Professor Yingjie Tian has long been engaged in research in data science and artificial intelligence, focusing on the complexity and uncertainty issues in management decision-making. His work integrates big data mining, machine learning, optimization theory, and knowledge engineering methods in an interdisciplinary manner, forming a systematic and original research framework. He has published more than 200 papers in top-tier international journals in management science, economics, artificial intelligence, and machine learning, as well as at leading AI conferences such as ICML, AAAI, NeurIPS, IJCAI, and CVPR. He has also authored seven academic monographs, obtained four software copyrights, and filed three invention patents. His Google Scholar citations exceed 15,000, with an h-index of 47. He was selected as a Highly Ranked Scholar (Top 0.05% Global Lifetime Scientist) in 2024 and has been listed among the Top 2% Scientists Worldwide by Stanford University for five consecutive years (2021–2025). His research outcomes have been successfully applied in areas such as credit risk assessment and medical decision support. As a principal investigator, he has led and participated in more than ten major research projects, including the National Natural Science Foundation of China (General, Key, and Innovation Group Projects), Major International Cooperation Projects, Special Programs of the Ministry of Water Resources, Major Projects of other national ministries, and the Chinese Academy of Sciences’ Knowledge Innovation Program. He currently serves as Executive Editor-in-Chief of the Annals of Data Science (Springer), Associate Editor of Neurocomputing, and Editorial Board Member of Healthcare Science, Cancer Innovation, iLIVER, and iRADIOLOGY.
Education
l 2002-09--2005-06 China Agricultural University, Ph.D.
l 1994-09--1997-04 Beijing Institute of Technology, M.Sc.
l 1990-09--1994-07 Shandong Normal University , B.Sc.
Monographs
1. Naiyang Deng, Yingjie Tian. “New method in Data Mining: Support Vector Machines ” Science Press, 2004,6 Beijing China.
2. Naiyang Deng, Yingjie Tian. “Support Vector Machines --- Theory, Algorithms and Extensions:” Science Press, 2009,8 Beijing China.
3. Yong Shi, Yingjie Tian, et al. "Optimization Based Data Mining: Theory and Applications", Springer Press, 2011,5.
4. Naiyang Deng, Yingjie Tian, Chunhua Zhang. "Support Vector Machines---Optimiaztion based Theory, Algorithms and Extensions", CRC Press, 2012,12.
Selected Papers
1. Yingjie Tian, Haonan Wen, Kun Guo, Machine learning applications in climate finance: An overview, Research in International Business and Finance, 2025, 79: 103063.
2. Jianyu Miao, Xiaochan Zhang, Tiejun Yang, Chao Fan, Yingjie Tian, Yong Shi, Mingliang Xu, A Comprehensive Survey on Subspace Clustering: Methods and Applications, Artificial Intelligence Review, 2025, 58: 346.
3. Saiji Fu, Haonan Wen, Xiaoxiao Wang, Yingjie Tian*, Self-improved multi-view interactive knowledge transfer, Information Fusion, 2025, 114: 102718.
4. Yingjie Tian, Haoran Jiang, Recent advances in complementary label learning, Information Fusion, 2025, 114: 102702.
5. Yingjie Tian, Siyu Zhao, Xingyu Zhang, Robustness and orthogonality: Time series forecasting via wavelets, Information Sciences, 2025, 717:122328.
6. Yingjie Tian, Shaokai Xu, Muyang Li, Class-view graph knowledge distillation: A new idea for learning MLPs on graphs, Neurocomputing, 2025, 637: 130035.
7. Xiaotong Yu, Shiding Sun, Yingjie Tian*, Sample selection for noisy partial label learning with interactive contrastive learning, Pattern Recognition, 2025, 166: 111681.
8. Xiaoxi Zhao, Yingjie Tian, Chonghua Zheng, Robust one-class support vector machine, Neural Networks, 2025, 188: 107416.
9. Long Tang, Pengfei Yan, Yingjie Tian, Pano.M. Pardalos, Self-adaptive label discovery and multi-view fusion for complementary label learning, Neural Networks, 2025, 181: 106763.
