Mingsheng Long

Associate Professor, PhD Supervisor

Machine Learning Group
School of Software
Tsinghua University

longmingsheng@gmail.com
mingsheng@tsinghua.edu.cn
Room 11-413, East Main Building, Tsinghua University, Beijing, China

Reminder to prospective applicants: Opening positions are available for class 2021. [Instructions]

Biography

My research spans machine learning theories and algorithms, with special interests in transfer learning, representation learning, and predictive learning. In particular, my research is persistently dedicated to advancing major areas of transfer learning, including learning transferable, predictive, and compact representations, domain adaptation, inductive transfer, multitask learning, and meta learning.

I am leading the Machine Learning Group. Our team is interested in developing foundational theories, effective algorithms, and reliable systems of machine learning to make sense of Out-of-Distribution (OOD) data in representation, inference, and decision making. We focus on real applications of AI and data science, including text, sequence, image & video understanding and scientific data analysis.

My Quote

“Everything should be made as simple as possible, but no simpler.” --Albert Einstein

Research

Machine Learning: Transfer Learning, Representation Learning, Predictive Learning

Education

PhD in Computer Science, 2008-2014
Department of Computer Science and Technology, Tsinghua University, Beijing, China
Advisor: Prof. Jianmin Wang
Bachelor in Electrical Engineering, 2004-2008
Department of Electrical Engineering, Tsinghua University, Beijing, China

Research Experience

Visiting Researcher, 2014-2015
Department of Computer Science, UC Berkeley
Advisor: Prof. Michael I. Jordan
Postdoc Researcher, 2014-2016
School of Software, Tsinghua University, Beijing, China
Advisor: Prof. Jianmin Wang

Publications [DBLP] [Google Scholar] [Semantic Scholar]

(* Corresponding Author)

Thesis

Mingsheng Long. Transfer Learning: Problems and Methods. 1-127, 2014 [PDF] (In Chinese)

Preprints

Towards Understanding the Transferability of Deep Representations
Hong Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan [arXiv]
How Does Learning Rate Decay Help Modern Neural Networks?
Kaichao You, Mingsheng Long*, Jianmin Wang, Michael I. Jordan [arXiv]
Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation
Ying Jin, Ximei Wang, Mingsheng Long*, Jianmin Wang [arXiv]
Adversarial Pyramid Network for Video Domain Generalization
Zhiyu Yao, Yunbo Wang, Xingqiang Du, Mingsheng Long*, Jianmin Wang [arXiv]

Journal Articles

Transferable Representation Learning with Deep Adaptation Networks
Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41(12):3071-3085, 2019 [Link]
Deep Learning of Transferable Representation for Scalable Domain Adaptation
Mingsheng Long, Jianmin Wang, Yue Cao, Jiaguang Sun, Philip S. Yu
IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(8):2027-2040, 2016 [Link] [Code]
Domain Invariant Transfer Kernel Learning
Mingsheng Long, Jianmin Wang, Jiaguang Sun, Philip S. Yu
IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(6):1519-1532, 2015 [Link] [Code]
Transfer Learning with Graph Co-Regularization
Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang
IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(7):1805-1818, 2014 [Link] [Code]
Adaptation Regularization: A General Framework for Transfer Learning
Mingsheng Long, Jianmin Wang, Guiguang Ding, Sinno Jialin Pan, Philip S. Yu
IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(5):1076-1089, 2014 [Link] [Code] (ESI Highly Cited Paper)

