I am currently a software engineer at Google Research, working on VLM and multimodal classification.

I obtained my Ph.D. in Computing Science from Simon Fraser University in Sept 2021, advised by Professor Jian Pei. During my Ph.D. study, my research focuses on the understanding of machine learning and deep learning, with an emphasis on the interpretability and model complexity of deep architectures.

Before that, I worked as a software engineer at Sogou and Baidu, focused on distributed cloud platform and Hadoop, Spark. I obtained my B.E. degree in Computer Science from the University of Science and Technology of China in 2013.

Please visit here for my full CV.

Xia Hu, Lingyang Chu, Jian Pei, Weiqing Liu, Jiang Bian. "Model Complexity of Deep Learning: A Survey." Knowledge and Information Systems (KAIS), 2021.
[arxiv]

Xia Hu, Weiqing Liu, Jiang Bian, and Jian Pei. "Measuring Model Complexity of Neural Networks with Curve Activation Functions." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1521-1531. 2020.
[paper] [arxiv] [video]

Zicun Cong, Lingyang Chu, Lanjun Wang, Xia Hu, and Jian Pei. "Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution." In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 613-624. IEEE, 2020.
[paper] [arxiv]

Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang, and Jian Pei. "Exact and consistent interpretation for piecewise linear neural networks: A closed form solution." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1244-1253. 2018.
[paper] [arxiv]

Xia Hu, Lingyang Chu, Jian Pei, Jiang Bian, Weiqing Liu. "Deep Learning Model Complexity: Concepts and Approaches." At the SIAM International Conference on Data Mining (SDM21). Virtual conference, April 29 - May 1, 2021.
[tutorial webpage]