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Wei Ye

Tenure-Track Professor, Ph.D.

Geometric machinE lEarning and Knowledge diScovery (GEEX) Research Group
College of Electronic and Information Engineering
Shanghai Research Institute for Autonomous Intelligent Systems
Tongji University, Shanghai 201804

Shanghai Innovation Institute, Shanghai 200231

yew@tongji.edu.cn
weiye@sii.edu.cn

Short Bio

I am a tenure-track professor with the College of Electronic and Information Engineering at Tongji University. I worked as a postdoctoral researcher with Prof. Ambuj Singh on Human-AI team decision making and network science at University of California, Santa Barbara. Before that, I was a researcher with the AI Platform Department at Tencent, working on the game AI. I did my PhD at Ludwig-Maximilian University of Munich, where I was fortunately advised by Prof. Christian Böhm. I was a visiting PhD student with the iKDD research group at Helmholtz Zentrum Munich, where I worked with Prof. Claudia Plant.

Prospective Students

I am always looking for self-motivated (Definitions from Oxford Languages: motivated to do or achieve something because of one's own enthusiasm or interest, without needing pressure from others.) students. If you are interested in my research and have a related background, please feel free to contact me.

Research

My research mainly focuses on representation learning and its applications in clustering, classification, generative AI, and human-AI/robot collaboration.

Selected Publications (ranked according to CCF):

  • Geometry Awakening: Cross-Geometry Learning Exhibits Superiority over Individual Structures
    Yadong Sun, Xiaofeng Cao, Yu Wang, Wei Ye, Jingcai Guo, Qing Guo
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2024. (CCF: A)

  • DP-VINS: Dynamics Adaptive Plane-Based Visual-Inertial SLAM for Autonomous Vehicles
    Linchuan Zhang, Huilin Yin, Wei Ye, Johannes Betz
    IEEE Transactions on Instrumentation and Measurement, 2024.

  • Incorporating Heterophily into Graph Neural Networks for Graph Classification
    Jiayi Yang, Sourav Medya, Wei Ye
    IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2024. (CCF: C)

  • Deep Spectral Clustering via Joint Spectral Embedding and Kmeans
    Wengang Guo, Wei Ye
    IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2024. (CCF: C)

  • Enhancing Shortest-Path Graph Kernels via Graph Augmentation
    Wei Ye, Hao Tian, Shuhao Tang, Xin Sun
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2024. (CCF: B)

  • Deep Hierarchical Graph Alignment Kernels
    Shuhao Tang, Hao Tian, Xiaofeng Cao, Wei Ye
    International Joint Conference on Artificial Intelligence (IJCAI), 2024. (CCF: A)

  • Loosely Coupled Stereo VINS Based on Point-Line Features Tracking with Feedback Loops
    Linchuan Zhang, Wei Ye, Jun Yan, Hao Zhang, Johannes Betz, Huilin Yin
    IEEE Transactions on Vehicular Technology (TVT), 2024.

  • SG-RoadSeg: End-to-End Collision-Free Space Detection Sharing Encoder Representations Jointly Learned via Unsupervised Deep Stereo
    Zhiyuan Wu, Jiaqi Li, Yi Feng, Chengju Liu, Wei Ye, Qijun Chen, Rui Fan
    IEEE International Conference on Robotics and Automation (ICRA), 2024. (CCF: B)

  • PICNN: A Pathway towards Interpretable Convolutional Neural Networks
    Wengang Guo, Jiayi Yang, Huilin Yin, Qijun Chen, Wei Ye
    AAAI Conference on Artificial Intelligence (AAAI), 2024. (CCF: A)

  • COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
    Hao Tian, Sourav Medya, Wei Ye
    AAAI Conference on Artificial Intelligence (AAAI), 2024. (CCF: A)

  • Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification
    Wei Ye, Hao Tian, Qijun Chen
    IEEE Transactions on Artificial Intelligence (TAI), 2023.

  • Review-Enhanced Sequential Recommendation with Self-Attention and Graph Collaborative Features
    Yunqi Hong, Wei Ye
    IEEE International Conference on Data Mining Workshops (ICDMW), 2023. (CCF: B)

  • Intelligent Upgrade of Waste-activated Sludge Dewatering Process Based on Artificial Neural Network Model: Core Influential Factor Identification and Non-experimental Prediction of Sludge Dewatering Performance
    Hewei Li, Chunjiang Li, Kun Zhou, Wei Ye, Yufei Lu, Xiaoli Chai, Xiaohu Dai, Boran Wu
    Journal of Environmental Management, 2023.