Conference Proceedings

Probabilistic Video Prediction from Noisy Data with a Posterior Confidence
Yunbo Wang, Jiajun Wu, Mingsheng Long, Joshua Tenenbaum
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Accepted)
Progressive Adversarial Networks for Fine-Grained Domain Adaptation
Sinan Wang, Xinyang Chen, Yunbo Wang, Mingsheng Long*, Jianmin Wang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Accepted)
Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification
Liang Li, Weirui Ye, Mingsheng Long*, Yateng Tang, Jin Xu, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2020 [PDF]
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long*, Jianmin Wang
Neural Information Processing Systems (NeurIPS), 2019 [PDF] [Code]
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Ximei Wang, Ying Jin, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NeurIPS), 2019 [PDF] [Code]
Bridging Theory and Algorithm for Domain Adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long*, Michael I. Jordan
International Conference on Machine Learning (ICML), 2019 [PDF] [Appendix] [Code]
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Kaichao You, Ximei Wang, Mingsheng Long*, Michael I. Jordan
International Conference on Machine Learning (ICML), 2019 [PDF] [Code]
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
Hong Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
International Conference on Machine Learning (ICML), 2019 [PDF] [Code]
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long*, Jianmin Wang
International Conference on Machine Learning (ICML), 2019 [PDF] [Code]
Maximum-Margin Hamming Hashing
Rong Kang, Yue Cao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
IEEE International Conference on Computer Vision (ICCV), 2019 [PDF] [Code]
Z-Order Recurrent Neural Networks for Video Prediction
Jianjin Zhang, Yunbo Wang, Mingsheng Long*, Jianmin Wang, Philip S. Yu
IEEE International Conference on Multimedia and Expo (ICME), 2019
Universal Domain Adaptation
Kaichao You, Mingsheng Long*, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]
Separate to Adapt: Open Set Domain Adaptation via Progressive Separation
Hong Liu, Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Qiang Yang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]
Learning to Transfer Examples for Partial Domain Adaptation
Zhangjie Cao, Kaichao You, Mingsheng Long*, Jianmin Wang, Qiang Yang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long*, Jianmin Wang, Philip S. Yu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [PDF] [Code]
Eidetic 3D LSTM: A Model for Video Prediction and Beyond
Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei
International Conference on Learning Representations (ICLR), 2019 [PDF]
Transferable Curriculum for Weakly-Supervised Domain Adaptation
Yang Shu, Zhangjie Cao, Mingsheng Long*, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF] [Code]
Transferable Attention for Domain Adaptation
Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long*, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF]
Conditional Adversarial Domain Adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Poster] [Code]
Generalized Zero-Shot Learning with Deep Calibration Network
Shichen Liu, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Code]
Partial Adversarial Domain Adaptation
Zhangjie Cao, Lijia Ma, Mingsheng Long*, Jianmin Wang
European Conference on Computer Vision (ECCV), 2018 [PDF] [Code]
Cross-Modal Hamming Hashing
Yue Cao, Bin Liu, Mingsheng Long*, Jianmin Wang
European Conference on Computer Vision (ECCV), 2018 [PDF]
Deep Triplet Quantization
Bin Liu, Yue Cao, Mingsheng Long*, Jianmin Wang, Jingdong Wang
ACM International Conference on Multimedia (ACMMM), 2018 [PDF] [Code]
Deep Priority Hashing
Zhangjie Cao, Ziping Sun, Mingsheng Long*, Jianmin Wang, Philip S. Yu
ACM International Conference on Multimedia (ACMMM), 2018 [PDF] [Code]
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Yunbo Wang, Zhifeng Gao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
International Conference on Machine Learning (ICML), 2018 [PDF] [Code]
PredCNN: Predictive Learning with Cascade Convolutions
Ziru Xu, Yunbo Wang, Mingsheng Long*, Jianmin Wang
International Joint Conference on Artificial Intelligence (IJCAI), 2018 [PDF] [Code]
Partial Transfer Learning with Selective Adversarial Networks
Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Michael I. Jordan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF] [Slides] [Code]
HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN
Yue Cao, Bin Liu, Mingsheng Long*, Jianmin Wang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF]
Deep Cauchy Hashing for Hamming Space Retrieval
Yue Cao, Mingsheng Long*, Bin Liu, Jianmin Wang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [PDF] [Code]
