Jian Lou

Associate Professor

Xidian University

Email: jlou (at) xidian.edu.cn (current)

jian.lou (at) emory.edu (previous)

About me

I am currently an associate professor at Xidian University. I was a Post-doc in the Department of Computer Science at Emory University from 2019 to 2021. I was very fortunate to be hosted by Prof. Li Xiong. I obtained my Ph.D. in Computer Science at Hong Kong Baptist University in 2018, supervised by Prof. Yiu-ming Cheung. Prior to that, I received a B.S. in Mathematics from Zhejiang University in 2013. Here is my homepage in Chinese (中文主页).

Research Interests

  • Machine Learning Privacy and Security

  • Data Markets and Data Economy

  • Machine Learning Optimization



  • with Haowen Lin, Li Xiong, Cyrus Shahabi, “SemiFed: A Framework to Leverage Unlabeled Data in Federated Learning with Consistency and Pseudo labeling", under review.

Conference Proceedings

  • with Farnaz Tahmasebian, Li Xiong, “RobustFed: A Truth Inference Approach for Robust Federated Learning", CIKM'22.

  • Congcong Fu, Hui Li, Jian Lou, Jiangtao Cui, DP-HORUS: Differentially Private Hierarchical Count Histograms under Untrusted Server", CIKM'22.

  • with Xiaoyu Zhang, Yulin Jin, Tao Wang, Xiaofeng Chen, Purifier: Plug-and-play Backdoor Mitigation for Pre-trained Models Via Anomaly Activation Suppression", ACMMM'22.

  • Yuhua Sun, Tailai Zhang, Xingjun Ma, Pan Zhou, Jian Lou, Zichuan Xu, Xing Di, Yu Cheng, Lichao Sun, Backdoor Attacks on Crowd Counting", ACMMM'22.

  • Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, Xiaofeng Meng, “Projected Federated Averaging with Heterogeneous Differential Privacy", VLDB'22.

  • with Haowen Lin, Li Xiong, Cyrus Shahabi, “Integer-arithmetic-only Certified Robustness for Quantized Neural Networks", ICCV'21.

  • with Qiuchen Zhang, Jing Ma, Li Xiong, “Private Stochastic Non-convex Optimization with Improved Utility Rates", IJCAI'21.

  • with Wenjie Wang, Pengfei Tang, Li Xiong, “Certified Robustness to Word Substitution Attack with Differential Privacy", NAACL'21.

  • with Jing Ma, Qiuchen Zhang, Li Xiong, Joyce Ho, “Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics", WWW'21.

  • Jinfei Liu, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, Jimeng Sun, “Dealer: An End-to-End Model Marketplace with Differential Privacy", VLDB'21.

  • Yiu-ming Cheung, Jian Lou, Feng Yu, “Vertical Federated Principal Component Analysis on Feature-wise Distributed Data", WISE'21.

  • Jing Ma, Qiuchen Zhang, Jian Lou, Li Xiong, Joyce Ho, Sivasubramanium Bhavani, “Communication Efficient Tensor Factorization for Decentralized Healthcare Networks", ICDM'21.

  • Jing Ma, Qiuchen Zhang, Jian Lou, Li Xiong, Joyce Ho, “Temporal Network Embedding via Tensor Factorization", CIKM'21.

  • Jinfei Liu, Qiongqiong Lin, Jiayao Zhang, Kui Ren, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, Jimeng Sun, “Demonstration of Dealer: An End-to-End Model Marketplace with Differential Privacy", VLDB'21 Demo Track.

  • with Yiu-ming Cheung, “Projection-free Online Empirical Risk Minimization with Privacy-preserving and Privacy Expiration", WI-IAT'20 (Best in Theoretical Paper Award).

  • with Yifei Ren, Li Xiong, Joyce Ho,Robust Irregular Tensor Factorization and Completion for Temporal Health Data Analysis", CIKM'20.

  • Qiuchen Zhang, Jing Ma, Yonghui Xiao, Jian Lou, Li Xiong, “Broadening Differential Privacy for Deep Learning Against Model Inversion Attacks", Bigdata'20.

