ZHU, Guangxu

POSITION/TITLE             

Associate Director of the Data-driven Intelligent Information System Laboratory

Research Scientist     

RESEARCH INTEREST

Edge Intelligence, Federated Learning, Integrated Communications and Sensing

EMAIL

gxzhu@sribd.cn 

EDUCATION BACKGROUND

From 2015-9 to 2019-12, The University of Hong Kong, Electronic and Electrical Engineering, PhD, Supervisor: Kaibin Huang 

From 2012-9 to 2015-3, Zhejiang University, Information and Electronic Engineering, Master, Supervisor: Caijun Zhong

From 2008-9 to 2012-6, Zhejiang University, Information and Electronic Engineering, bachelor 

HONORS AND AWARDS

  •  Best paper award at 2013 International Conference on WCSP.
  •  Hong Kong Ph.D. Fellowship. (09/2015 - 08/2019)
  • Outstanding Ph.D. Thesis Award at The University of Hong Kong
  • Exemplary Reviewer at IEEE Transactions on Communications 
  • Shenzhen Overseas High-Caliber Personnel (Level C)

BIOGRAPHY

Dr. Guangxu Zhu is a research scientist at the Shenzhen research institute of big data. He received the Ph.D. degree in electrical and electronic engineering from The University of Hong Kong in 2019, and the BS and MS degrees in information and communication engineering from Zhejiang University in 2012 and 2015, respectively. His research interests include intelligent edge, federated learning, and integrated sensing and communications. He have published over 30 top-tier journal papers and more than 20 conference papers in his research area. He is currently hosting several national and provincial project including NSFC Youth Program, Guangdong Natural Science Foundation General Program, and the 6G open project of China Academy of Information and Communications Technology and participating the National Key Research and Development Program of China. He is a recipient of the Hong Kong Postgraduate Fellowship (HKPF), Outstanding Ph.D. Thesis Award from HKU, and Best Paper Award from WCSP 2013, Shenzhen Overseas High-Caliber Personnel (Level C). He was invited to be a co-chair for the “MAC and cross-layer design” track in IEEE PIMRC 2021.

SELECTED JOURNAL PUBLICATIONS

1.G. Zhu, D. Liu, Y, Du, C. You, J. Zhang, and K. Huang, "Toward an Intelligent Edge: Wireless Communication Meets Machine Learning", IEEE Commun. Mag., vol. 58, no. 1, pp. 19 - 25, Jan. 2020. ( ESI highly cited paper, ESI hot paper)

2. G. Zhu, Y. Wang K. Huang, "Broadband analog aggregation for low-latency federated edge learning", IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 491-506, Jan. 2020. ( ESI highly cited paper)

3. G. Zhu, Y. Du, D. Gunduz, K. Huang, "One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis", IEEE Trans. Wireless Commun., vol. 20, no. 3, Mar. 2021.

4. G. Zhu, K. Huang, V. K. N. Lau, B. Xia, X. Li and S. Zhang, "Hybrid beamforming via the Kronecker decomposition for the millimeter-wave massive MIMO systems", IEEE J. Sel Area Commun., Vol. 35, no. 9, pp. 2097–2114, Sep. 2017. 

5.G. Zhu and K. Huang, "MIMO Over-the-Air Computation for High-Mobility Multi-Modal Sensing", IEEE IoT Journal, vol. 6, no. 4, pp. 6089 - 6103, Aug. 2019. 

PU, Wenqiang

POSITION/TITLE

Research Scientist                  

Assistant Director

PROFESSIONAL EXPERIENCES

2020/10 to present, Shenzhen Research Institute of Big Data, Research Scientist, Position

2019/01 to 2020/09, CUHKSZ, Postdoc Researcher, Position

EDUCATION BACKGROUND

2013/09 to 2018/12, Xidian University, Signal and Information Processing, Ph.D

2009/09 to 2013/06, Xidian University, Electronic Information Engineering, Bachelor

EMAIL

wpu@sribd.cn

PERSONAL WEBSITE

https://wqpu.github.io

RESEARCH FIELD

Signal Processing, Optimization Methods，Machine Learning

BIOGRAPHY

Wenqiang Pu received the B.S. and Ph.D degrees from Xidian University, in 2013 and 2018, respectively. During Jan. 2015 to Sep. 2018, he was a visiting Ph.D student at the Chinese University of Hong Kong (Shenzhen). From Jan. 2019 to Sep. 2020, he was a postdoc researcher with CUHKSZ. He joined SRIBD since Oct. 2020. His research interests include large scale signal processing, resource allocation in sensor network, and optimization algorithms. He has published many papers in journals / conferences on signal processing, including 2 ESI highly cited papers. At present, he hosts the youth project of the National Natural Science Foundation of China.