Multi-Adversarial Domain Adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long*, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF] [Code]
Unsupervised Domain Adaptation with Distribution Matching Machines
Yue Cao, Mingsheng Long*, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF]
Transfer Adversarial Hashing for Hamming Space Retrieval
Zhangjie Cao, Mingsheng Long*, Chao Huang, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2018 [PDF] [Code]
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
Yunbo Wang, Mingsheng Long*, Jianmin Wang, Zhifeng Gao, Philip S. Yu
Neural Information Processing Systems (NeurIPS), 2017 [PDF]
Learning Multiple Tasks with Multilinear Relationship Networks
Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu
Neural Information Processing Systems (NeurIPS), 2017 [PDF] [Poster] [Code]
HashNet: Deep Learning to Hash by Continuation
Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
IEEE International Conference on Computer Vision (ICCV), 2017 [PDF] [Code]
Correlation Hashing Network for Efficient Cross-Modal Retrieval
Yue Cao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
British Machine Vision Conference (BMVC), 2017 [PDF]
Deep Transfer Learning with Joint Adaptation Networks
Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
International Conference on Machine Learning (ICML), 2017 [PDF] [Slides] [Poster] [Code]
Spatiotemporal Pyramid Network for Video Action Recognition
Yunbo Wang, Mingsheng Long*, Jianmin Wang, Philip S. Yu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 [PDF] [Slides] [Code]
Deep Visual-Semantic Quantization for Efficient Image Retrieval
Yue Cao, Mingsheng Long*, Jianmin Wang, Shichen Liu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 [PDF] [Code]
Transitive Hashing Network for Heterogeneous Multimedia Retrieval
Zhangjie Cao, Mingsheng Long*, Jianmin Wang, Qiang Yang
AAAI Conference on Artificial Intelligence (AAAI), 2017 [PDF]
Collective Deep Quantization for Efficient Cross-Modal Retrieval
Yue Cao, Mingsheng Long*, Jianmin Wang, Shichen Liu
AAAI Conference on Artificial Intelligence (AAAI), 2017 [PDF] [Code]
Unsupervised Domain Adaptation with Residual Transfer Networks
Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NeurIPS), 2016 [PDF] [Poster] [Code]
Deep Visual-Semantic Hashing for Cross-Modal Retrieval
Yue Cao, Mingsheng Long*, Jianmin Wang, Qiang Yang, Philip S. Yu
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016 [PDF]
Composite Correlation Quantization for Efficient Multimodal Retrieval
Mingsheng Long, Yue Cao, Jianmin Wang, Philip S. Yu
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016 [PDF] [Slides] [Code]
Correlation Autoencoder Hashing for Supervised Cross-Modal Search
Yue Cao, Mingsheng Long*, Jianmin Wang, Han Zhu
ACM International Conference on Multimedia Retrieval (ICMR), 2016
Deep Quantization Network for Efficient Image Retrieval
Yue Cao, Mingsheng Long*, Jianmin Wang, Han Zhu, Qingfu Wen
AAAI Conference on Artificial Intelligence (AAAI), 2016 [PDF] [Slides] [Code]
Deep Hashing Network for Efficient Similarity Retrieval
Han Zhu, Mingsheng Long*, Jianmin Wang, Yue Cao
AAAI Conference on Artificial Intelligence (AAAI), 2016 [PDF] [Code]
Learning Transferable Features with Deep Adaptation Networks
Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan
International Conference on Machine Learning (ICML), 2015 [PDF] [Slides] [Poster] [Code] (1000+ Citations)
Transfer Joint Matching for Unsupervised Domain Adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 [PDF] [Code]
Inherent Replica Inconsistency in Cassandra
Xiangdong Huang, Jianmin Wang, Jian Bai, Guiguang Ding, Mingsheng Long
International Congress on Big Data (BigData), 2014
Local Hybrid Coding for Image Classification
Wu Xiang, Jianmin Wang, Mingsheng Long
International Conference on Pattern Recognition (ICPR), 2014
Transfer Feature Learning with Joint Distribution Adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu
IEEE International Conference on Computer Vision (ICCV), 2013 [PDF] [Code]
Transfer Sparse Coding for Robust Image Representation
Mingsheng Long, Guiguang Ding, Jianmin Wang, Jiaguang Sun, Philip S. Yu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 [PDF] [Code]
Transfer Learning with Graph Co-Regularization
Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang
AAAI Conference on Artificial Intelligence (AAAI), 2012 [PDF] [Code]
Topic Correlation Analysis for Cross-Domain Text Classification
Lianghao Li, Xiaoming Jin, Mingsheng Long
AAAI Conference on Artificial Intelligence (AAAI), 2012 [PDF] [Code]
Dual Transfer Learning
Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, Wei Wang
SIAM International Conference on Data Mining (SDM), 2012 [PDF] (Best Paper Nominee)
Transfer Learning via Cluster Correspondence Inference
Mingsheng Long, Wei Cheng, Xiaoming Jin, Jianmin Wang, Dou Shen
IEEE International Conference on Data Mining (ICDM), 2010 (Short Paper)