10. Long Tang, Yelei Liu, Yingjie Tian, Panos M Pardalos, Complementary label learning with multi-view data and a semi-supervised labeling mechanism, Pattern Recognition, 2025, 165: 111651.
11. Jianyu Miao, Jingjing Zhao, Tiejun Yang, Yingjie Tian, Yong Shi, Mingliang Xu, Robust sparse orthogonal basis clustering for unsupervised feature selection, Expert Systems With Applications, 2025, 274:126890.
12. Yingjie Tian, Haonan Wen, Saiji Fu∗, Multi-step ahead prediction of carbon price movement using time-series privileged information, Expert Systems With Applications, 2024, 255:124825.
13. Jingjing Tang, Yan Li, Zhaojie Hou, Saiji Fu, Yingjie Tian*, Robust two-stage instance-level cost-sensitive learning method for class imbalance problem Knowledge-Based Systems, 2024, 300:112143.
14. Yingjie Tian, Shaokai Xu, Muyang Li, Decoupled graph knowledge distillation: A general logits-based method for learning MLPs on graphs, Neural Networks, 2024, 179:106567.
15. Zhaojie Hou, Jingjing Tang, Yan Li, Saiji Fu, Yingjie Tian, MVQS: Robust multi-view instance-level cost-sensitive learning method for imbalanced data classification, Information Sciences, 2024, 675: 120467.
16. Jingjing Tang, Bangxin Liu, Saiji Fu, Yingjie Tian, Gang Kou, Advancing robust regression: Addressing asymmetric noise with the BLINEX loss function, Information Fusion, 2024, 110: 102463.
17. Jingjing Tang, Qingqing Yi, Saiji Fu*, Yingjie Tian, Incomplete multi-view learning: Review, analysis, and prospects, Applied Soft Computing, 2024, 153:111278.
18. Saiji Fu, Tianyi Dong, Zhaoxin Wang, Yingjie Tian*, Weakly privileged learning with knowledge extraction, Pattern Recognition, 2024,153:110517,
19. Shiding Sun, Bo Wang, Yingjie Tian*, Decoupled Representation for Multi-View Learning, Pattern Recognition, 2024, 151:110377.
20. Duo Su, Junjie Hou, Weizhi Gao, Yingjie Tian*, Bowen Tang, D4M: Dataset Distillation via Disentangled Diffusion Model, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR-2024), 5809~5818.
21. Haoran Jiang, Zhihao Sun, Yingjie Tian*, Navigating Real-World Partial Label Learning: Unveiling Fine-Grained Images with Attributes, the 38th AAAI Conference on Artificial Intelligence (AAAI-2024), 12874~12882.
22. Dalian Liu, Saiji Fu, Yingjie Tian*, Jingjing Tang, Universum driven cost-sensitive learning method with asymmetric loss function, Engineering Applications of Artificial Intelligence, 2024, 131: 107849.
23. Yingjie Tian, Duo Su, Shilin Li, Adaptive robust loss for landmark detection, Information Fusion, 2024, 101: 102013.
24. Xiaotong Yu, Shiding Sun, Yingjie Tian∗, Self-distillation and Self-supervision for Partial Label Learning, Pattern Recognition, 2024, 146: 110016.
25. Yingjie Tian, Yuhao Xie, Artificial cheerleading in IEO: Marketing campaign or pump and dump scheme, Information Processing and Management, 2024, 61: 103537.
26. Haoran Jiang, Zhihao Sun, Yingjie Tian∗, ComCo: Complementary Supervised Contrastive Learning for Complementary Label Learning, Neural Networks, 2024, 169: 44~56.
27. Saiji Fu, Xiaoxiao Wang, Jingjing Tang, Shulin Lan, Yingjie Tian*, Generalized robust loss functions for machine learning, Neural Networks, 2024，171: 200~214.
28. Kai Li, Jie Yang, Siwei Ma, Bo Wang, Shanshe Wang, Yingjie Tian*, Zhiquan Qi, Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection, IEEE Transactions on Intelligent Transportation Systems, 2024, 25(1):237~250.
