Wu-Jun LI (Resume in Chinese)
Professor, PhD

National Key Laboratory for Novel Software Technology
Department of Computer Science and Technology
Nanjing University
163 Xianlin Avenue, Qixia District, Nanjing, 210023, P.R.China

Office: Room 402, Computer Science Building
Phone: 86-25-8968 0345

Email: liwujun at nju.edu.cn

News

• [May 25, 2022] 招收2023年入学的博士生、直博生和硕士生。请将申请材料发送到 liwujun@nju.edu.cn，申请材料包括：个人简历、成绩单和个人陈述。个人陈述的内容包括：为什么要读博（或硕），为什么申请南大，为什么申请计算机系，为什么申请我的课题组，将来的职业规划，个人最大的优点和缺点等。
  
  
• [May 25, 2022] 长期招收计算机、数学、电子相关专业的大二、大三本科生进组实习，要求成绩优异。请将个人简历和成绩单发送到 liwujun@nju.edu.cn。
  
  
• [Apr 20, 2021] Wu-Jun Li has been recognized as the AI 2000 Most Influential Scholar (李武军老师获得 AI 2000人工智能全球最具影响力学者奖)
  
  
• [Dec 31, 2018] PhD student Shen-Yi Zhao won the Baidu Scholarship 2018
  
  
• [Sep 19, 2018] Open source project: a PyTorch-based platform for distributed deep learning (LIBBLE-DL).
  https://github.com/LIBBLE/LIBBLE-DL
  
  
• [Oct 18, 2017] Open source project: a library for big data machine learning with Parameter Server (LIBBLE-PS).
  https://github.com/LIBBLE/LIBBLE-PS
  
  
• [Mar 17, 2017] Open source project: a library for parallel knowledge graph embedding (ParaGraphE).
  https://github.com/LIBBLE/LIBBLE-MultiThread/tree/master/ParaGraphE
  
  
• [Jun 03, 2016] Open source project: a library for big data machine learning on Spark (LIBBLE-Spark).
  https://github.com/LIBBLE/LIBBLE-Spark/
  
  
• [Nov 29, 2015] Give a tutorial about "Learning to Hash for Big Data" at the CCF ADL. [Slides] .
  The "Learning to Hash" website.
  
  
• [Nov 26, 2015] Give a talk about "Big Data Machine Learning" at the Institute of Automation, Chinese Academy of Science. [Slides]
  
  
• [May 09, 2015] Give a talk about "Learning to Hash for Big Data" at the Vision And Learning SEmilar (VALSE). [Slides]
  
  
• [Nov 08, 2014] Give a talk about "Big Data Machine Learning" at the China Workshop on Machine Learning and Applications. [Slides]
  
  
• More......
  
  

Biography

Wu-Jun LI is currently a full professor at the Department of Computer Science and Technology, Nanjing University.

From 2010 to 2013, he was a faculty member of the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU) .

He received his PhD degree from the Department of Computer Science and Engineering, HKUST in 2010, under the supervision of Prof. Dit-Yan Yeung.

Before that, he received his M.Eng. degree and B.Sc. degree from the Department of Computer Science and Technology, Nanjing University in 2006 and 2003, respectively.

Research Interests

·         Machine Learning

·        Big Data

·        Artificial Intelligence

·        Data Mining

Students

·       PhD Students:

Kai-Lang Yao, Xiao Ma, Chu-Xiao Zuo, Wen-Pu Cai, Jun Li, Jian Lin, Yi-Rui Yang, Yan-Shuo Liang, Yu-Hao Huang, Chang-Wei Shi

·       Master Students:

Yin-Peng Xie, Li-Jun Xu, Jia-Yi Zhou, Xiao-Chen Cai, Feng-Bin He, Jia-Yi Leng, Jia-Fan Xu, Yang-Fan Zhou, Shan-Liang Chen, Hao Lin, Kun Wang, Yu Xia, Jin-Dong Zhou

·       Undergraduate Students:

Jin Han, Huan-Yi Su

·       Alumni

Teaching

·       Courses in Nanjing University:

Data Structures [for UG students, Spring 2014, Fall 2015, Fall 2016, Fall 2017, Spring 2018, Fall 2019, Fall 2020, Spring 2021]

Big Data Analytics [for part-time PG students, Fall 2017, Fall 2018, Fall 2019]

·       Former courses in SJTU:

Web Search and Mining [for PG students, Fall 2010, Fall 2011, Fall 2012]

Mining Massive Datasets [for UG students, Spring 2011, Spring 2012, Spring 2013]

Information Retrieval in Practice [for UG students, Spring 2012]

Big Data Processing in Practice [for UG students, Fall 2012]