Open Libraries

Xlearn: An Open-Source Package for Deep Transfer Learning [Code]
DeepHash: An Open-Source Package for Deep Learning to Hash [Code]

Honors and Awards

First Prize, Technical Invention Award, Ministry of Education (MOE), China, 2018
Distinguished Dissertation Award, China Association for Artificial Intelligence (CAAI), 2016
Distinguished Dissertation Nominee, China Computer Federation (CCF), 2014
Outstanding PhD Graduate, Tsinghua University, 2014
Distinguished Dissertation Award, Tsinghua University, 2014
Best Paper Nominee, SIAM SDM, 2012

Professional Activities

Invited Talks

Transfer Learning: Theories and Algorithms, Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 [PDF]
Transfer Learning: Theories and Algorithms, CCF Conference on Artificial Intelligence, CCFAI 2019 [PDF]
Transfer Learning: From Algorithms to Theories and Back, Vision And Learning SEminar, VALSE 2019 [PDF]
Transfer Learning: Generalizing Deep Learning across Domains and Tasks, Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018 [PDF]
Predictive Learning for Video Prediction, Chinese Conference on Data Mining, CCDM 2018 [PDF]
Deep Transfer Learning, Google AI China Center Machine Learning Day, 2018 [PDF]
Recent Advances in Transfer Learning, Samsung Machine Learning Workshop, 2017 [PDF]
Deep Hashing for Multimedia Retrieval, International Conference on Data Science, 2017 [PDF]

PC Chair

Transfer and Multi-Task Learning Workshop, NeurIPS, 2015
Transferring and Adapting Source Knowledge in Computer Vision Workshop and Challenge (TASK-CV), ICCV, 2017

Area Chair

International Conference on Learning Representations (ICLR) 2020
IEEE International Conference on Multimedia and Expo (ICME) 2020

Senior PC Member

AAAI Conference on Artificial Intelligence (AAAI) 2020
International Joint Conference on Artificial Intelligence (IJCAI) 2019, 2020

PC Member | Reviewer

International Conference on Machine Learning (ICML) 2014, 2017, 2018, 2019, 2020
Neural Information Processing Systems (NeurIPS) 2016, 2017, 2018, 2019, 2020
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2019, 2020
IEEE International Conference on Computer Vision (ICCV) 2019
European Conference on Computer Vision (ECCV) 2018, 2020
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2018, 2019
ACM Multimedia Conference (ACMMM) 2017, 2018
AAAI Conference on Artificial Intelligence (AAAI) 2018, 2019
International Joint Conference on Artificial Intelligence (IJCAI) 2017, 2018
Artificial Intelligence Journal (AIJ) 2016, 2018
Journal of Machine Learning Research (JMLR) 2018
International Journal of Computer Vision (IJCV) 2019
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2016, 2017, 2018, 2019
IEEE Transactions on Knowledge and Data Engineering (TKDE) 2014, 2015, 2016, 2017, 2018, 2019
IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2016, 2017, 2018, 2019
IEEE Transactions on Image Processing (TIP) 2014, 2015, 2016, 2017, 2018, 2019
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 2018
IEEE Transactions on Multimedia (TMM) 2018
IEEE Transactions on Cybernetics (TC) 2018
ACM Computing Surveys 2019
ACM Transactions on Knowedge Discovery from Data (TKDD) 2016
ACM Transactions on Intelligent Systems and Technology (TIST) 2017
ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) 2014, 2015
Nature Communications 2019

Research Grants

Principal Investigator, Robust Transfer Learning for OOD Data in Open Dynamic Environment, MOE Strategic Research Project on Artificial Intelligence Algorithms (Project PI: Prof. Yang Yu @ Nanjing University), 2020-2021
Principal Investigator, Transfer Learning Algorithms, Kuaishou Research, 2019-2020
Principal Investigator, Multimodal Learning Algorithms, Kuaishou Research, 2019-2020
Principal Investigator, Recommendation Algorithm based on Deep Learning, Alibaba Innovative Research (AIR), 2018-2019
Principal Investigator, Intelligent Severe Weather Nowcasting System, China Meteorological Administration, 2019-2020
Principal Investigator, Spatiotemporal Deep Learning for Severe Weather Nowcasting, National Key R&D Plan, 2018-2022
Principal Investigator, Spatiotemporal Deep Learning: Algorithms and Applications, Natural Science Foundation of China, 2018-2021
Principal Investigator, Safe Transfer Learning for Big Data Analytics, Natural Science Foundation of China, 2016-2018
Principal Investigator, Scalable Transfer Learning: Theory and Algorithms, China Postdoc Science Foundation, 2015-2016