  • Learning Deep Graph Representations via Convolutional Neural Networks (Extended abstract)
    Wei Ye, Omid Askarisichani, Alex Jones, Ambuj Singh
    IEEE International Conference on Data Engineering (ICDE), 2023. (CCF: A)

  • Incorporating User's Preference into Attributed Graph Clustering (Extended abstract)
    Wei Ye, Dominik Mautz, Christian Böhm, Ambuj Singh, Claudia Plant
    IEEE International Conference on Data Engineering (ICDE), 2023. (CCF: A)

  • Graph Neural Diffusion Networks for Semi-supervised Learning
    Wei Ye, Zexi Huang, Yunqi Hong, Ambuj Singh
    arXiv, 2022.

  • Modeling Human-AI Team Decision Making
    Wei Ye, Francesco Bullo, Noah Friedkin, Ambuj K Singh
    arXiv, 2022.

  • Deep Embedded K-Means Clustering
    Wengang Guo, Kaiyan Lin, Wei Ye
    IEEE International Conference on Data Mining Workshops (ICDMW), 2021. (CCF: B)

  • Learning Deep Graph Representations via Convolutional Neural Networks
    Wei Ye, Omid Askarisichani, Alex Jones, Ambuj Singh
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. (CCF: A)

  • Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans
    Dominik Mautz, Wei Ye, Claudia Plant, Christian Böhm
    ACM Transactions on Knowledge Discovery from Data (TKDD), 2020. (CCF: B)

  • Incorporating User's Preference into Attributed Graph Clustering
    Wei Ye, Dominik Mautz, Christian Böhm, Ambuj Singh, Claudia Plant
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 2020. (CCF: A)

  • Tree++: Truncated Tree Based Graph Kernels
    Wei Ye, Zhen Wang, Rachel Redberg, Ambuj Singh
    IEEE Transactions on Knowledge Discovery and Data Engineering (TKDE), 2019. (CCF: A)

  • Discovering Non-Redundant K-means Clusterings in Optimal Subspaces
    Dominik Mautz, Wei Ye, Claudia Plant, Christian Böhm
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018. (CCF: A)

  • Learning from Labeled and Unlabeled Vertices in Networks
    Wei Ye, Linfei Zhou, Dominik Mautz, Claudia Plant, Christian Böhm
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. (CCF: A)

  • Towards an Optimal Subspace for KMeans
    Dominik Mautz, Wei Ye, Claudia Plant, Christian Böhm
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. (CCF: A)

  • Attributed Graph Clustering with Unimodal Normalized Cut
    Wei Ye, Linfei Zhou, Xin Sun, Claudia Plant, Christian Böhm
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery , ml, 2017. (CCF: B)

  • A Knowledge Discovery of Complex Data using Gaussian Mixture Models
    Linfei Zhou, Wei Ye, Claudia Plant, Christian Böhm
    International Conference on Big Data Analytics and Knowledge Discovery (DaWaK), 2017. (CCF: C)

  • Novel Indexing Strategy and Similarity Measures for Gaussian Mixture Models
    Linfei Zhou, Wei Ye, Bianca Wackersreuther, Claudia Plant, Christian Böhm
    International Conference on Database and Expert Systems Applications (DEXA), 2017. (CCF: C)

  • Indexing Multiple-instance Objects
    Linfei Zhou, Wei Ye, Zhen Wang, Claudia Plant, Christian Böhm
    International Conference on Database and Expert Systems Applications (DEXA), 2017. (CCF: C)

  • Generalized Independent Subspace Clustering
    Wei Ye, Samuel Maurus, Nina Hubig, Claudia Plant
    IEEE International Conference on Data Mining (ICDM), 2016. (CCF: B)

  • FUSE: Full Spectral Clustering
    Wei Ye, Sebastian Goebl, Claudia Plant, Christian Böhm
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016. (CCF: A)

  • IDEA: Integrative Detection of Early-stage Alzheimer’s Disease
    Wei Ye, Bianca Wackersreuther, Christian Böhm, Michael Ewers, Claudia Plant
    5th Workshop on Data Mining for Medicine and Healthcare, SIAM International Conference on Data Mining (SDM), 2016. (CCF: B)

  • A Human Learning Optimization Algorithm and Its Application to Multi-dimensional Knapsack Problems
    Ling Wang, Ruixin Yang, Haoqi Ni, Wei Ye, Minrui Fei, Panos M Pardalos
    Applied Soft Computing, 2015.

  • A Simple Human Learning Optimization Algorithm
    Ling Wang, Haoqi Ni, Ruixin Yang, Minrui Fei, Wei Ye
    International Conference on Life System Modeling and Simulation, 2014.

