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魏新亮 Xinliang Wei

助理研究员
Assistant Professor
xl.wei@siat.ac.cn

Interests 研究方向

魏新亮，博士，助理研究员/博士后，分别于2014和2016获得中山大学学士和硕士学位，于2023年获得美国天普大学（Temple University）博士学位。主要研究方向包括边缘计算、机器学习、量子算法、量子强化学习等。近两年在IEEE TCC、JCST、iWQoS、ICPP、IPCCC、MASS等国际国内著名期刊和会议发表论文10余篇。担任JUSTC、IEEE TCC、IEEE TVT、IEEE INFOCOM、IEEE MASS、IEEE ICC等多个著名期刊和会议的审稿专家。担任国际学术会议IEEE MASS 2023 Web co-chairs以及TPC委员，BIGCOM 2024 TPC 委员。担任中国计算机学会（CCF）体系结构、量子计算专委委员，IEEE会员。2021年获得IEEE INFOCOM学生大会奖，2022年获得美国天普大学科技学院杰出研究助理奖，2023年获得美国天普大学计算机与信息科学系Scott Hibbs未来计算奖。

个人主页：https://greenhill520.github.io/brucewei.profile/

边缘计算、机器学习、量子算法、量子强化学习

参与多个国家及省市级科研项目，包括：

  1. 科技部国家重点研发计划，基于数字孪生的智慧防控通关系统构建及应用示范，参与

  2. 深圳市科创委基础研究重点项目，基于信创与数字对象架构(DOA)的数据要素与治理中间件平台核心技术研发，参与

  3. 深圳市基础研究面上项目，数据与硬件双重异构的联邦学习优化理论与方法，参与

  4. National Science Foundation, “AirEdge: Robust Airborne Network with Adaptive Edge Computing using Swarming UAV”, 参与

  5. National Science Foundation, “A Hybrid NVM based Computing Architecture for Machine Learning Applications”, 参与

  6. 佛山一门式法人数据库建设技术咨询项目，专项负责人

  7. 佛山禅城区大数据平台建设项目，课题骨干

  8. 广州团委智慧团建信息化规划项目，课题骨干

代表性论文：

  1. X. Wei, J. Liu, Y. Wang, “Joint Participant Selection and Learning Optimization for Federated Learning of Multiple Models in Edge Cloud”, Journal of Computer Science and Technology (JCST), 2023, 38(4): 754−772. DOI: 10.1007/s11390-023-3074-4. (CCF计算领域高质量科技期刊T1类)

  2. X. Wei, ABM M. Rahman, D. Cheng, Y. Wang, “Joint Optimization across Timescales: Resource Placement and Task Dispatching in Edge Clouds”, IEEE Transactions on Cloud Computing, 2023, 11(1), 730-744. DOI: 10.1109/TCC.2021.3113605. (JCR Q1)

  3. X. Wei and Y. Wang, “Popularity-based Data Placement with Load Balancing in Edge Computing”, IEEE Transactions on Cloud Computing, 2023, 11(1), 397-411. DOI: 10.1109/TCC.2021.3096467. (JCR Q1)

  4. X. Wei, L. Fan, Y. Guo, Y. Gong, Z. Han, Y. Wang, “Quantum Assisted Scheduling Algorithm for Federated Learning in Distributed Networks”, IEEE 32nd International Conference on Computer Communications and Networks (ICCCN), 2023. DOI: 10.1109/ICCCN58024.2023.10230094.

  5. X. Wei, J. Liu, X. Shi, Y. Wang. “Participant Selection for Hierarchical Federated Learning in Edge Clouds”, in 16th IEEE International Conference on Networking, Architecture, and Storage (NAS), 2022. DOI: 10.1109/NAS55553.2022.9925313.

  6. X. Wei, J. Liu, Y. Wang, “Joint Participant Selection and Learning Scheduling for Multi-Model Federated Edge Learning”, in 19th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), 2022. DOI: 10.1109/MASS56207.2022.00081.

  7. X. Wei, ABM M. Rahman, Y. Wang, “Data Placement Strategies for Data-Intensive Computing over Edge Clouds”, IEEE 40th International Performance Computing and Communications Conference (IPCCC), 2021. DOI: 10.1109/IPCCC51483.2021.9679438.

