个人简历
万林,男,博士,副教授,硕士生导师, 中国计算机学会(CCF)、中国人工智能学会(CAAI)、中国图象图形学学会(CSIG)会员。2002年本科毕业于武汉理工大学计算机科学与技术专业,2009年硕士毕业于中国地质大学计算机软件与理论专业,2012年6月博士毕业于中国地质大学信息工程学院,同年留校任教,2015年6月中国地质大学博士后流动站出站。主要研究方向为机器学习方法及应用。
Lin Wan received the B.E. degree in computer science from Wuhan University of Technology, Wuhan, China, in 2002, and the M.S. and Ph.D. degrees from China University of Geosciences, Wuhan, China, in 2009 and 2012, respectively. He presently holds the position of associate professor in the Department of Artificial Intelligence at the School of Computer Science, China University of Geosciences, Wuhan, China. His current research interests mainly include machine learning and computer vision.
研究兴趣(Research Interests)
计算机视觉领域中的行人再识别、人群分析等任务 Person Re-identification (ReID)、Crowd Analysis
时空数据挖掘 Spatio-temporal data mining (with Prof. Zhou Huang at PKU)
医学图像计算 Medical image computing (with Prof. Xuepeng Xiong at WHU)
科研项目(Research Grants)
2016.07-2019.02 北方重要盆地铀矿钻孔数据库管理系统研发,100万
2015.01-2017.12 国家自然科学基金项目(No. 41401449):云环境下协同式GIS工作流关键技术研究,25万
2015.11-2016.08 电网地理接线图制图综合工具研发,28万
2013.12-2015.05 土地资源产权产籍决策模拟系统研发,60万
2013.04-2014.03 CCOP秘书处管理信息系统研发,60万
近期论文(Recent Publications)
Chaoqun Rao, Lin Wan*. Spatial Exchanging Fusion Network for RGB-T Crowd Counting, Neurocomputing, 2024. https://doi.org/10.1016/j.neucom.2024.128433
Nai Yang, Zhitao Deng, Fangtai Hu, Yi Chao, Lin Wan*, Qingfeng Guan, Zhiwei Wei. Urban perception by using eye movement data on street view images, Transactions in GIS, 2024. https://doi.org/10.1111/tgis.13172
Lin Wan, Qianyan Jing, Zongyuan Sun, Chuang Zhang, Zhihang Li and Yehansen Chen. Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification, IEEE Transactions on Information Forensics and Security, 2023. (CCF-A) https://doi.org/10.1109/TIFS.2023.3273911
Lin Wan, Ganmin Yin, Jiahao Wang, Golan Ben-Dor, Aleksey Ogulenko, Zhou Huang. PATRIC: A high performance parallel urban transport simulation framework based on traffic clustering, Simulation Modelling Practice and Theory, 2023. https://doi.org/10.1016/j.simpat.2023.102775
Lin Wan, Zongyuan Sun, Qianyan Jing, Yehansen Chen, Lijing Lu, Zhihang Li. G2DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification, Pattern Recognition, 2022. https://doi.org/10.1016/j.patcog.2022.109150
Lin Wan, Han Wang, Yuming Hong, Ran Li, Wei Chen, and Zhou Huang. 2022. iTourSPOT: a context-aware framework for next POI recommendation in location-based social networks, International Journal of Digital Earth, 2022, 15(1): 1614-1636. https://doi.org/10.1080/17538947.2022.2122611
Yehansen Chen, Lin Wan, Zhihang Li, Qianyan Jing, and Zongyuan Sun. Neural Feature Search for RGB-Infrared Person Re-Identification, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Online, Jun. 19-25, 2021. pp. 587-597. (CCF-A) https://doi.org/10.1109/CVPR46437.2021.00065
A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study, EClinicalMedicine, 2020, 27: 100558. (柳叶刀子刊, 共同通讯) [ 检测软件: Video | 媒体报道: 长江网 武大新闻网 ] https://doi.org/10.1016/j.eclinm.2020.100558
Lin Wan, Yuming Hong, Zhou Huang, Xia Peng, and Ran Li. A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks, International Journal of Geographical Information Science, 2018, 32(11): 2225-2246. https://doi.org/10.1080/13658816.2018.1458988