29. Saiji Fu, Yingjie Tian*, Long Tang, Robust regression under the general framework of bounded loss functions, European Journal of Operational Research, 2023, 310: 1325~1339.
30. Saiji Fu, Duo Su, Shilin Li, Shiding Sun, Yingjie Tian*, Linear-exponential loss incorporated deep learning for imbalanced classification, ISA Transactions, 2023, 140: 279~292.
31. Saiji Fu, Xiaoxiao Wang, Yingjie Tian*, Tianyi Dong, Jingjing Tang, Jicai Li, Coarse-grained privileged learning for classification, Information Processing and Management, 2023, 60: 103506.
32. Yingjie Tian, Kunlong Bai, End-to-End multitask learning with vision transformer, IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(7): 9579~9590.
33. Yingjie Tian, Kunlong Bai, Xiaotong Yu, and Siyu Zhu, Causal Multi-Label Learning for Image Classification，Neural Networks, 2023, 167: 626~637.
34. Shiding Sun, Xiaotong Yu, Yingjie Tian*, Multi-view prototype-based disambiguation for partial label learning, Pattern Recognition, 2023, 141: 109625.
35. Yingjie Tian, Xiaotong Yu, Saiji Fu, Partial label learning: taxonomy, analysis and outlook, Neural Networks, 2023, 161: 708~734.
36. Yuqi Zhang, Yingjie Tian*, Junjie Hou. CSAST: content self-supervised and style contrastive learning for arbitrary style transfer, Neural Networks, 2023, 164: 146~155.
37. Yingjie Tian, Xiaoxi Zhao, Saiji Fu*, Kernel methods with asymmetric and robust loss function, Expert Systems With Applications, 2023, 213: 119236.
38. Siyu Zhu, Yingjie Tian*, Shape robustness in style enhanced cross domain semantic segmentation, Pattern Recognition, 2023, 135: 109143.
39. Saiji Fu, Yingjie Tian*, Jingjing Tang, Xiaohui Liu, Cost-sensitive learning with modified stein loss function, Neurocomputing, 2023, 525: 57~75.
40. Yingjie Tian, Yuhao Xie, Picture For Proof (PFPs): aesthetics, IP and post launch performance, Finance Research Letters, 2023, 55, 103974.
41. Yingjie Tian, Xiaotong Yu, Saiji Fu*, Multi-view side information-incorporated tensor completion, Numerical Linear Algebra with Applications, 2023, DOI: 10.1002/nla.2485.
42. Shiding Sun, Yingjie Tian, Zhiquan Qi, Yang Wu, Weizhi Gao, Yahe Wu, Two-stage training strategy combined with neural network for segmentation of internal mammary artery graft, Biomedical Signal Processing and Control, 2023, 80:104278.
43. Kai Li, Bo Wang, Yingjie Tian*, Zhiquan Qi. Fast and accurateroad crack detection based on adaptive cost-sensitive loss function, IEEE Transactions on Cybernetics, 2023, 53(2): 1051~1062.
44. Xiang Gao, Yuqi Zhang, Yingjie Tian*, Learning to incorporate texture saliency adaptive attention to image cartoonization，ICML, 2022, 162: 7183~7207.
45. Yingjie Tian, Yuqi Zhang, A comprehensive survey on regularization strategies in machine learning, Information Fusion, 2022, 80: 146~166.
46. Yingjie Tian, Duo Su, Stanislao Lauria, Xiaohui Liu, Recent advances on loss functions in deep learning for computer vision, Neurocomputing, 2022, 497: 129~158.
47. Saiji Fu, Xiaotong Yu, Yingjie Tian*, Cost sensitive ν-support vector machine with LINEX loss, Information Processing and Management, 2022, 59(2): 102809.
48. Yingjie Tian, Shiding Sun, Jingjing Tang, Multi-view teacher–student network, Neural Network, 2022, 146: 69~84.
49. Jingjing Tang, Dewei Li, Yingjie Tian*, Image classification with multi-view multi-instance metric learning, Expert Systems With Applications, 2022, 189, 116117.
50. Yingjie Tian, Siyu Zhu, Partial domain adaptation on semantic segmentation, IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(6): 3798~3809.