ACADEMIC PUBLICATIONS

  • Pu, W.*, Ibrahim, S., Fu, X., & Hong, M. (2022). Stochastic mirror descent for low-rank tensor decomposition under non-Euclidean losses. IEEE Transactions on Signal Processing, 70, 1803-1818.
  • Pu, W.*, Xiao, J., Zhang, T., & Luo, Z. Q. (2022). A Penalized Inequality-Constrained Approach for Robust Beamforming with DoF Limitation. Signal Processing, 108746.
  • Sun, H., Pu, W., Fu, X., Chang, T. H., & Hong, M.* (2022). Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective. IEEE Transactions on Signal Processing, 70, 1900-1917.
  • Yan, J., Pu, W.*, Liu, H.*, Jiu, B., & Bao, Z. (2018). Robust chance constrained power allocation scheme for multiple target localization in colocated MIMO radar system. IEEE Transactions on Signal Processing, 66(15), 3946-3957.
  • Pu, W., Liu, Y. F., Yan, J., Liu, H., & Luo, Z. Q*. (2018). Optimal estimation of sensor biases for asynchronous multi-sensor data fusion. Mathematical Programming, 170(1), 357-386.
PU, Wenqiang

POSITION/TITLE

Research Scientist                  

Assistant Director

PROFESSIONAL EXPERIENCES

2020/10 to present, Shenzhen Research Institute of Big Data, Research Scientist, Position

2019/01 to 2020/09, CUHKSZ, Postdoc Researcher, Position

EDUCATION BACKGROUND

2013/09 to 2018/12, Xidian University, Signal and Information Processing, Ph.D

2009/09 to 2013/06, Xidian University, Electronic Information Engineering, Bachelor

EMAIL

wpu@sribd.cn

PERSONAL WEBSITE

https://wqpu.github.io

RESEARCH FIELD

Signal Processing, Optimization Methods，Machine Learning

BIOGRAPHY

Wenqiang Pu received the B.S. and Ph.D degrees from Xidian University, in 2013 and 2018, respectively. During Jan. 2015 to Sep. 2018, he was a visiting Ph.D student at the Chinese University of Hong Kong (Shenzhen). From Jan. 2019 to Sep. 2020, he was a postdoc researcher with CUHKSZ. He joined SRIBD since Oct. 2020. His research interests include large scale signal processing, resource allocation in sensor network, and optimization algorithms. He has published many papers in journals / conferences on signal processing, including 2 ESI highly cited papers. At present, he hosts the youth project of the National Natural Science Foundation of China.

ACADEMIC PUBLICATIONS

  • Pu, W.*, Ibrahim, S., Fu, X., & Hong, M. (2022). Stochastic mirror descent for low-rank tensor decomposition under non-Euclidean losses. IEEE Transactions on Signal Processing, 70, 1803-1818.
  • Pu, W.*, Xiao, J., Zhang, T., & Luo, Z. Q. (2022). A Penalized Inequality-Constrained Approach for Robust Beamforming with DoF Limitation. Signal Processing, 108746.
  • Sun, H., Pu, W., Fu, X., Chang, T. H., & Hong, M.* (2022). Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective. IEEE Transactions on Signal Processing, 70, 1900-1917.
  • Yan, J., Pu, W.*, Liu, H.*, Jiu, B., & Bao, Z. (2018). Robust chance constrained power allocation scheme for multiple target localization in colocated MIMO radar system. IEEE Transactions on Signal Processing, 66(15), 3946-3957.
  • Pu, W., Liu, Y. F., Yan, J., Liu, H., & Luo, Z. Q*. (2018). Optimal estimation of sensor biases for asynchronous multi-sensor data fusion. Mathematical Programming, 170(1), 357-386.
PU, Wenqiang

POSITION/TITLE

Research Scientist                  

Assistant Director

PROFESSIONAL EXPERIENCES

2020/10 to present, Shenzhen Research Institute of Big Data, Research Scientist, Position

2019/01 to 2020/09, CUHKSZ, Postdoc Researcher, Position

EDUCATION BACKGROUND

2013/09 to 2018/12, Xidian University, Signal and Information Processing, Ph.D

2009/09 to 2013/06, Xidian University, Electronic Information Engineering, Bachelor

EMAIL

wpu@sribd.cn

PERSONAL WEBSITE

https://wqpu.github.io

RESEARCH FIELD

Signal Processing, Optimization Methods，Machine Learning

BIOGRAPHY

Wenqiang Pu received the B.S. and Ph.D degrees from Xidian University, in 2013 and 2018, respectively. During Jan. 2015 to Sep. 2018, he was a visiting Ph.D student at the Chinese University of Hong Kong (Shenzhen). From Jan. 2019 to Sep. 2020, he was a postdoc researcher with CUHKSZ. He joined SRIBD since Oct. 2020. His research interests include large scale signal processing, resource allocation in sensor network, and optimization algorithms. He has published many papers in journals / conferences on signal processing, including 2 ESI highly cited papers. At present, he hosts the youth project of the National Natural Science Foundation of China.