  • Qiuchen Zhang, Jing Ma, Jian Lou, Li Xiong, Xiaoqian Jiang, “Towards Training Robust Private Aggregation of Teacher Ensembles Under Noisy Labels", Bigdata'20.

  • Jing Ma, Qiuchen Zhang, Jian Lou, Joyce Ho, Li Xiong, Xiaoqian Jiang, "Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis", CIKM'19.

  • with Wenwen Li, Shuo Zhou, Haiping Lu, “Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI", MLMI@MICCAI'19.

  • with Yiu-ming Cheung, "Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data", AAAI'18.

  • with Yiu-ming Cheung, “Scalable Spectral k-Support Norm Regularization for Robust Low Rank Subspace Learning", CIKM'16.

  • with Yiu-ming Cheung, “Efficient Generalized Conditional Gradient with Gradient Sliding for Composite Optimization", IJCAI'15.

  • with Yiu-ming Cheung, “Proximal Average Approximated Incremental Gradient Method for Composite Penalty Regularized Empirical Risk Minimization", ACML'15.

Journal Publications

  • Pengfei Tang, Wenjie Wang, Jian Lou, Li Xiong, “Generating Adversarial Examples with Distance Constrained Adversarial Imitation Networks", IEEE Transactions on Dependable and Secure Computing, 2022.

  • with Yiu-ming Cheung, “An Uplink Communication Efficient Approach to Feature-wise Distributed Sparse Optimization with Differential Privacy”, IEEE Transactions on Neural Networks and Learning Systems, 2021.

  • Qiquan Shi, Yiu-ming Cheung, Jian Lou, Robust Tensor SVD and Recovery with Rank Estimation", IEEE Transactions on Cybernetics, 2021.

  • with Yiu-ming Cheung, “Robust Low-rank Tensor Minimization via a New Tensor Spectral k-Support Norm”, IEEE Transactions on Image Processing, 2020.

  • with Yiu-ming Cheung, “Proximal Average Approximated Incremental Gradient Descent for Composite Penalty Regularized Empirical Risk Minimization”, Machine Learning, 2017.

  • Meng Pang, Yiu-ming Cheung, Binghui Wang, Jian Lou, “Synergistic Generic Learning for Face Recognition From a Contaminated Single Sample per Person", IEEE Transactions on Information Forensics and Security, 2020.

  • Meng Pang, Yiu-ming Cheung, Risheng Liu, Jian Lou, and Chuang Lin, “Toward efficient image representation: Sparse concept discriminant matrix factorization", IEEE Transactions on Circuits and Systems for Video Technology, 2018.


Conference Program Committee Member

  • International Conference on Very Large Data Bases (VLDB 2023)

  • ACM Conference on Computer and Communications Security (ACM CCS 2022)

  • Conference on Neural Information Processing Systems (NeurIPS 2022)

  • International Conference on Machine Learning (ICML 2022)

  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)

  • International Joint Conference on Artificial Intelligence (IJCAI 2022)

  • AAAI Conference on Artificial Intelligence (AAAI 2022 2021 2020 2019)

  • International Conference on Web Search and Data Mining (WSDM 2022)

  • International Conference on Artificial Intelligence and Statistics (AISTATS 2021)

  • Medical Image Computing and Computer Assisted Interventions (MICCAI 2021 2020)

  • ACM International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2021 2020)

  • International Conference on Information and Communication Systems (ICICS 2021)

  • IEEE International Conference on Big Data (IEEE Bigdata 2021 2020)

  • IEEE International Conference on Distributed Computing Systems (IEEE ICDCS 2020)

Journal Reviewer

  • IEEE Transactions on Image Processing

  • IEEE Transactions on Knowledge and Data Engineering

  • IEEE Transactions on Biomedical Engineering

  • ACM Transactions on Computing for Healthcare

  • IEEE Transactions on Neural Networks and Learning Systems

  • IEEE Transactions on Cybernetics

  • IEEE Signal Processing Letters

  • IEEE Communications Letters