  8. X. Wei and Y. Wang, “Joint Resource Placement and Task Dispatching in Mobile Edge Computing across Timescales”, IEEE 29th International Workshop on Quality of Service (iWQoS), 2021. DOI: 10.1109/IWQOS52092.2021.9521283. (CCF B, acceptance rate=25%)

  9. J. Liu, X. Wei, H. Gao, Y. Wang, “Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling”, The International Conference on Parallel Processing (ICPP), 2023. (CCF B)

Introduction 简介

魏新亮，博士，助理研究员/博士后，分别于2014和2016获得中山大学学士和硕士学位，于2023年获得美国天普大学（Temple University）博士学位。主要研究方向包括边缘计算、机器学习、量子算法、量子强化学习等。近两年在IEEE TCC、JCST、iWQoS、ICPP、IPCCC、MASS等国际国内著名期刊和会议发表论文10余篇。担任JUSTC、IEEE TCC、IEEE TVT、IEEE INFOCOM、IEEE MASS、IEEE ICC等多个著名期刊和会议的审稿专家。担任国际学术会议IEEE MASS 2023 Web co-chairs以及TPC委员，BIGCOM 2024 TPC 委员。担任中国计算机学会（CCF）体系结构、量子计算专委委员，IEEE会员。2021年获得IEEE INFOCOM学生大会奖，2022年获得美国天普大学科技学院杰出研究助理奖，2023年获得美国天普大学计算机与信息科学系Scott Hibbs未来计算奖。

个人主页：https://greenhill520.github.io/brucewei.profile/

边缘计算、机器学习、量子算法、量子强化学习

参与多个国家及省市级科研项目，包括：

  1. 科技部国家重点研发计划，基于数字孪生的智慧防控通关系统构建及应用示范，参与

  2. 深圳市科创委基础研究重点项目，基于信创与数字对象架构(DOA)的数据要素与治理中间件平台核心技术研发，参与

  3. 深圳市基础研究面上项目，数据与硬件双重异构的联邦学习优化理论与方法，参与

  4. National Science Foundation, “AirEdge: Robust Airborne Network with Adaptive Edge Computing using Swarming UAV”, 参与

  5. National Science Foundation, “A Hybrid NVM based Computing Architecture for Machine Learning Applications”, 参与

  6. 佛山一门式法人数据库建设技术咨询项目，专项负责人

  7. 佛山禅城区大数据平台建设项目，课题骨干

  8. 广州团委智慧团建信息化规划项目，课题骨干

代表性论文：

  1. X. Wei, J. Liu, Y. Wang, “Joint Participant Selection and Learning Optimization for Federated Learning of Multiple Models in Edge Cloud”, Journal of Computer Science and Technology (JCST), 2023, 38(4): 754−772. DOI: 10.1007/s11390-023-3074-4. (CCF计算领域高质量科技期刊T1类)

  2. X. Wei, ABM M. Rahman, D. Cheng, Y. Wang, “Joint Optimization across Timescales: Resource Placement and Task Dispatching in Edge Clouds”, IEEE Transactions on Cloud Computing, 2023, 11(1), 730-744. DOI: 10.1109/TCC.2021.3113605. (JCR Q1)

  3. X. Wei and Y. Wang, “Popularity-based Data Placement with Load Balancing in Edge Computing”, IEEE Transactions on Cloud Computing, 2023, 11(1), 397-411. DOI: 10.1109/TCC.2021.3096467. (JCR Q1)

  4. X. Wei, L. Fan, Y. Guo, Y. Gong, Z. Han, Y. Wang, “Quantum Assisted Scheduling Algorithm for Federated Learning in Distributed Networks”, IEEE 32nd International Conference on Computer Communications and Networks (ICCCN), 2023. DOI: 10.1109/ICCCN58024.2023.10230094.

  5. X. Wei, J. Liu, X. Shi, Y. Wang. “Participant Selection for Hierarchical Federated Learning in Edge Clouds”, in 16th IEEE International Conference on Networking, Architecture, and Storage (NAS), 2022. DOI: 10.1109/NAS55553.2022.9925313.

  6. X. Wei, J. Liu, Y. Wang, “Joint Participant Selection and Learning Scheduling for Multi-Model Federated Edge Learning”, in 19th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), 2022. DOI: 10.1109/MASS56207.2022.00081.

  7. X. Wei, ABM M. Rahman, Y. Wang, “Data Placement Strategies for Data-Intensive Computing over Edge Clouds”, IEEE 40th International Performance Computing and Communications Conference (IPCCC), 2021. DOI: 10.1109/IPCCC51483.2021.9679438.