ACADEMIC PUBLICATIONS

  • Pu, W.*, Ibrahim, S., Fu, X., & Hong, M. (2022). Stochastic mirror descent for low-rank tensor decomposition under non-Euclidean losses. IEEE Transactions on Signal Processing, 70, 1803-1818.
  • Pu, W.*, Xiao, J., Zhang, T., & Luo, Z. Q. (2022). A Penalized Inequality-Constrained Approach for Robust Beamforming with DoF Limitation. Signal Processing, 108746.
  • Sun, H., Pu, W., Fu, X., Chang, T. H., & Hong, M.* (2022). Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective. IEEE Transactions on Signal Processing, 70, 1900-1917.
  • Yan, J., Pu, W.*, Liu, H.*, Jiu, B., & Bao, Z. (2018). Robust chance constrained power allocation scheme for multiple target localization in colocated MIMO radar system. IEEE Transactions on Signal Processing, 66(15), 3946-3957.
  • Pu, W., Liu, Y. F., Yan, J., Liu, H., & Luo, Z. Q*. (2018). Optimal estimation of sensor biases for asynchronous multi-sensor data fusion. Mathematical Programming, 170(1), 357-386.
PU, Wenqiang

POSITION/TITLE

Research Scientist                  

Assistant Director

PROFESSIONAL EXPERIENCES

2020/10 to present, Shenzhen Research Institute of Big Data, Research Scientist, Position

2019/01 to 2020/09, CUHKSZ, Postdoc Researcher, Position

EDUCATION BACKGROUND

2013/09 to 2018/12, Xidian University, Signal and Information Processing, Ph.D

2009/09 to 2013/06, Xidian University, Electronic Information Engineering, Bachelor

EMAIL

wpu@sribd.cn

PERSONAL WEBSITE

https://wqpu.github.io

RESEARCH FIELD

Signal Processing, Optimization Methods，Machine Learning

BIOGRAPHY

Wenqiang Pu received the B.S. and Ph.D degrees from Xidian University, in 2013 and 2018, respectively. During Jan. 2015 to Sep. 2018, he was a visiting Ph.D student at the Chinese University of Hong Kong (Shenzhen). From Jan. 2019 to Sep. 2020, he was a postdoc researcher with CUHKSZ. He joined SRIBD since Oct. 2020. His research interests include large scale signal processing, resource allocation in sensor network, and optimization algorithms. He has published many papers in journals / conferences on signal processing, including 2 ESI highly cited papers. At present, he hosts the youth project of the National Natural Science Foundation of China.

ACADEMIC PUBLICATIONS

  • Pu, W.*, Ibrahim, S., Fu, X., & Hong, M. (2022). Stochastic mirror descent for low-rank tensor decomposition under non-Euclidean losses. IEEE Transactions on Signal Processing, 70, 1803-1818.
  • Pu, W.*, Xiao, J., Zhang, T., & Luo, Z. Q. (2022). A Penalized Inequality-Constrained Approach for Robust Beamforming with DoF Limitation. Signal Processing, 108746.
  • Sun, H., Pu, W., Fu, X., Chang, T. H., & Hong, M.* (2022). Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective. IEEE Transactions on Signal Processing, 70, 1900-1917.
  • Yan, J., Pu, W.*, Liu, H.*, Jiu, B., & Bao, Z. (2018). Robust chance constrained power allocation scheme for multiple target localization in colocated MIMO radar system. IEEE Transactions on Signal Processing, 66(15), 3946-3957.
  • Pu, W., Liu, Y. F., Yan, J., Liu, H., & Luo, Z. Q*. (2018). Optimal estimation of sensor biases for asynchronous multi-sensor data fusion. Mathematical Programming, 170(1), 357-386.
LI, Yang

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Radio Resource Management, Learning to Optimize, Large-Scale Optimization, Massive Random Access

EMAIL

liyang@sribd.cn

EDUCATION BACKGROUND

Ph.D. The University of Hong Kong

M.E Beihang University

B.E     Beihang University

BIOGRAPHY

Dr. Yang Li received his B.E. and M.E. degrees in Electronic Information Engineering from Beihang University, Beijing, China, in 2012 and 2015, respectively, and his Ph.D. in Electrical and Electronic Engineering from The University of Hong Kong (HKU) in 2019. He has a long-standing commitment to research at the intersection of wireless communications, artificial intelligence, and large-scale optimization, with extensive experience and innovative results in areas such as massive MIMO signal processing, learning to optimize, and the design of distributed optimization algorithms. From 2019 to 2020, he served as a Senior Research Engineer at Huawei Noah's Ark Laboratory, where he conducted research on intelligent communication algorithms and achieved breakthrough results in the intelligent design of nested polar codes, earning him the Innovation Pioneer Award from Huawei Technologies Co., Ltd. Currently, he holds the position of Research Scientist at the Shenzhen Research Institute of Big Data, where in recent years he has led projects in large-scale wireless resource management, large-scale network optimization, and large-scale intelligent access. He has presided over the National Natural Science Foundation of China's (NSFC's) Youth Fund, the sub-project of the National Key R&D Program, and the open project of National Key Laboratory, and has participated as the principal investigator in the joint fund project of the NSFC. His research has resulted in the publication of over 30 high-level papers in top international journals in the fields of wireless communications and signal processing, such as IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing, including over 20 as the first author or corresponding author.

