Xu Zhang
Welcome to my homepage!
I am Xu Zhang, an assistant professor at the School of Artificial Intelligence, Xidian University. Previously, I was a Postdoc at the State Key Laboratory of Scientific and Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences from 2021 to 2023. I received my Ph.D. and B.S. degrees in electronics engineering from the School of Information and Electronics, Beijing Institute of Technology, Beijing, China, in 2021 and 2015, respectively. I was a Visiting Student with the Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA, from 2018 to 2019. My research interests include federated learning, distributed optimization, and sparse learning.
Email: connorbitzx at gmail dot com
Address: No. 266, Xinglong Section, Xifeng Road, Xi'an, Shaanxi Province
Services: ICML (2022 (Outstanding reviewer), 2023, 2024), NeurIPS (2022, 2023), IEEE TSP, IEEE TIT, IEEE Access, IEEE ISIT, IEEE CDC
Fundings: China Postdoctoral Science Foundation, China National Postdoctoral Program for Innovative Talents
[Research] [Publications] [Education]
Notes: Please don't hesitate to contact me if you're interested in my research.
News
[April 2024] Our paper entitled "Meta Generative Flow Networks with Personalization for Task-Specific Adaptation" has been accepted for publication in Infomation Sciences, 2024.
[March 2024] Our paper entitled "Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization" has been accepted for publication in IEEE Transactions on Signal Processing, 2024.
[Sept. 2023] I joined the School of Artificial Intelligence, Xidian University as an assistant professor.
[April 2023] I gave a talk named "Sparse Personalized Federated Learning" at Beihang University.
[March 2023] The paper entitled "Top-k data selection via distributed sample quantile inference " has been accepted for publication in the 5th Annual Learning for Dynamics & Control Conference (L4DC)，2023
[February 2023] The paper entitled "Sparse Personalized Federated Learning" has been accepted for publication in IEEE Transactions on Neural Networks and Learning Systems，2023
[Dec. 2022] I gave a talk named "Machine Learning with Side Information" at Hunan University.
[Sept. 2022] I gave a talk named "Personalized Federated Learning: Algorithm and Theory" at Tsinghua University.
[July 2022] The paper entitled "Robust remote estimation over the collision channel in the presence of an intelligent jammer" has been accepted for publication in the IEEE Conference on Decision and Control, 2022.
[May 2022] The paper entitled "Personalized Federated Learning via Variational Bayesian Inference" has been accepted for publication in the International Conference on Machine Learning (ICML), 2022.
[April 2022] The paper entitled "Data-Time Tradeoffs for Optimal k-Thresholding Algorithms in Compressed Sensing " has been accepted for publication in the IEEE International Symposium on Information Theory (ISIT), 2022.
[June 2021] I received the Excellent Ph.D. Thesis Award from Beijing Institute of Technology.
[June 2021] The paper entitled "Distributed remote estimation over the collision channel with and without local communication" has been accepted in IEEE Transactions on Control of Network Systems, 2021.
[April 2021] I joined the LSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences as a Postdoc.
[March 2021] The paper entitled "Spectrally Sparse Signal Recovery via Hankel Matrix Completion with Prior Information" has been accepted in IEEE Transactions on Signal Processing, 2021.
[May 2020] Talk at 45th International Conference on Acoustics, Speech, and Signal Processing, Virtual Barcelona, Spain.
[Jan. 2020] The paper entitled "An optimal symmetric threshold strategy for remote estimation over the collision channel" has been accepted for publication in the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.
[April 2019] The paper entitled "Covariance Matrix Estimation from Linearly-Correlated Gaussian Samples" has been published in IEEE Transactions on Signal Processing, 2019.