Teacher

梁浩健

about
梁浩健
中国科学院空天信息创新研究院

个人简介

梁浩健，博士，中国科学院空天信息创新研究院特别研究助理。研究方向主要为地理空间优化、深度强化学习、遥感大数据分析和数字地球综合应用等。具有优秀的数理逻辑、数学建模能力、编程能力和英文写作能力。熟练使用Python等开发工具，熟练掌握Pytorch等深度学习框架，熟悉Linux系统基本操作。

研究方向

  1. 地理空间优化
  2. 深度强化学习
  3. 遥感大数据分析和数字地球综合应用

教育经历

  • 2020.07-2024.06, 吉林大学 人工智能学院 数学 理学博士

  • 2017.09-2020.06, 吉林大学 数学学院 基础数学 理学硕士

  • 2013.09-2017.07, 吉林大学 数学学院 信息与计算科学 理学学士

从业经历

  • 2024.07至今， 中国科学院空天信息创新研究院 特别研究助理

研究成果

期刊论文
  1. Liang H, Wang S*, Li H, et al. Sponet: solve spatial optimization problem using deep reinforcement learning for urban spatial decision analysis[J]. International Journal of Digital Earth, 2024, 17(1): 2299211. (中科院1区, 第一作者)
  2. Liang H, Wang S*, Li H, et al. BiGNN: Bipartite graph neural network with attention mechanism for solving multiple traveling salesman problems in urban logistics[J]. International Journal of Applied Earth Observation and Geoinformation, 2024, 129: 103863. (中科院1区, 第一作者)
  3. Liang H, Wang S*, et al. DeepHullNet: A Deep Learning Approach for Solving the Convex Hull and Concave Hull Problems with Transformer[J], International Journal of Digital Earth. (中科院1区, 第一作者)
  4. Liang H, Wang S*, Li H, Ye H, Zhong Y. A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network[J]. ISPRS International Journal of Geo-Information, 2022, 11(5): 270. https://doi.org/10.3390/ijgi11050270 (中科院三区，第一作者)
  5. Zhong Y, Wang S*, Liang H, et al. ReCovNet: Reinforcement learning with covering information for solving maximal coverage billboards location problem[J]. International Journal of Applied Earth Observation and Geoinformation, 2024, 128: 103710. (中科院1区)
  6. Zhang Y, Wang S*, Liang H, et al. Dual hybrid frameworks combining graph convolutional network with decoding for covering location problem[J]. Iscience, 2024, 27(5). (Cell子刊)
  7. Chen X, Wang S.*, Li H, Liang H, Li Z, & Lu H. An Attention Model with Multiple Decoders for Solving p-Center Problems. International Journal of Applied Earth Observation and Geoinformation, 2023, 125: 103526.https://doi.org/10.1016/j.jag.2023.103526（中科院一区）代码链接：https://github.com/HIGISX/AMMD-PC
  8. Zhao J, Wang S*, Xie B, Wang Z, Liang H, & Lu H. Improved Benders Decomposition Algorithm for City Emergency Service Facility Location. Computers & Operations Research (审稿中) 代码链接：https://github.com/jinqiuzhao/IBD-of-p-center-and-p-median
  9. Chen X, Wang S*, Li H, Lyu F, Liang H, Zhang X, & Zhong Y. Ndist2vec: Node with Landmark and New Distance to Vector Method for Predicting Shortest Path Distance along Road Networks. ISPRS International Journal of Geo-Information, 2022, 11(10), 514. https://doi.org/10.3390/ijgi11100514 (SCI)
  10. Yue J, Long Y, Wang S*, Liang H. Optimization of Shared Electric Scooter Deployment Stations Based on Distance Tolerance[J]. ISPRS International Journal of Geo-Information, 2024, 13(5): 147. (中科院3区)
会议论文
  1. Liang H, Wang S*. A New Approach Based on Graph Neural Network for Solving p-center Problems. (Oral, GeoKG&GeoAI 2021)
  2. Liang H, Wang S*. A Graph Convolution Neural Network Methodology for Solving Traveling Salesman Problem. (Poster，spatialDI 2022)
  3. Wang S, Liang H*, Zhong Y, Zhang X, & Su C. DeepMCLP: Solving the MCLP with Deep Reinforcement Learning for Urban Facility Location Analytics. 2023 Symposium on Data Science and Statistics. https://doi.org/10.25436/E2KK5V
  4. Wang S, Liang H*, Zhong Y, Su C, Chen X, Zhou J. DeepSpo: A Deep Learning Framework for Solving Spatial Optimization Problems. (GeoAI 2023, 审稿中)
  5. Wang S, Zhou J, Liang H*, et al. A New Approach for Solving Location Routing Problems with Deep Reinforcement Learning of Emergency Medical Facility[C] Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS 2023. 2023: 50-53. https://doi.org/10.1145/3615884.3629429
  6. Wang S, Zhang Z, Su C, Zhou L, Liang H, Wang W. Spatial Optimization Site Selection of Beijing Cainiao Station Based on Multi-Source Geospatial Data. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geocomputational Analysis of Socio-Economic Data (GeoSocial '23). Association for Computing Machinery, New York, NY, USA, 20–23. https://doi.org/10.1145/3615892.3628479
  7. Wang S, Wang R, Su C, Zhou L, Wang W, and Liang H. Optimization of shared bicycle location in Wuhan city based on multi-source geospatial big data[C] Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications. 2023: 43-46. https://doi.org/10.1145/3615888.3627815