ACADEMIC PUBLICATIONS

[1] Yang Li and Ya-Feng Liu, “HPE Transformer: Learning to Optimize Multi-Group Multicast Beamforming Under Nonconvex QoS Constraints,” IEEE Transactions on Communications, early access, Apr. 2024.

[2] Hao Zhang, Qingfeng Lin, Yang Li*, Lei Cheng, and Yik-Chung Wu, “Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Active Probability: A Bayesian Approach,” IEEE Transactions on Signal Processing, vol. 72, pp. 942-957, 2024.

[3] Yunqi Wang, Yang Li*, Qingjiang Shi, and Yik-Chung Wu, “ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks,” IEEE Transactions on Wireless Communications, early access, Oct. 2023.

[4] Yang Li, Zhilin Chen, Yunqi Wang, Chenyang Yang, Bo Ai, and Yik-Chung Wu, “Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection,” IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3432-3446, May 2023.

[5] Yang Li, Qingfeng Lin, Ya-Feng Liu, Bo Ai, and Yik-Chung Wu, “Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms,” IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2477-2492, Apr. 2023.

LI, Yang

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Radio Resource Management, Learning to Optimize, Large-Scale Optimization, Massive Random Access

EMAIL

liyang@sribd.cn

EDUCATION BACKGROUND

Ph.D. The University of Hong Kong

M.E Beihang University

B.E     Beihang University

BIOGRAPHY

Dr. Yang Li received his B.E. and M.E. degrees in Electronic Information Engineering from Beihang University, Beijing, China, in 2012 and 2015, respectively, and his Ph.D. in Electrical and Electronic Engineering from The University of Hong Kong (HKU) in 2019. He has a long-standing commitment to research at the intersection of wireless communications, artificial intelligence, and large-scale optimization, with extensive experience and innovative results in areas such as massive MIMO signal processing, learning to optimize, and the design of distributed optimization algorithms. From 2019 to 2020, he served as a Senior Research Engineer at Huawei Noah's Ark Laboratory, where he conducted research on intelligent communication algorithms and achieved breakthrough results in the intelligent design of nested polar codes, earning him the Innovation Pioneer Award from Huawei Technologies Co., Ltd. Currently, he holds the position of Research Scientist at the Shenzhen Research Institute of Big Data, where in recent years he has led projects in large-scale wireless resource management, large-scale network optimization, and large-scale intelligent access. He has presided over the National Natural Science Foundation of China's (NSFC's) Youth Fund, the sub-project of the National Key R&D Program, and the open project of National Key Laboratory, and has participated as the principal investigator in the joint fund project of the NSFC. His research has resulted in the publication of over 30 high-level papers in top international journals in the fields of wireless communications and signal processing, such as IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing, including over 20 as the first author or corresponding author.

ACADEMIC PUBLICATIONS

[1] Yang Li and Ya-Feng Liu, “HPE Transformer: Learning to Optimize Multi-Group Multicast Beamforming Under Nonconvex QoS Constraints,” IEEE Transactions on Communications, early access, Apr. 2024.

[2] Hao Zhang, Qingfeng Lin, Yang Li*, Lei Cheng, and Yik-Chung Wu, “Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Active Probability: A Bayesian Approach,” IEEE Transactions on Signal Processing, vol. 72, pp. 942-957, 2024.

[3] Yunqi Wang, Yang Li*, Qingjiang Shi, and Yik-Chung Wu, “ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks,” IEEE Transactions on Wireless Communications, early access, Oct. 2023.

[4] Yang Li, Zhilin Chen, Yunqi Wang, Chenyang Yang, Bo Ai, and Yik-Chung Wu, “Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection,” IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3432-3446, May 2023.

[5] Yang Li, Qingfeng Lin, Ya-Feng Liu, Bo Ai, and Yik-Chung Wu, “Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms,” IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2477-2492, Apr. 2023.