Professional Service

·       Conference/Workshop organization:

2014: PC co-chair of ICML workshop on Machine Learning in China (MLChina'14)

·       PC member:

2019: AAAI (Senior PC), CVPR, ICML, IJCAI (Senior PC), NIPS
2018: AISTATS, CVPR, ICLR, ICML, IJCAI (Senior PC), NIPS, SIGKDD
2017: AAAI, AISTATS, CVPR, ICCV, ICML, IJCAI, NIPS, UAI
2016: AAAI, CVPR, ICML, IJCAI, NIPS (reviewer), SIGKDD, UAI, ECCV, PAKDD, PRICAI
2015: IJCAI (Senior PC), NIPS (reviewer), AAAI, SIGKDD, ICCV, UAI, PAKDD, BigComp, CCFAI, CCML, NCIIP, NLPCC, CCF-BigData
2014: ICML, NIPS (reviewer), UAI, SDM, ICPR, ICTAI, BigComp, CCDM, CCPR, PRICAI, CIDM, NLPCC, CCF-BigData
2013: IJCAI
2012: ICTAI
2011: IJCAI, ICTAI, ICONIP
2010: ICPR

·       Editorial board member:

Junior Associate Editor of Frontiers of Computer Science (FCS)

·       Journal reviewer:

Artificial Intelligence
ACM Transactions on Information Systems
ACM Transactions on Intelligent Systems and Technology
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Multimedia
International Journal of Computer Vision
Journal of Machine Learning Research
Machine Learning
Data Mining and Knowledge Discovery
Pattern Recognition
Neural Networks
Neurocomputing
Frontiers of Computer Science
Journal of Computer Science and Technology
SCIENCE CHINA Information Sciences
Chinese Science Bulletin
Journal of Software

Selected Publications (* indicates students under my supervision)

Technical Report:

1. Stochastic Normalized Gradient Descent with Momentum for Large Batch Training
   Shen-Yi Zhao*, Yin-Peng Xie*, Wu-Jun Li.
   [arXiv version]
   
   
2. Stagewise Enlargement of Batch Size for SGD-based Learning.
   Shen-Yi Zhao*, Yin-Peng Xie*, Wu-Jun Li.
   [arXiv version]
   
   
3. Global Momentum Compression for Sparse Communication in Distributed SGD.
   Shen-Yi Zhao*, Yin-Peng Xie*, Hao Gao*, Wu-Jun Li.
   [arXiv version]
   
   
4. ADASS: Adaptive Sample Selection for Training Acceleration.
   Shen-Yi Zhao*, Hao Gao*, Wu-Jun Li.
   [arXiv version]
   
   
5. TOMA: Topological Map Abstraction for Reinforcement Learning.
   Zhao-Heng Yin*, Wu-Jun Li.
   [arXiv version]
   
   
6. Clustered Reinforcement Learning.
   Xiao Ma*, Shen-Yi Zhao*, Wu-Jun Li.
   [arXiv version]
   
   
7. On the Evaluation Metric for Hashing.
   Qing-Yuan Jiang*, Ming-Wei Li*, Wu-Jun Li.
   [arXiv version]
   
   
8. Deep Multi-Index Hashing for Person Re-Identification.
   Ming-Wei Li*, Qing-Yuan Jiang*, Wu-Jun Li.
   [arXiv version]
   
   
9. Convolutional Geometric Matrix Completion.
   Kai-Lang Yao*, Wu-Jun Li, Jianbo Yang, Xinyan Lu
   [arXiv version]
   
   
10. Weight Normalization based Quantization for Deep Neural Network Compression.
    Wen-Pu Cai*, Wu-Jun Li.
    [arXiv version]
    
    

Published Papers:

1. BASGD: Buffered Asynchronous SGD for Byzantine Learning.
   Yi-Rui Yang*, Wu-Jun Li.
   Proceedings of the International Conference on Machine Learning (ICML), 2021.
   [pdf][code]
   
   
2. Blocking-based Neighbor Sampling for Large-scale Graph Neural Networks.
   Kai-Lang Yao*, Wu-Jun Li.
   Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021.
   [pdf][code]
   
   
3. CAM: Context-Aware Masking for Robust Speaker Verification.
   Ya-Qi Yu*, Siqi Zheng, Hongbin Suo, Yun Lei, Wu-Jun Li.
   Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021.
   [pdf]
   
   
4. On the Convergence and Improvement of Stochastic Normalized Gradient Descent.
   Shen-Yi Zhao*, Yin-Peng Xie*, Wu-Jun Li.
   SCIENCE CHINA Information Sciences (SCIS), 2021.
   [pdf]
   