Lin Wan, Zhou Huang, and Xia Peng. An effective NoSQL-based vector map tile management approach, ISPRS International Journal of Geo-Information, 2016, 5(11): 215. https://doi.org/10.3390/ijgi5110215
Zhou Huang, Yiran Chen, Lin Wan, and Xia Peng. GeoSpark SQL: An Effective Framework Enabling Spatial Queries on Spark, ISPRS International Journal of Geo-Information, 2017, 6(9): 285. https://doi.org/10.3390/ijgi6090285
Lin Wan, and Rongrong Ren. A VGI data Integration framework based on linked data model, In: Int'l Conference on Intelligent Earth Observing and Applications (IEOAs), Guilin, China, 2015. https://doi.org/10.1117/12.2211068
教学活动(Teaching)
Machine Learning (Spring 2023, 2024, 2025-)
Numerical Methods (Fall 2022, 2023, 2024-)
Principles of Operating System (Spring 2021, 2022)
Data Mining (Spring 2018, 2019, 2020, 2021, Fall 2021)
High-performance Computing (Fall 2018, 2019, 2020, Spring 2022)
Introduction to Database System (Fall 2016)
Linux Kernel Design (Fall 2014, 2015, 2016, 2017)
Software Testing (Spring 2016, 2017)
Modern Web Development (Spring 2015)
Principles of Geographic Information System (Spring 2013)
学生培养(Student recruitment)
目前在计算机学院招生,每年招收1~2名硕士研究生(学/专硕均可招收),欢迎优秀本科生来我组参加科研训练。近年来,指导研究生发表CCF-A类顶会论文,获国家奖学金、校科技论文报告会一等奖和校优秀硕士学位论文,连续3年指导本科生获校级优秀学位论文,多位学生保送至北京大学等高校或拿到大厂offer。欢迎学习态度端正、具备一定专业基础和动手能力的同学加入我组,培养学生注重口碑和质量,坚持以认真负责的态度,逐一关注和指导每一位同学,以夯实基础、提升科研品味和综合能力为目标,共同努力实现“双向奔赴”。缺乏专业兴趣和主动性的同学咱们就彼此放过吧!如若感兴趣,请将个人简历、本科成绩单、英语成绩单、获奖证书、代表作等发至本人邮箱:wanyixue {AT} gmail.com (type @ in place of {AT} when sending mail)
常用资源(Useful links)
Errata for DHS book 1 & 2 printings - R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd edition, Wiley-Interscience, 2004. ISBN 978-0-471-05669-3
Mathematics of machine learning, Chapters 5, 6, 7 are useful to understand vector calculus and continuous optimization. Note that there are lots of typos in the printed edition.
Mathematical Analysis of Machine Learning Algorithms, Cambridge University Press, 2023. ISBN 978-1-009-09838-0
Understanding Deep Learning, Errata, The MIT Press, 2023. ISBN 978-0-262-04864-4
Errata for An Introduction to Optimization, Fifth Edition. Wiley, 2023. ISBN 978-1-119-87763-9
Errata for Essentials of Pattern Recognition: An Accessible Approach, Cambridge University Press, 2020. ISBN 978-1-108-48346-9
Errata for Digital Image Processing, 4th edition. Rafael C. Gonzalez and Richard E. Woods, Pearson, 2017. ISBN 978-0-133-35672-4
Errata for Artificial Intelligence: A Modern Approach, 4th edition. Stuart Russell and Peter Norvig, Pearson, 2020. ISBN 978-0-134-61099-3
High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications, Cambridge University Press, 2022. ISBN 978-1-108-48973-7
More Math into LaTeX, 4th edition. Springer-Verlag, New York, 2007. ISBN 978-0-387-32289-6
PyTorch basics: jupyter notebook, generated pdf
Math Cheat Sheet (lots of useful formulas)
The Matrix Cookbook (2012)
Code for Numerical Methods Using MATLAB, 4th edition. John Mathews & Kurtis Fink, Pearson, 2004. ISBN 978-0-130-65248-5
Code for Numerical Analysis, 3rd edition. Timothy Sauer, Pearson, 2017. ISBN 978-0-134-69645-4
Review Papers on Statistical Pattern Recognition, Neural Networks and Learning
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