LI, Yang

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Radio Resource Management, Learning to Optimize, Large-Scale Optimization, Massive Random Access

EMAIL

liyang@sribd.cn

EDUCATION BACKGROUND

Ph.D. The University of Hong Kong

M.E Beihang University

B.E     Beihang University

BIOGRAPHY

Dr. Yang Li received his B.E. and M.E. degrees in Electronic Information Engineering from Beihang University, Beijing, China, in 2012 and 2015, respectively, and his Ph.D. in Electrical and Electronic Engineering from The University of Hong Kong (HKU) in 2019. He has a long-standing commitment to research at the intersection of wireless communications, artificial intelligence, and large-scale optimization, with extensive experience and innovative results in areas such as massive MIMO signal processing, learning to optimize, and the design of distributed optimization algorithms. From 2019 to 2020, he served as a Senior Research Engineer at Huawei Noah's Ark Laboratory, where he conducted research on intelligent communication algorithms and achieved breakthrough results in the intelligent design of nested polar codes, earning him the Innovation Pioneer Award from Huawei Technologies Co., Ltd. Currently, he holds the position of Research Scientist at the Shenzhen Research Institute of Big Data, where in recent years he has led projects in large-scale wireless resource management, large-scale network optimization, and large-scale intelligent access. He has presided over the National Natural Science Foundation of China's (NSFC's) Youth Fund, the sub-project of the National Key R&D Program, and the open project of National Key Laboratory, and has participated as the principal investigator in the joint fund project of the NSFC. His research has resulted in the publication of over 30 high-level papers in top international journals in the fields of wireless communications and signal processing, such as IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing, including over 20 as the first author or corresponding author.

ACADEMIC PUBLICATIONS

[1] Yang Li and Ya-Feng Liu, “HPE Transformer: Learning to Optimize Multi-Group Multicast Beamforming Under Nonconvex QoS Constraints,” IEEE Transactions on Communications, early access, Apr. 2024.

[2] Hao Zhang, Qingfeng Lin, Yang Li*, Lei Cheng, and Yik-Chung Wu, “Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Active Probability: A Bayesian Approach,” IEEE Transactions on Signal Processing, vol. 72, pp. 942-957, 2024.

[3] Yunqi Wang, Yang Li*, Qingjiang Shi, and Yik-Chung Wu, “ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks,” IEEE Transactions on Wireless Communications, early access, Oct. 2023.

[4] Yang Li, Zhilin Chen, Yunqi Wang, Chenyang Yang, Bo Ai, and Yik-Chung Wu, “Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection,” IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3432-3446, May 2023.

[5] Yang Li, Qingfeng Lin, Ya-Feng Liu, Bo Ai, and Yik-Chung Wu, “Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms,” IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2477-2492, Apr. 2023.

LI, Yang

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Radio Resource Management, Learning to Optimize, Large-Scale Optimization, Massive Random Access

EMAIL

liyang@sribd.cn

EDUCATION BACKGROUND

Ph.D. The University of Hong Kong

M.E Beihang University

B.E     Beihang University

BIOGRAPHY

Dr. Yang Li received his B.E. and M.E. degrees in Electronic Information Engineering from Beihang University, Beijing, China, in 2012 and 2015, respectively, and his Ph.D. in Electrical and Electronic Engineering from The University of Hong Kong (HKU) in 2019. He has a long-standing commitment to research at the intersection of wireless communications, artificial intelligence, and large-scale optimization, with extensive experience and innovative results in areas such as massive MIMO signal processing, learning to optimize, and the design of distributed optimization algorithms. From 2019 to 2020, he served as a Senior Research Engineer at Huawei Noah's Ark Laboratory, where he conducted research on intelligent communication algorithms and achieved breakthrough results in the intelligent design of nested polar codes, earning him the Innovation Pioneer Award from Huawei Technologies Co., Ltd. Currently, he holds the position of Research Scientist at the Shenzhen Research Institute of Big Data, where in recent years he has led projects in large-scale wireless resource management, large-scale network optimization, and large-scale intelligent access. He has presided over the National Natural Science Foundation of China's (NSFC's) Youth Fund, the sub-project of the National Key R&D Program, and the open project of National Key Laboratory, and has participated as the principal investigator in the joint fund project of the NSFC. His research has resulted in the publication of over 30 high-level papers in top international journals in the fields of wireless communications and signal processing, such as IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing, including over 20 as the first author or corresponding author.

ACADEMIC PUBLICATIONS

[1] Yang Li and Ya-Feng Liu, “HPE Transformer: Learning to Optimize Multi-Group Multicast Beamforming Under Nonconvex QoS Constraints,” IEEE Transactions on Communications, early access, Apr. 2024.

[2] Hao Zhang, Qingfeng Lin, Yang Li*, Lei Cheng, and Yik-Chung Wu, “Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Active Probability: A Bayesian Approach,” IEEE Transactions on Signal Processing, vol. 72, pp. 942-957, 2024.

[3] Yunqi Wang, Yang Li*, Qingjiang Shi, and Yik-Chung Wu, “ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks,” IEEE Transactions on Wireless Communications, early access, Oct. 2023.