Teaching

Instructor: Deep Learning, 2018-Present (清华大学年度教学优秀奖, 2019)
Instructor: Machine Learning, 2019-Present
Instructor: Introduction to Data Science, 2018-Present
Co-Instructor: Introduction to Big Data Technology, 2017-Present
Co-Instructor: Foundations of Big Data Systems (B), 2015-Present
Co-Instructor: Foundations of Big Data Systems (A), 2016-Present

Students

PhDs

Yue Cao (2014-2019): Co-supervised with Prof. Jianmin Wang. 清华大学特等奖学金获得者, 清华大学优秀博士学位论文 -> MSRA
Yunbo Wang (2015-2020): Co-supervised with Prof. Philip S. Yu. 麻省理工学院 (MIT) 访问学者 ->
Zhongyi Pei (2015-2020): Co-supervised with Prof. Philip S. Yu -> THU Postdoc
Ximei Wang (2017-2022): Co-supervised with Prof. Jianmin Wang ->
Xinyang Chen (2017-2022): ->
Yang Shu (2018-2023): ->
Zhiyu Yao (2019-2024): ->

Masters

Han Zhu (2014-2017): Co-supervised with Prof. Jianmin Wang. 清华大学优秀硕士学位论文 -> Alimama (A-Star)
Feng Li (2014-2017): Co-supervised with Prof. Jianmin Wang -> Alimama
Qiaoan Chen (2014-2017): Co-supervised with Prof. Jianmin Wang -> WeChat
Qingfu Wen (2015-2018): Co-supervised with Prof. Jianmin Wang -> SenseTime
Jue Wang (2015-2018): Co-supervised with Prof. Jianmin Wang -> Government
Ziru Xu (2016-2019): Co-supervised with Prof. Jianmin Wang. 清华大学优秀硕士学位论文 -> Alimama
Jianjin Zhang (2016-2019): -> Microsoft
Zhifeng Gao (2016-2019): -> Alimama
Yuchen Zhang (2017-2020): Co-supervised with Prof. Jianmin Wang ->
Jintao Liu (2017-2020): Co-supervised with Prof. Jianmin Wang ->
Liang Li (2017-2020): Co-supervised with Prof. Jianmin Wang ->
Sinan Wang (2017-2020): ->
Ying Jin (2018-2021): Co-supervised with Prof. Jianmin Wang ->
Xingqiang Du (2018-2021): Co-supervised with Prof. Jianmin Wang ->
Bo Fu (2018-2021): Co-supervised with Prof. Jianmin Wang ->
Bin Liu (2018-2021): ->
Chao Huang (2019-2022): ->
Zhi Kou (2019-2022): ->
Jincheng Zhong (2019-2022): ->
Jinghan Gao (2019-2022): ->

Undergraduates

Zhangjie Cao (2014-2018): 清华大学特等奖学金提名, 清华大学优良毕业生 -> Stanford PhD
Shichen Liu (2014-2018): 清华大学优良毕业生 -> USC PhD
Bin Liu (2014-2018): 清华大学优良毕业生 -> THU Master
Lijia Ma (2015-2019): -> UW PhD
Hongyu Zhu (2015-2019): -> CMU Master
Zhiyu Yao (2015-2019): -> THU PhD
Chao Huang (2015-2019): -> THU Master
Zhi Kou (2015-2019): -> THU Master
Kaichao You (2016-2020): 清华大学特等奖学金获得者 -> THU PhD
Weirui Ye (2016-2020): -> THU PhD (Advisor: Andrew Yao @ IIIS)
Junguang Jiang (2016-2020): -> THU Master
Haixu Wu (2016-2020): -> THU Master
Ziping Sun (2016-2020): -> THU Master
Tianle Liu (2016-2020): ->
Hong Liu (2017-2021): 清华大学蒋南翔奖学金获得者 ->
Yong Liu (2017-2021): 清华大学蒋南翔奖学金获得者 ->
Yifei Ji (2017-2021): ->
Shijie Wang (2017-2021): ->
Jialong Wu (2018-2022): ->
Qi Li (2018-2022): ->