  8. X. Wei and Y. Wang, “Joint Resource Placement and Task Dispatching in Mobile Edge Computing across Timescales”, IEEE 29th International Workshop on Quality of Service (iWQoS), 2021. DOI: 10.1109/IWQOS52092.2021.9521283. (CCF B, acceptance rate=25%)

  9. J. Liu, X. Wei, H. Gao, Y. Wang, “Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling”, The International Conference on Parallel Processing (ICPP), 2023. (CCF B)

Research 科研

魏新亮，博士，助理研究员/博士后，分别于2014和2016获得中山大学学士和硕士学位，于2023年获得美国天普大学（Temple University）博士学位。主要研究方向包括边缘计算、机器学习、量子算法、量子强化学习等。近两年在IEEE TCC、JCST、iWQoS、ICPP、IPCCC、MASS等国际国内著名期刊和会议发表论文10余篇。担任JUSTC、IEEE TCC、IEEE TVT、IEEE INFOCOM、IEEE MASS、IEEE ICC等多个著名期刊和会议的审稿专家。担任国际学术会议IEEE MASS 2023 Web co-chairs以及TPC委员，BIGCOM 2024 TPC 委员。担任中国计算机学会（CCF）体系结构、量子计算专委委员，IEEE会员。2021年获得IEEE INFOCOM学生大会奖，2022年获得美国天普大学科技学院杰出研究助理奖，2023年获得美国天普大学计算机与信息科学系Scott Hibbs未来计算奖。

个人主页：https://greenhill520.github.io/brucewei.profile/

边缘计算、机器学习、量子算法、量子强化学习

参与多个国家及省市级科研项目，包括：

  1. 科技部国家重点研发计划，基于数字孪生的智慧防控通关系统构建及应用示范，参与

  2. 深圳市科创委基础研究重点项目，基于信创与数字对象架构(DOA)的数据要素与治理中间件平台核心技术研发，参与

  3. 深圳市基础研究面上项目，数据与硬件双重异构的联邦学习优化理论与方法，参与

  4. National Science Foundation, “AirEdge: Robust Airborne Network with Adaptive Edge Computing using Swarming UAV”, 参与

  5. National Science Foundation, “A Hybrid NVM based Computing Architecture for Machine Learning Applications”, 参与

  6. 佛山一门式法人数据库建设技术咨询项目，专项负责人

  7. 佛山禅城区大数据平台建设项目，课题骨干

  8. 广州团委智慧团建信息化规划项目，课题骨干

代表性论文：

  1. X. Wei, J. Liu, Y. Wang, “Joint Participant Selection and Learning Optimization for Federated Learning of Multiple Models in Edge Cloud”, Journal of Computer Science and Technology (JCST), 2023, 38(4): 754−772. DOI: 10.1007/s11390-023-3074-4. (CCF计算领域高质量科技期刊T1类)

  2. X. Wei, ABM M. Rahman, D. Cheng, Y. Wang, “Joint Optimization across Timescales: Resource Placement and Task Dispatching in Edge Clouds”, IEEE Transactions on Cloud Computing, 2023, 11(1), 730-744. DOI: 10.1109/TCC.2021.3113605. (JCR Q1)

  3. X. Wei and Y. Wang, “Popularity-based Data Placement with Load Balancing in Edge Computing”, IEEE Transactions on Cloud Computing, 2023, 11(1), 397-411. DOI: 10.1109/TCC.2021.3096467. (JCR Q1)

  4. X. Wei, L. Fan, Y. Guo, Y. Gong, Z. Han, Y. Wang, “Quantum Assisted Scheduling Algorithm for Federated Learning in Distributed Networks”, IEEE 32nd International Conference on Computer Communications and Networks (ICCCN), 2023. DOI: 10.1109/ICCCN58024.2023.10230094.

  5. X. Wei, J. Liu, X. Shi, Y. Wang. “Participant Selection for Hierarchical Federated Learning in Edge Clouds”, in 16th IEEE International Conference on Networking, Architecture, and Storage (NAS), 2022. DOI: 10.1109/NAS55553.2022.9925313.

  6. X. Wei, J. Liu, Y. Wang, “Joint Participant Selection and Learning Scheduling for Multi-Model Federated Edge Learning”, in 19th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), 2022. DOI: 10.1109/MASS56207.2022.00081.

  7. X. Wei, ABM M. Rahman, Y. Wang, “Data Placement Strategies for Data-Intensive Computing over Edge Clouds”, IEEE 40th International Performance Computing and Communications Conference (IPCCC), 2021. DOI: 10.1109/IPCCC51483.2021.9679438.

