软件学院

School Of Software

Introduction:

Name: Mingsheng Long
Position:Associate Professor
Email: longmingsheng@gmail.com, mingsheng@tsinghua.edu.cn
Address: Room 11-413, East Main Building, Tsinghua University
Homepage: http://ise.thss.tsinghua.edu.cn/~mlong


Education background:

Sep. 2008 – Jul. 2014, Ph.D., Department of Computer Science, Tsinghua University
Sep. 2004 – Jul. 2008, B.E., Department of Electrical Engineering, Tsinghua University


Experience:

Jan. 2019 – Present, Associate Professor, School of Software, Tsinghua University
Mar. 2017 – Present, Group Leader, Machine Learning Group, National Engineering Laboratory for Big Data Software
Jul. 2016 – Dec. 2018, Assistant Professor, School of Software, Tsinghua University
Sep. 2014 – Oct. 2015, Visiting Researcher, Department of Computer Science, University of California, Berkeley
Jul. 2014 – Jul. 2016, Postdoctoral Researcher, School of Software, Tsinghua University


Concurrent Academic:

Member of the ACM, IEEE, CCF, and CAAI
Program Committee Chair: NIPS Transfer Learning Workshop, ICCV TASK-CV Workshop
Area Chair: ICLR
Senior Program Committee Member: IJCAI, AAAI
Program Committee Member: ICML, NIPS, KDD, CVPR, ICCV, etc.
Journal Reviewer: AIJ, JMLR, IJCV, TPAMI, TKDE, TIP, TNNLS, etc.


Areas of Research Interests/ Research Projects:

Machine Learning, Deep Learning, Transfer Learning, Big Data Analytics


Honors And Awards:

2018, Technical Invention Award, China Ministry of Education (4th contributor)
2018, Teaching Award, Tsinghua University
2017, Service Award for International Cooperation, Tsinghua University
2016, Distinguished Dissertation Award, China Association for Artificial Intelligence (CAAI)
2014, Distinguished Dissertation Award, Tsinghua University
2012, Best Paper Award Nominee, SIAM International Conference on Data Mining (SDM)


Academic Achievement:

Publications
A complete list of publications can be found at my homepage: http://ise.thss.tsinghua.edu.cn/~mlong.

Journals:
[1] Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan. Transferable Representation Learning with Deep Adaptation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018.
[2] Mingsheng Long, Jianmin Wang, Yue Cao, Jiaguang Sun, Philip S. Yu. Deep Learning of Transferable Representation for Scalable Domain Adaptation. IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(8):2027-2040, 2016.
[3] Mingsheng Long, Jianmin Wang, Jiaguang Sun, Philip S. Yu. Domain Invariant Transfer Kernel Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(6):1519-1532, 2015.
[4] Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang. Transfer Learning with Graph Co-Regularization. IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(7):1805-1818, 2014.
[5] Mingsheng Long, Jianmin Wang, Guiguang Ding, Sinno Jialin Pan, Philip S. Yu. Adaptation Regularization: A General Framework for Transfer Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(5):1076-1089, 2014. (1% Highly Cited Paper)

Conferences:
[1] Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long*, Jianmin Wang. Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning. Neural Information Processing Systems (NIPS), 2019.
[2] Ximei Wang, Ying Jin, Mingsheng Long*, Jianmin Wang, Michael I. Jordan. Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. Neural Information Processing Systems (NIPS), 2019.
[3] Yuchen Zhang, Tianle Liu, Mingsheng Long*, Michael I. Jordan. Bridging Theory and Algorithm for Domain Adaptation. International Conference on Machine Learning (ICML), 2019.
[4] Kaichao You, Ximei Wang, Mingsheng Long*, Michael I. Jordan. Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. International Conference on Machine Learning (ICML), 2019.
[5] Hong Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan. Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. International Conference on Machine Learning (ICML), 2019.
[6] Xinyang Chen, Sinan Wang, Mingsheng Long*, Jianmin Wang. Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation. International Conference on Machine Learning (ICML), 2019.
[7] Universal Domain Adaptation. Kaichao You, Mingsheng Long*, Zhangjie Cao, Jianmin Wang, Michael I. Jordan. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[8] Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long*, Jianmin Wang, Philip S. Yu. Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[9] Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei. Eidetic 3D LSTM: A Model for Video Prediction and Beyond. International Conference on Learning Representations (ICLR), 2019.
[10] Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan. Conditional Adversarial Domain Adaptation. Neural Information Processing Systems (NIPS), 2018.
[11] Shichen Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan. Generalized Zero-Shot Learning with Deep Calibration Network. Neural Information Processing Systems (NIPS), 2018.
[12] Yunbo Wang, Zhifeng Gao, Mingsheng Long*, Jianmin Wang, Philip S. Yu. PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning. International Conference on Machine Learning (ICML), 2018.
[13] Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Michael I. Jordan. Partial Transfer Learning with Selective Adversarial Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[14] Yue Cao, Mingsheng Long*, Bin Liu, Jianmin Wang. Deep Cauchy Hashing for Hamming Space Retrieval. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[15] Yunbo Wang, Mingsheng Long*, Jianmin Wang, Zhifeng Gao, Philip S. Yu. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs. Neural Information Processing Systems (NIPS), 2017.
[16] Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu. Learning Multiple Tasks with Multilinear Relationship Networks. Neural Information Processing Systems (NIPS), 2017.
[17] Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Philip S. Yu. HashNet: Deep Learning to Hash by Continuation. IEEE International Conference on Computer Vision (ICCV), 2017.
[18] Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan. Deep Transfer Learning with Joint Adaptation Networks. International Conference on Machine Learning (ICML), 2017.
[19] Yunbo Wang, Mingsheng Long*, Jianmin Wang, Philip S. Yu. Spatiotemporal Pyramid Network for Video Action Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[20] Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan. Unsupervised Domain Adaptation with Residual Transfer Networks. Neural Information Processing Systems (NIPS), 2016.
[21] Yue Cao, Mingsheng Long*, Jianmin Wang, Qiang Yang, Philip S. Yu. Deep Visual-Semantic Hashing for Cross-Modal Retrieval. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016.
[22] Han Zhu, Mingsheng Long*, Jianmin Wang, Yue Cao. Deep Hashing Network for Efficient Similarity Retrieval. AAAI Conference on Artificial Intelligence (AAAI), 2016.
[23] Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan. Learning Transferable Features with Deep Adaptation Networks. International Conference on Machine Learning (ICML), 2015. (Citation Count: 900)
[24] Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu. Transfer Feature Learning with Joint Distribution Adaptation. IEEE International Conference on Computer Vision (ICCV), 2013.
[25] Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, Wei Wang. Dual Transfer Learning. In Proceedings of SIAM International Conference on Data Mining (SDM), Anaheim, USA, 2012. (Best Paper Nominee)