’s degree in Mathematics from Renmin University of China. Her research focuses on responsible AI, particularly in explainable AI (XAI) and privacy-preserving machine learning. Lijie’s recent research emphasizes making XAI more accessible and practical. Her work centers on developing Usable XAI-as-a-Service systems (Usable XAI) and Useful Explainable AI toolkits (Useful XAI), bridging the gap between theoretical innovation and real-world application. Her research was recognized as “Best of PODS 2022”. She has received several prestigious honors, including the KAUST Dean’s List Award in 2022, 2024, and 2025, and was recognized as a Top Reviewer at AISTATS 2023. Beyond her research, Lijie actively contributes to the academic community as a member of the AAAI Student Committee.

Education

Bachelor of Science (B.S.)
Mathematics, Minzu University of China, China, 2018
Master of Science (M.S.)
Mathematics, Renmin University of China, China, 2020

Selected Publications

  • Lijie Hu, Shuo Ni, Hanshen Xiao and Di Wang. "High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data". https://arxiv.org/abs/2107.11136. 
    To appear in the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2022).
  • Di Wang*, Jiahao Ding*, Lijie Hu, Zejun Xie, Miao Pan and Jinhui Xu. "Differentially Private Expectation Maximization  Algorithm with Statistical Guarantees". https://arxiv.org/abs/2010.13520v2. (* equal contributions)
  • Di Wang*, Lijie Hu*, Huanyu Zhang*, Marco Gaboardi and Jinhui Xu. "Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data". https://arxiv.org/abs/1910.00482v3. (* equal contributions)
  • Lijie Hu, Jinwu Gao, Giuseppe Fenza, Yanghe Feng, and Carmen De Maio. "Uncertain inference network in evidential reasoning." Evolutionary Intelligence (2020): 1-16.

Related People

Academic Advisors

Di Wang

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