Iā€™m a PhD student of Computer Science Department at UCLA, advised by Cho-Jui Hsieh. Prior to that, I received my B.S. degree at School of Physics, Peking University in 2016 (thesis advisor: Qite Li). Before graduation, I was a research intern studying natural language processing, advised by Yansong Feng. Currently my reseach interests are optimization problems in machine learning, robust neural networks and generative modeling, specifically:

  • Convex/non-convex optimization algorithms for efficient machine learning
  • Robust neural networks
  • Efficient learning of generative models

Curriculum vitae


  • Fall 2018 ā€” Now, Ph.D. student, Department of Computer Science, UCLA
  • Fall 2016 ā€” Spring 2018 (transferred out), Ph.D. student, Department of Computer Science, UC Davis
  • Fall 2011 ā€” Spring 2016, School of Physics, Peking University



  • Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective, Lu Wang, Xuanqing Liu, Jinfeng Yi, Zhi-Hua Zhou, Cho-Jui Hsieh . ArXiv preprint (2019) [PDF] [Code]
  • Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise, Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh. ArXiv preprint (2019) [PDF]
  • Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient, Liu Liu, Xuanqing Liu, Cho-Jui Hsieh, Dacheng Tao. ArXiv preprint (2018). [PDF]
  • An inexact subsampled proximal Newton-type method for large-scale machine learning, Xuanqing Liu, Cho-Jui Hsieh*, Jason D. Lee*, Yuekai Sun* (*alphabetical order). ArXiv preprint (2017). [PDF]


  • A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning, Xuanqing Liu, Si Si, Xiaojin(Jerry) Zhu, Yang Li, Cho-Jui Hsieh. To appear at NeurIPS 2019 [PDF] [Poster] [Code]
  • Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks, Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh. KDD (2019) [PDF] Code[V1] [V2]
  • Rob-GAN: Generator, Discriminator and Adversarial Attacker, Xuanqing Liu, Cho-Jui Hsieh. CVPR (2019). [PDF] [Code]
  • Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network, Xuanqing Liu, Yao Li*, Chongruo Wu*, Cho-Jui Hsieh (*equal contribution). ICLR (2019). [PDF] [Code]
  • Towards Robust Neural Networks via Random Self-ensemble, Xuanqing Liu, Minhao Cheng, Huan Zhang, Cho-Jui Hsieh. ECCV (2018). [PDF] [Appendix] [Code]
  • Fast Variance Reduction Method with Stochastic Batch Size. Xuanqing Liu, Cho-Jui Hsieh. ICML (2018). [PDF]


  • Better Generalization by Efficient Trust Region Method. Xuanqing Liu, Jason D. Lee, Cho-Jui Hsieh. OpenReview.net. [PDF]


  • Summer/Fall 2019, Research Scientist Intern, Amazon A9 (Palo Alto, CA)
  • Fall/Winter 2018, Student Research Collaborator, Google Research (Mountain View, CA)
  • Summer 2018, Research Scientist Intern, Criteo Lab (Palo Alto, CA)


I serve as a reviewer for ICML, NeurIPS, CVPR, ICCV, ECCV, IJCAI, AAAI and TPAMI.