伍元凯 - 四川大学 - 计算机学院

个人简介

伍元凯，博士，四川大学计算机学院研究员。2019年于北京理工大学获博士学位，2019年到2022在麦吉尔大学进行博士后研究，主要研究方向是时空大数据分析、机器学习、智能网联交通系统、智慧城市。获加拿大数据价值化研究所（IVADO）博士后基金项目，已在计算机科学、交通工程等领域发表论文50余篇（IEEE TNNLS， IEEE TITS， IEEE TII，IEEE TVT，Transportation Research Part C（最高被引用论文）、AAAI会议，Google Scholar引用超1600次），申请三项美国专利、获批一项、获中国电子学会科技进步二等奖，目前作为审稿人服务于30余种SCI期刊，担任多个期刊的客座编辑。工作经历 2019.10-2022.02 麦吉尔大学博士后 2016-11-2017.12 威斯康辛大学麦迪逊分校访问学者教育背景 2015.09-2019.06 北京理工大学博士 2012.09-2015.03 北京理工大学硕士 2008.10-2012.06 上海海洋大学学士研究及参与项目 2022.03至今四川大学科研启动经费, (主持. 50 万元). 2020.02 — 2022.02 Ivado博士后基金, (主持. 140,000 加币), Deep Spatiotemporal Modeling for Urban Traffic Data 2019.12 — 2022.02 加拿大 Mitacs 基金会, 参与, Develop reinforcement learning platform for traffic signal control based on real-world traffic data and scenarios. 2021.04 — 2022.02 加拿大太空局地球系统数据分析项目, 参与, Dynamic flood inundation modelling in regional earth system models guide by space-based observations and machine learning. 2018.01 — 2019.08 国家自然科学基金, 国际 (地区) 合作与交流重点项目, 参与, 面向高维多源耦合大数据的多张量网络理论及其实证研究.

研究领域

机器学习、时空大数据分析、智能决策与控制

近期论文

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时空大数据分析方向 [1] Wu, Y., Zhuang, D., Labbe, A. and Sun, L., 2021, May. Inductive Graph Neural Networks for Spatiotemporal Kriging. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4478-4485).（CCF A类会议) [2] Zhang, H., Wu, Y.*, Tan, H., Dong, H., Ding, F. and Ran, B., 2020. Understanding and modeling urban mobility dynamics via disentangled representation learning. IEEE Transactions on Intelligent Transportation Systems.（中科院1区Top） [3] Wu, Y., Teufel, B., Sushama, L., Belair, S. and Sun, L., 2021. Deep Learning‐Based Super‐Resolution Climate Simulator‐Emulator Framework for Urban Heat Studies. Geophysical Research Letters, 48(19), p.e2021GL094737.（中科院2区Top） [4] Wu, Y., Tan, H., Qin, L., Ran, B. and Jiang, Z., 2018. A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, pp.166-180.（中科院1区Top，ESI高被引论文) [5] Wu, Y., Tan, H., Li, Y., Zhang, J. and Chen, X., 2018. A fused CP factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems, 30(3), pp.751-764.（中科院1区Top） [6] Wu, Y., Tan, H., Li, Y., Li, F. and He, H., 2017. Robust tensor decomposition based on Cauchy distribution and its applications. Neurocomputing, 223, pp.107-117.（中科院2区Top） [7] Tan, H., Wu, Y., Shen, B., Jin, P.J. and Ran, B., 2016. Short-term traffic prediction based on dynamic tensor completion. IEEE Transactions on Intelligent Transportation Systems, 17(8), pp.2123-2133.（中科院1区Top) 智能决策与控制方向 [1] Wang, Y., Tan, H., Wu, Y.* and Peng, J., 2020. Hybrid electric vehicle energy management with computer vision and deep reinforcement learning. IEEE Transactions on Industrial Informatics, 17(6), pp.3857-3868.（中科院1区Top） [2] Wu, Y., Tan, H., Qin, L. and Ran, B., 2020. Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm. Transportation research part C: emerging technologies, 117, p.102649.（中科院1区Top） [3] Lian, R., Tan, H., Peng, J., Li, Q. and Wu, Y.*, 2020. Cross-type transfer for deep reinforcement learning based hybrid electric vehicle energy management. IEEE Transactions on Vehicular Technology, 69(8), pp.8367-8380.（中科院2区Top) [4] Lian, R., Peng, J., Wu, Y.*, Tan, H. and Zhang, H., 2020. Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle. Energy, 197, p.117297.（中科院1区Top) [5] Wu, Y., Tan, H., Peng, J., Zhang, H. and He, H., 2019. Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus. Applied energy, 247, pp.454-466.（中科院1区Top，ESI高被引论文）