51. Xiang Gao, Yingjie Tian*, Zhiquan Qi, Multi-view feature augmentation with adaptive class activation mapping, IJCAI, 2021, 678-684.
52. Jiabin Liu, Bo Wang, Xin Shen, Zhiquan Qi, Yingjie Tian, Two-stage training for learning from label proportions, IJCAI, 2021, 2737-2743.
53. Yingjie Tian, Saiji Fu, Jingjing Tang, Incomplete-view oriented kernel learning method with generalization error bound, Information Sciences, 2021, 581: 951~977.
54. Fenfen Zhou, Yingjie Tian*, Zhiquan Qi, Attention transfer network for nature image matting, IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(6): 2192~2205.
55. Xiang Gao, Yingjie Tian*, Zhiquan Qi, RPD-GAN: Learning to draw realistic paintings with generative adversarial network, IEEE Transactions on Image Processing, 2020, 29: 8706~8720.
56. Yingjie Tian, Mahboubeh Mirzabagheri, Peyman Tirandazic, Seyed Mojtaba Hosseini Bamakan, A non-convex semi-supervised approach to opinion spam detection by ramp-one class SVM, Information Processing and Management, 2020, 57(6): 102381.
57. Yingjie Tian, Saiji Fu, A descriptive framework for the field of deep learning applications in medical images，Knowledge-Based Systems, 2020, 210: 106445.
58. Jiabin Liu, Bo Wang, Zhiquan Qi, Yingjie Tian, Yong Shi, Learning from label proportions with generative adversarial networks, NeurIPS, 2019, 7167~7177.
59. Jingjing Tang, Yingjie Tian*, Dalian Liu, Gang Kou, Coupling privileged kernel method for multi-view learning, Information Sciences, 2019, 481: 110~127.
60. Wen Long, Linqiu Song, Yingjie Tian*, A new graphic kernel method of stock price trend prediction based on financial news semantic and structural similarity, Expert Systems With Applications, 2019, 118: 411~424.
61. Jingjing Tang, Yingjie Tian*, Peng Zhang, and Xiaohui Liu, Multiview privileged support vector machines, IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8): 3463~3477.
62. Zhiquan Qi, Fan Meng, Yingjie Tian*, Lingfeng Niu, Yong Shi, and Peng Zhang, Adaboost-LLP: A boosting method for learning with label proportions, IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8): 3548~3559.
63. Dewei Li, Yingjie Tian*, Survey and experimental study on metric learning methods, Neural Networks, 2018, 105: 447~462.
64. Lingfeng Niu, Ruizhi Zhou, Yingjie Tian*, Zhiquan Qi, Peng Zhang, Nonsmooth penalized clustering via lp regularized sparse regression, IEEE Transactions on Cybernetics, 2017, 47(6): 1423~1433.
65. Huadong Wang, Yong Shi, Lingfeng Niu, and Yingjie Tian, Nonparallel support vector ordinal regression, IEEE Transactions on Neural Networks and Learning Systems, 2017, 47(10): 3306~3317.
66. Dongkuan Xu, Jia Wu, Dewei Li, Yingjie Tian*, Xingquan Zhu, Xindong Wu, SALE: Self-adaptive LSH encoding for multi-instance learning, Pattern Recognition, 2017, 71: 460~482,
67. Qin Zhang, Jia Wu, Hong Yang, Yingjie Tian*, Chengqi Zhang, Unsupervised feature learning from time series, IJCAI, 2016, 2322~2328.
68. Dandan Chen, Yingjie Tian*, Xiaohui Liu, Structural nonparallel support vector machine for pattern recognition, Pattern Recognition, 2016, 60: 296~305.
69. Zhiquan Qi, Yingjie Tian*, Yong Shi, Successive Overrelaxation for laplacian support vector machine, IEEE Transactions on Neural Networks, 2015, 26(4): 674~683.
70. Yingjie Tian, Zhiquan Qi, Xuchan Ju, Yong Shi, Xiaohui Liu, Nonparallel support vector machines for pattern classification, IEEE Transactions on Cybernetics, 2014, 44(7): 1067~1079.
