职称:助理研究员
所在单位:计算机学院
职务:助理研究员
学历:博士研究生毕业
在职信息:在职
个人简介:
本人现为重庆大学计算机学院助理研究员,博士毕业于意大利特伦托大学(University of Trento),师从欧洲科学院院士 Fausto Giunchiglia 教授。在博士阶段,长期聚焦情境感知相关研究,具体研究方向为用户情境识别,重点解决 mobile crowd sensing 中的用户错误标记问题,已在 Ubicomp 等国内外知名期刊和会议上发表学术论文。目前,我的研究工作以博士期间成果为基础进行延续拓展,开展城市计算、大规模时空数据分析相关研究,把研究目标从对小规模个人行为情境感知扩展至大规模城市乘客出行情境感知及语义挖掘。相应的研究成果涵盖移动出行数据质量增强、多源数据融合及表示学习、移动出行预测、移动出行语义理解等方面。
研究领域:
Context Recognition,
Urban Computing,
Trajectory Data Mining
发表论文:
[1] Zhang W, Meng F, Liao C, et al. Enabling Smart Mobility for People and Beyond With Heterogeneous Trajectory Data[J]. IT Professional, 2024, 26(5): 71-78.
[2] Liao C, Chen C, Zhang W, et al. AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain Adaptation[J]. ACM Transactions on Knowledge Discovery from Data, 2024, 18(8): 1-25.
[3] Deng M, Zhang W, Zhao J, et al. A Novel Framework for Joint Learning of City Region Partition and Representation[J]. ACM Transactions on Multimedia Computing, Communications and Applications, 2024, 20(7): 1-23.
[4] Chen C X, Zhang W, Yu B, et al. SAMLink: a mobility signature augmentation model for trajectory-user linking[J]. Neural Computing and Applications, 2023, 35(34): 24473-24491.
[5] Zhao J, Chen C, Zhang W, et al. Coupling makes better: an intertwined neural network for taxi and ridesourcing demand co-prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2023.
[6] Zeni M, Zhang W, Bignotti E, et al. Fixing mislabeling by human annotators leveraging conflict resolution and prior knowledge[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2019, 3(1): 1-23.
[7] Zhang W, Zeni M, Passerini A, et al. Skeptical Learning—An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition[J]. Algorithms, 2022, 15(4): 109.
[8] Zhang W, Shen Q, Teso S, et al. Putting human behavior predictability in context[J]. EPJ Data Science, 2021, 10(1): 42.
[9] Zhang W, Passerini A, Giunchiglia F. Dealing with mislabeling via interactive machine learning[J]. KI-Künstliche Intelligenz, 2020, 34(2): 271-278.