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Lingxiao Wang
Department of Data Science
New Jersey Institute of Technology
Email: lw324@njit.edu

Lingxiao Wang

I am an Assistant Professor in the Department of Data Science at New Jersey Institute of Technology. Previously I was a Research Assistant Professor at the Toyota Technological Institute at Chicago. I recevied my Ph.D. in Department of Computer Science at the University of California, Los Angeles, where I was advised by Professor Quanquan Gu. Prior to that, I obtained my MS in Statistics at University of Washington.

My research interests are broadly in machine learning, including privacy and security in machine learning (e.g., privacy risks in large models), collaborative learning (distributed and federated leanring), optimization for machine learning, low-rank matrix recovery, and high-dimensional statistics.

Recent News

[07/2023] I gave a talk on "On the Still Unreasonable Effectiveness of Federated Averaging for Heterogeneous Distributed Learning" in FL-ICML 2023.

[06/2023] We are oragnizing a workshop on New Frontiers in Federated Learning (Recent Theoretical Advances & Practice) at TTIC on September 8-9, 2023. You can register here!

[03/2023] I am serving as an area chair for NeurIPS 2023.

Publications

(*indicates equal contribution)

Revisiting Consensus Error: A Fine-grained Analysis of Local SGD under Second-order Data Heterogeneity
Kumar Kshitij Patel, Ali Zindari, Sebastian Stich, Lingxiao Wang, in Proc. of the 39th Advances in Neural Information Processing Systems (NeurIPS), 2025.

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao, Lingxiao Wang, Mladen Kolar, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen, Journal of Machine Learning Research (JMLR), 2025.
The Limits and Potentials of Local Sgd for Distributed Heterogeneous Learning with Intermittent Communication
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro, in Proc. of the 37th Conference on Learning Theory (COLT), 2024. Part of this work has been presented at ICML Workshop on Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities (FL-ICML 2023), 2023

Efficient Privacy-Preserving Stochastic Nonconvex Optimization
Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu, in Proc. of the 39th International Conference on Uncertainty in Artificial Intelligence (UAI), 2023.

Federated Online and Bandit Convex Optimization
Kumar Kshitij Patel, Lingxiao Wang, Aadrirupa Saha, Nathan Srebro, in Proc. of the 40th International Conference on Machine Learning (ICML), 2023. A short version of this work has been presented at the NeurIPS 2022 OPTML workshop.

Differentially Private Matrix Completion through Low-rank Matrix Factorization
Lingxiao Wang, Boxin Zhao, Mladen Kolar, in Proc. of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.

Towards Optimal Communication Complexity in Distributed Non-convex Optimization
Kumar Kshitij Patel, Lingxiao Wang, Blake Woodworth, Brian Bullins, Nathan Srebro, in Proc. of the 36th Advances in Neural Information Processing Systems (NeurIPS), 2022.

Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the US
Estee Y Cramer, ..., Lingxiao Wang, ..., Nicholas G. Reich, Proceedings of the National Academy of Sciences (PNAS), 2022.

Variance-reduced First-order Meta-learning for Natural Language Processing Tasks
Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang, in Proc. of 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021.

Revisiting Membership Inference Under Realistic Assumptions
Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans, 21st Privacy Enhancing Technologies Symposium (PETS), 2021.

Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks?
Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, Miryung Kim, in Proc. of the ACM SIGSOFT International Symposium on the Foundations of Software Engineering (ESEC/FSE), 2020.

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher, in Proc. of the Mathematical and Scientific Machine Learning Conference (MSML), 2020.

Improving Neural Language Generation with Spectrum Control
Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu, in Proc. of the 8th International Conference on Learning Representations (ICLR), 2020.

A Knowledge Transfer Framework for Differentially Private Sparse Learning
Lingxiao Wang, Quanquan Gu, in Proc. of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.

Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning
Lingxiao Wang, Quanquan Gu, in Proc. of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019.

Learning One-hidden-layer ReLU Networks via Gradient Descent
Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu, in Proc. of the 22nd International Conference on Artificial Intelligence and Statistics* (AISTATS), 2019.

Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu, in Proc. of the 32nd Advances in Neural Information Processing Systems (NeurIPS), 2018.

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu, in Proc. of the 35th International Conference on Machine Learning (ICML), 2018.

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Xiao Zhang, Lingxiao Wang, Quanquan Gu, in Proc. of the 35th International Conference on Machine Learning (ICML), 2018.

A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery
Xiao Zhang, Lingxiao Wang, Quanquan Gu, in Proc. of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.

Variance-Reduced Stochastic Gradient High-dimensional Expectation-Maximization Algorithm
Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu, in Proc. of the 34th International Conference on Machine Learning (ICML), 2017.

A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
Lingxiao Wang, Xiao Zhang, Quanquan Gu, in Proc. of the 34th International Conference on Machine Learning (ICML), 2017.

Robust Gaussian Graphical Model Estimation with Arbitrary Corruption
Lingxiao Wang, Quanquan Gu, in Proc. of the 34th International Conference on Machine Learning (ICML), 2017.

A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation
Lingxiao Wang, Xiao Zhang, Quanquan Gu, in Proc. of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates
Lingxiao Wang, Xiang Ren, Quanquan Gu, in Proc. of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.

Recent Preprints

Personalized Federated Training of Diffusion Models with Privacy Guarantees
Kumar Kshitij Patel, Weitong Zhang, Lingxiao Wang, arXiv:2504.00952,2025.
Efficient Private Federated Non-Convex Optimization With Shuffled Model
Lingxiao Wang, Xingyu Zhou, Kumar Kshitij Patel, Lawrence Tang, Aadirupa Saha, Privacy Regulation and Protection in Machine Learning Workshop at ICLR 2024.
Personalization Mitigates the Perils of Local SGD for Heterogeneous Distributed Learning
Kumar Kshitij Patel, Nidham Gazagnadou, Lingxiao Wang, Lingjuan Lyu, 2023.

Private Overparameterized Linear Regression without Suffering in High Dimensions
Lingxiao Wang, Difan Zou, Kumar Kshitij Patel, Jingfeng Wu, Nathan Srebro, 2023.

On the Effect of Defections in Federated Learning and How to Prevent Them
Minbiao Han, Kumar Kshitij Patel, Han Shao, Lingxiao Wang, arXiv:2311.16459, 2023.

Teaching

CS301: Introduction to Data Science, Fall 2025, NJIT.

DS644: Introduction to Big Data, Fall 2024, NJIT.

CS188: Data Science Fundamentals, Winter 2021, UCLA.

CS144: Web Applications, Spring 2021, UCLA.

CS118: Computer Network Fundamentals, FALL 2020, UCLA.

SYS 4582/6016: Machine Learning, Spring 2017, UVA.

SYS 3062: Discrete Event Simulation, Spring 2017, UVA.

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