  8. X. Wei and Y. Wang, “Joint Resource Placement and Task Dispatching in Mobile Edge Computing across Timescales”, IEEE 29th International Workshop on Quality of Service (iWQoS), 2021. DOI: 10.1109/IWQOS52092.2021.9521283. (CCF B, acceptance rate=25%)

  9. J. Liu, X. Wei, H. Gao, Y. Wang, “Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling”, The International Conference on Parallel Processing (ICPP), 2023. (CCF B)

Publications 研究成果

魏新亮，博士，助理研究员/博士后，分别于2014和2016获得中山大学学士和硕士学位，于2023年获得美国天普大学（Temple University）博士学位。主要研究方向包括边缘计算、机器学习、量子算法、量子强化学习等。近两年在IEEE TCC、JCST、iWQoS、ICPP、IPCCC、MASS等国际国内著名期刊和会议发表论文10余篇。担任JUSTC、IEEE TCC、IEEE TVT、IEEE INFOCOM、IEEE MASS、IEEE ICC等多个著名期刊和会议的审稿专家。担任国际学术会议IEEE MASS 2023 Web co-chairs以及TPC委员，BIGCOM 2024 TPC 委员。担任中国计算机学会（CCF）体系结构、量子计算专委委员，IEEE会员。2021年获得IEEE INFOCOM学生大会奖，2022年获得美国天普大学科技学院杰出研究助理奖，2023年获得美国天普大学计算机与信息科学系Scott Hibbs未来计算奖。

个人主页：https://greenhill520.github.io/brucewei.profile/

边缘计算、机器学习、量子算法、量子强化学习

参与多个国家及省市级科研项目，包括：

  1. 科技部国家重点研发计划，基于数字孪生的智慧防控通关系统构建及应用示范，参与

  2. 深圳市科创委基础研究重点项目，基于信创与数字对象架构(DOA)的数据要素与治理中间件平台核心技术研发，参与

  3. 深圳市基础研究面上项目，数据与硬件双重异构的联邦学习优化理论与方法，参与

  4. National Science Foundation, “AirEdge: Robust Airborne Network with Adaptive Edge Computing using Swarming UAV”, 参与

  5. National Science Foundation, “A Hybrid NVM based Computing Architecture for Machine Learning Applications”, 参与

  6. 佛山一门式法人数据库建设技术咨询项目，专项负责人

  7. 佛山禅城区大数据平台建设项目，课题骨干

  8. 广州团委智慧团建信息化规划项目，课题骨干

代表性论文：

  1. X. Wei, J. Liu, Y. Wang, “Joint Participant Selection and Learning Optimization for Federated Learning of Multiple Models in Edge Cloud”, Journal of Computer Science and Technology (JCST), 2023, 38(4): 754−772. DOI: 10.1007/s11390-023-3074-4. (CCF计算领域高质量科技期刊T1类)

  2. X. Wei, ABM M. Rahman, D. Cheng, Y. Wang, “Joint Optimization across Timescales: Resource Placement and Task Dispatching in Edge Clouds”, IEEE Transactions on Cloud Computing, 2023, 11(1), 730-744. DOI: 10.1109/TCC.2021.3113605. (JCR Q1)

  3. X. Wei and Y. Wang, “Popularity-based Data Placement with Load Balancing in Edge Computing”, IEEE Transactions on Cloud Computing, 2023, 11(1), 397-411. DOI: 10.1109/TCC.2021.3096467. (JCR Q1)

  4. X. Wei, L. Fan, Y. Guo, Y. Gong, Z. Han, Y. Wang, “Quantum Assisted Scheduling Algorithm for Federated Learning in Distributed Networks”, IEEE 32nd International Conference on Computer Communications and Networks (ICCCN), 2023. DOI: 10.1109/ICCCN58024.2023.10230094.

  5. X. Wei, J. Liu, X. Shi, Y. Wang. “Participant Selection for Hierarchical Federated Learning in Edge Clouds”, in 16th IEEE International Conference on Networking, Architecture, and Storage (NAS), 2022. DOI: 10.1109/NAS55553.2022.9925313.

  6. X. Wei, J. Liu, Y. Wang, “Joint Participant Selection and Learning Scheduling for Multi-Model Federated Edge Learning”, in 19th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), 2022. DOI: 10.1109/MASS56207.2022.00081.

  7. X. Wei, ABM M. Rahman, Y. Wang, “Data Placement Strategies for Data-Intensive Computing over Edge Clouds”, IEEE 40th International Performance Computing and Communications Conference (IPCCC), 2021. DOI: 10.1109/IPCCC51483.2021.9679438.

  8. X. Wei and Y. Wang, “Joint Resource Placement and Task Dispatching in Mobile Edge Computing across Timescales”, IEEE 29th International Workshop on Quality of Service (iWQoS), 2021. DOI: 10.1109/IWQOS52092.2021.9521283. (CCF B, acceptance rate=25%)

  9. J. Liu, X. Wei, H. Gao, Y. Wang, “Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling”, The International Conference on Parallel Processing (ICPP), 2023. (CCF B)

中国科学院深圳先进技术研究院