  • A Modified Multi-objective Binary Particle Swarm Optimization Algorithm
    Ling Wang, Wei Ye, Xiping Fu, Muhammad Ilyas Menhas
    International Conference on Swarm Intelligence, 2011.
    • Projects

  • Research on the Representational Power of Graph Neural Networks, 570,000¥, National Natural Science Foundation of China (NSFC), 2022/01-2025/12, Principal Investigator.

  • Study on the Message-passing Mechanism of Homophilous Graph Neural Networks, 200,000¥, The Fundamental Research Funds for the Central Universities, 2023/01-2023/11, Principal Investigator.

  • Study on Unsupervised Representation Learning by Autoencoders, 240,000¥, The Fundamental Research Funds for the Central Universities, 2022/01-2022/11, Principal Investigator.

  • Study on Deep Multiview Graph Clustering, 320,000¥, The Fundamental Research Funds for the Central Universities, 2021/01-2021/11, Principal Investigator.
    • Courses

    2023-2024 Spring semester: Undergraduate course, Machine Learning, Tuesday 8:00-9:35 and Thursday 10:00-11:35, A406.

    2022-2023 Spring semester: Undergraduate course, Machine Learning, Tuesday 8:00-9:35 and Thursday 10:00-11:35, B113.

    2022-2023 Fall semester: Undergraduate course, Deep Learning, Thursday 15:30-17:05, C413.

    2023-2024 Fall semester: Graduate course, Pattern Recognition, Monday 8:50-11:35, G110.

    2022-2023 Fall semester: Graduate course, Pattern Recognition, Monday 8:50-11:35, G110.

    Service

    Reviewers for Journals: ACM TKDD, ACM TIST, IEEE TKDE, IEEE TNNLS, IEEE TBD, IEEE TSMC: Systems, ECML-PKDD Journal, Information Sciences, Pattern Recognition, and Applied Soft Computing.

    External Reviewers for Conferences: ICLR 22, ICDE 20, ICDM 20, IJCAI 20, IJCAI 19, WWW 19, CIKM 17, IEEE BigData 15, KDD 20, KDD 19, KDD 18, KDD 17, KDD 16, KDD 15, KDD 14, VLDB.

    Program Committee Members of KDD 24, KDD 23, WSDM 23-24, TheWebConf (WWW) 22, AAAI 20-24, SDM 21-24, PAKDD 24.

    Senior Program Committee Member of AAAI 23, AAAI 24.

    Area Chair of ECML-PKDD 23, ECML-PKDD 24.

    Local Arrangement Chair of IEEE BigData Conference, 2019.

    Awards

    PhD Dissertation, Data Mining Using Concepts of Independence, Unimodality and Homophily, Ludwig-Maximilian University of Munich, "Magna Cum Laude", 2018.

    KDD 2017 Student Travel Award, ACM SIGKDD and NSF, 2017.

    ACM student scholarship (for only 10 students from KDD 2017) for the 50th celebration of Turing Awards.

    DAAD Conference Travel Award, German Academic Exchange Service (DAAD), 2016.

    ICDM 2016 Student Travel Award, IEEE TCII, 2016.

    KDD 2016 Student Travel Award, ACM SIGKDD and NSF, 2016

    Excellent Master Degree Thesis, Committee of Education, Shanghai, 2015.

    National Scholarship, Ministry of Education, China, 2012.

    National Scholarship for Encouragement, Ministry of Education, China, 2007-2009.

    Students

    Xing Wei, PhD student, 2023-

    Chunchun Chen, PhD student, 2023-

    Wengang Guo, PhD student, 2022-

    Jiayi Yang, PhD student, 2021-

    Chenrun Wang, Master student, 2023-

    Shuhao Tang, Master student, 2022-

    Hao Tian, Master student, 2021-

    Nihar Hegde, Reducing Noise in Power Iteration Clustering through L1-Regularized Autoencoders, UCSB Research Mentorship Program, 2019.

    John Piwinski, On the Application of Breadth-First Search Trees in Graph Kernels, UCSB Research Mentorship Program, 2019.

    Sai Nikhil Maram, Relating Network Structure to Performance, Master Thesis, University of California, Santa Barbara, 2019.

    Matthias Wolfgang Mueller, Integrating PCA and MRMR into the IDEA Framework, Master Thesis, Ludwig-Maximilian University of Munich, 2018.

    Yekaterina Synilo, Automated and Efficient Diagnosis of Early Stages of Alzheimer's Disease, Bachelor Thesis, Ludwig-Maximilian University of Munich, 2017.