[4] Yang Li, Zhilin Chen, Yunqi Wang, Chenyang Yang, Bo Ai, and Yik-Chung Wu, “Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection,” IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3432-3446, May 2023.

[5] Yang Li, Qingfeng Lin, Ya-Feng Liu, Bo Ai, and Yik-Chung Wu, “Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms,” IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2477-2492, Apr. 2023.

XUE, Ye

 POSITION/TITLE

Research Scientist 

RESEARCH FIELD

Machine learning, non-convex optimization, online learning, federated learning, applications of artificial intelligence in communication networks, digital twins, and other interdisciplinary fields intersecting machine learning and communication.

PERSONAL WEBSITE

https://yokoxue.github.io/

EMAIL

yokoxue@sribd.cn

EDUCATION BACKGROUND

The Hong Kong University of Science and Technology, PhD

Southeast University​​​​​, ​​Bachelor

BIOGRAPHY

Dr. Xue Ye obtained his Bachelor's degree in Communication Engineering from Southeast University in 2017 and his PhD degree from the School of Electronic and Computer Engineering at the Hong Kong University of Science and Technology in 2022. In the same year, he joined the Shenzhen Research Institute of Big Data as a research scientist. Dr. Xue has been engaged in research in the interdisciplinary fields of non-convex optimization, high-dimensional statistics, machine learning, and communication networks for many years. He has published over ten academic papers as the first author in high-level international journals and conferences in the fields of artificial intelligence and wireless communication, collaborating closely with numerous renowned scholars both domestically and internationally. Currently, he is actively involved as a key member in national key R&D projects.

ACADEMIC PUBLICATIONS

  1. Y. Xue and V. Lau, “Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation” IEEE Transactions on Signal Processing, 2023
  2. Ye Xue; Vincent K. N. Lau ; Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method, IEEE Transactions on Neural Networks and Learning Systems, 2021, 1-15
  3. Ye Xue; Vincent K. N. Lau; Songfu Cai ; Efficient Sparse Coding Using Hierarchical Riemannian Pursuit, IEEE Transactions on Signal Processing, 2021, 69: 4069-4084
  4. Ye Xue; Yifei Shen; Vincent K. N. Lau; Jun Zhang; Khaled B. Letaief ; Blind Data Detection in Massive MIMO via ℓ₃-Norm Maximization Over the Stiefel Manifold, IEEE Transactions on Wireless Communications, 2021, 20(2): 1411-1424
  5. Y. Xue*, Y. Shen, J. Zhang, K. Letaief, and V. Lau, “Complete dictionary learning via ℓp-norm maximization,” Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:280-289, 2020. （Co-first Author）
  6. Ye Xue; Liqun Su; Vincent K. N. Lau ; FedOComp: Two-Timescale Online Gradient Compression for Over-the-Air Federated Learning, IEEE Internet of Things Journal, 2022
XUE, Ye

 POSITION/TITLE

Research Scientist 

RESEARCH FIELD

Machine learning, non-convex optimization, online learning, federated learning, applications of artificial intelligence in communication networks, digital twins, and other interdisciplinary fields intersecting machine learning and communication.

PERSONAL WEBSITE

https://yokoxue.github.io/

EMAIL

yokoxue@sribd.cn

EDUCATION BACKGROUND

The Hong Kong University of Science and Technology, PhD

Southeast University​​​​​, ​​Bachelor

BIOGRAPHY

Dr. Xue Ye obtained his Bachelor's degree in Communication Engineering from Southeast University in 2017 and his PhD degree from the School of Electronic and Computer Engineering at the Hong Kong University of Science and Technology in 2022. In the same year, he joined the Shenzhen Research Institute of Big Data as a research scientist. Dr. Xue has been engaged in research in the interdisciplinary fields of non-convex optimization, high-dimensional statistics, machine learning, and communication networks for many years. He has published over ten academic papers as the first author in high-level international journals and conferences in the fields of artificial intelligence and wireless communication, collaborating closely with numerous renowned scholars both domestically and internationally. Currently, he is actively involved as a key member in national key R&D projects.

ACADEMIC PUBLICATIONS

  1. Y. Xue and V. Lau, “Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation” IEEE Transactions on Signal Processing, 2023
  2. Ye Xue; Vincent K. N. Lau ; Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method, IEEE Transactions on Neural Networks and Learning Systems, 2021, 1-15
  3. Ye Xue; Vincent K. N. Lau; Songfu Cai ; Efficient Sparse Coding Using Hierarchical Riemannian Pursuit, IEEE Transactions on Signal Processing, 2021, 69: 4069-4084
  4. Ye Xue; Yifei Shen; Vincent K. N. Lau; Jun Zhang; Khaled B. Letaief ; Blind Data Detection in Massive MIMO via ℓ₃-Norm Maximization Over the Stiefel Manifold, IEEE Transactions on Wireless Communications, 2021, 20(2): 1411-1424
  5. Y. Xue*, Y. Shen, J. Zhang, K. Letaief, and V. Lau, “Complete dictionary learning via ℓp-norm maximization,” Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:280-289, 2020. （Co-first Author）
  6. Ye Xue; Liqun Su; Vincent K. N. Lau ; FedOComp: Two-Timescale Online Gradient Compression for Over-the-Air Federated Learning, IEEE Internet of Things Journal, 2022
XUE, Ye

 POSITION/TITLE

Research Scientist 

RESEARCH FIELD

Machine learning, non-convex optimization, online learning, federated learning, applications of artificial intelligence in communication networks, digital twins, and other interdisciplinary fields intersecting machine learning and communication.

PERSONAL WEBSITE

https://yokoxue.github.io/

EMAIL

yokoxue@sribd.cn

EDUCATION BACKGROUND

The Hong Kong University of Science and Technology, PhD

Southeast University​​​​​, ​​Bachelor

BIOGRAPHY

Dr. Xue Ye obtained his Bachelor's degree in Communication Engineering from Southeast University in 2017 and his PhD degree from the School of Electronic and Computer Engineering at the Hong Kong University of Science and Technology in 2022. In the same year, he joined the Shenzhen Research Institute of Big Data as a research scientist. Dr. Xue has been engaged in research in the interdisciplinary fields of non-convex optimization, high-dimensional statistics, machine learning, and communication networks for many years. He has published over ten academic papers as the first author in high-level international journals and conferences in the fields of artificial intelligence and wireless communication, collaborating closely with numerous renowned scholars both domestically and internationally. Currently, he is actively involved as a key member in national key R&D projects.

ACADEMIC PUBLICATIONS

  1. Y. Xue and V. Lau, “Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation” IEEE Transactions on Signal Processing, 2023
  2. Ye Xue; Vincent K. N. Lau ; Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method, IEEE Transactions on Neural Networks and Learning Systems, 2021, 1-15
  3. Ye Xue; Vincent K. N. Lau; Songfu Cai ; Efficient Sparse Coding Using Hierarchical Riemannian Pursuit, IEEE Transactions on Signal Processing, 2021, 69: 4069-4084
  4. Ye Xue; Yifei Shen; Vincent K. N. Lau; Jun Zhang; Khaled B. Letaief ; Blind Data Detection in Massive MIMO via ℓ₃-Norm Maximization Over the Stiefel Manifold, IEEE Transactions on Wireless Communications, 2021, 20(2): 1411-1424
  5. Y. Xue*, Y. Shen, J. Zhang, K. Letaief, and V. Lau, “Complete dictionary learning via ℓp-norm maximization,” Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:280-289, 2020. （Co-first Author）
  6. Ye Xue; Liqun Su; Vincent K. N. Lau ; FedOComp: Two-Timescale Online Gradient Compression for Over-the-Air Federated Learning, IEEE Internet of Things Journal, 2022
XUE, Ye

 POSITION/TITLE

Research Scientist 

RESEARCH FIELD

Machine learning, non-convex optimization, online learning, federated learning, applications of artificial intelligence in communication networks, digital twins, and other interdisciplinary fields intersecting machine learning and communication.

PERSONAL WEBSITE

https://yokoxue.github.io/

EMAIL

yokoxue@sribd.cn

EDUCATION BACKGROUND

The Hong Kong University of Science and Technology, PhD

Southeast University​​​​​, ​​Bachelor

BIOGRAPHY

Dr. Xue Ye obtained his Bachelor's degree in Communication Engineering from Southeast University in 2017 and his PhD degree from the School of Electronic and Computer Engineering at the Hong Kong University of Science and Technology in 2022. In the same year, he joined the Shenzhen Research Institute of Big Data as a research scientist. Dr. Xue has been engaged in research in the interdisciplinary fields of non-convex optimization, high-dimensional statistics, machine learning, and communication networks for many years. He has published over ten academic papers as the first author in high-level international journals and conferences in the fields of artificial intelligence and wireless communication, collaborating closely with numerous renowned scholars both domestically and internationally. Currently, he is actively involved as a key member in national key R&D projects.

ACADEMIC PUBLICATIONS

  1. Y. Xue and V. Lau, “Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation” IEEE Transactions on Signal Processing, 2023
  2. Ye Xue; Vincent K. N. Lau ; Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method, IEEE Transactions on Neural Networks and Learning Systems, 2021, 1-15
  3. Ye Xue; Vincent K. N. Lau; Songfu Cai ; Efficient Sparse Coding Using Hierarchical Riemannian Pursuit, IEEE Transactions on Signal Processing, 2021, 69: 4069-4084
  4. Ye Xue; Yifei Shen; Vincent K. N. Lau; Jun Zhang; Khaled B. Letaief ; Blind Data Detection in Massive MIMO via ℓ₃-Norm Maximization Over the Stiefel Manifold, IEEE Transactions on Wireless Communications, 2021, 20(2): 1411-1424
  5. Y. Xue*, Y. Shen, J. Zhang, K. Letaief, and V. Lau, “Complete dictionary learning via ℓp-norm maximization,” Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:280-289, 2020. （Co-first Author）
  6. Ye Xue; Liqun Su; Vincent K. N. Lau ; FedOComp: Two-Timescale Online Gradient Compression for Over-the-Air Federated Learning, IEEE Internet of Things Journal, 2022
CHAI, Shuqi

POSITION/TITLE

Research Scientist

EDUCATION BACKGROUND

Hongkong University of Science and Technology Electronic and Computer Engineering Ph.D.

Xi’an Jiaotong University  Information Engineering Bachelor's degree

EMAIL

schai@sribd.cn

RESEARCH FIELD

Wireless communication and networks, Reinforcement learning, UAV communication

MAJOR ACHIEVEMENTS/HONORS

2014-Hongkong Ph.D. Fellowship

BIOGRAPHY

Shuqi Chai received the B.Eng. degree in electronic and information engineering from Xi’an Jiaotong University, and she received the Ph.D. degree in electronic and computer engineering from Hongkong University of Science and Technology in Jan 2022. She is currently a Research Scientist with the Shenzhen Research Institute of Big Data. Her research interests include MDP, dynamic programming, reinforcement learning in wireless communication systems, UAV trajectory and communication design.

ACADEMIC PUBLICATIONS

1.Shuqi Chai, Vincent Lau, "Joint Rate and Power Optimization for Multimedia Streaming in Wireless Fading Channels via Parametric Policy Gradient", IEEE Transactions on Signal Processing, accepted.
2.Shuqi Chai, Vincent Lau "Online Trajectory and Radio Resource Optimization of Cached-Enabled UAV Wireless Networks with Content and Energy Recharging", IEEE Transactions on Signal Processing, accepted.
3.Shuqi Chai, Vincent Lau "Multi-UAV Trajectory and Power Optimization for Cached UAV Wireless Networks with Energy and Content Recharging - Demand Driven Deep Learning Approach" , IEEE JSAC SI-UAV-B5G 2021, accepted.
4.Shuqi Chai, Vincent Lau "Mixed-Timescale Request-Driven User Association, Trajectory and Radio Resource Control for Cache-Enabled Multi-UAV Networks", IEEE Transactions on Signal Processing, accepted.
5.Shuqi Chai, Vincent Lau "Online Trajectory and Radio Resource Optimization for Cache-enabled Multi-UAV Networks", IEEE ICC'21, accepted.

 

CHAI, Shuqi

POSITION/TITLE

Research Scientist

EDUCATION BACKGROUND

Hongkong University of Science and Technology Electronic and Computer Engineering Ph.D.

Xi’an Jiaotong University  Information Engineering Bachelor's degree

EMAIL

schai@sribd.cn

RESEARCH FIELD

Wireless communication and networks, Reinforcement learning, UAV communication

MAJOR ACHIEVEMENTS/HONORS

2014-Hongkong Ph.D. Fellowship

BIOGRAPHY

Shuqi Chai received the B.Eng. degree in electronic and information engineering from Xi’an Jiaotong University, and she received the Ph.D. degree in electronic and computer engineering from Hongkong University of Science and Technology in Jan 2022. She is currently a Research Scientist with the Shenzhen Research Institute of Big Data. Her research interests include MDP, dynamic programming, reinforcement learning in wireless communication systems, UAV trajectory and communication design.

ACADEMIC PUBLICATIONS

1.Shuqi Chai, Vincent Lau, "Joint Rate and Power Optimization for Multimedia Streaming in Wireless Fading Channels via Parametric Policy Gradient", IEEE Transactions on Signal Processing, accepted.
2.Shuqi Chai, Vincent Lau "Online Trajectory and Radio Resource Optimization of Cached-Enabled UAV Wireless Networks with Content and Energy Recharging", IEEE Transactions on Signal Processing, accepted.
3.Shuqi Chai, Vincent Lau "Multi-UAV Trajectory and Power Optimization for Cached UAV Wireless Networks with Energy and Content Recharging - Demand Driven Deep Learning Approach" , IEEE JSAC SI-UAV-B5G 2021, accepted.
4.Shuqi Chai, Vincent Lau "Mixed-Timescale Request-Driven User Association, Trajectory and Radio Resource Control for Cache-Enabled Multi-UAV Networks", IEEE Transactions on Signal Processing, accepted.
5.Shuqi Chai, Vincent Lau "Online Trajectory and Radio Resource Optimization for Cache-enabled Multi-UAV Networks", IEEE ICC'21, accepted.