   
5. Densely Connected Time Delay Neural Network for Speaker Verification.
   Ya-Qi Yu*, Wu-Jun Li.
   Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020.
   [pdf]
   
   
6. DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images.
   Kang Fang*, Wu-Jun Li.
   Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020.
   [pdf]
   
   
7. ExchNet: A Unified Hashing Network for Accelerating Fine-Grained Image Retrieval.
   Quan Cui, Qing-Yuan Jiang*, Xiu-Shen Wei, Wu-Jun Li, Osamu Yoshie.
   Proceedings of the European Conference on Computer Vision (ECCV), 2020.
   [pdf]
   
   
8. Hashing based Answer Selection.
   Dong Xu*, Wu-Jun Li.
   Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.
   [pdf]
   
   
9. SVD: A Large-Scale Short Video Dataset for Near Duplicate Video Retrieval.
   Qing-Yuan Jiang*, Yi He, Gen Li, Jian Lin*, Lei Li, Wu-Jun Li.
   Proceedings of International Conference on Computer Vision (ICCV), 2019.
   [pdf] [dataset]
   
   
10. Deep Hashing for Speaker Identification and Retrieval.
    Lei Fan*, Qing-Yuan Jiang*, Ya-Qi Yu*, Wu-Jun Li.
    Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2019.
    [pdf]
    
    
11. Ensemble Additive Margin Softmax for Speaker Verification.
    Ya-Qi Yu*, Lei Fan*, Wu-Jun Li.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.
    [pdf]
    
    
12. Discrete Latent Factor Model for Cross-Modal Hashing.
    Qing-Yuan Jiang*, Wu-Jun Li.
    IEEE Transactions on Image Processing (TIP), 2019.
    [pdf][MATLAB code]
    
    
13. Robust Frequent Directions with Application in Online Learning.
    Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang.
    Journal of Machine Learning Research (JMLR), 2019.
    [pdf]
    
    
14. Proximal SCOPE for Distributed Sparse Learning.
    Shen-Yi Zhao*, Gong-Duo Zhang*, Ming-Wei Li*, Wu-Jun Li.
    Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS), 2018.
    [arXiv Version]
    
    
15. Deep Discrete Supervised Hashing.
    Qing-Yuan Jiang*, Xue Cui*, Wu-Jun Li.
    IEEE Transactions on Image Processing (TIP), 27(12): 5996-6009, 2018.
    [pdf] [code]
    
    
16. Asymmetric Deep Supervised Hashing.
    Qing-Yuan Jiang*, Wu-Jun Li.
    Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018.
    [pdf] [PyTorch code] [MATLAB code]
    
    
17. Semi-Supervised Deep Hashing with a Bipartite Graph.
    Xinyu Yan, Lijun Zhang, Wu-Jun Li.
    Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2017.
    [pdf]
    
    
18. Deep Cross-Modal Hashing.
    Qing-Yuan Jiang*, Wu-Jun Li.
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
    [pdf] [TensorFlow code] [MATLAB code]
    
    
19. SCOPE: Scalable Composite Optimization for Learning on Spark.
    Shen-Yi Zhao*, Ru Xiang*, Ying-Hao Shi*, Peng Gao*, Wu-Jun Li.
    Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), 2017.
    [pdf]
    
    
20. Lock-Free Optimization for Non-Convex Problems.
    Shen-Yi Zhao*, Gong-Duo Zhang*, Wu-Jun Li.
    Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), 2017.
    [pdf]
    
    
21. Feature Learning based Deep Supervised Hashing with Pairwise Labels.
    Wu-Jun Li, Sheng Wang*, Wang-Cheng Kang*.
    Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016.
    [pdf] [code]
    
    
22. Fast Asynchronous Parallel Stochastic Gradient Descent: A Lock-Free Approach with Convergence Guarantee.
    Shen-Yi Zhao*, Wu-Jun Li.
    Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), 2016.
    [pdf]
    
    
23. Column Sampling based Discrete Supervised Hashing.
    Wang-Cheng Kang*, Wu-Jun Li, Zhi-Hua Zhou
    Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), 2016.
    [pdf] [code]
    
    
24. Scalable Graph Hashing with Feature Transformation.
    Qing-Yuan Jiang*, Wu-Jun Li.
    Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), 2015.
    [pdf] [code]
    
    
25. Learning to hash for big data: current status and future trends.
    Wu-Jun Li, Zhi-Hua Zhou.
    Chinese Science Bulletin, 60: 485-490, 2015. (In Chinese, Invited Paper) .
    [pdf]
    
    
26. Relational collaborative topic regression for recommender systems.
    Hao Wang*, Wu-Jun Li.
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(5): 1343-1355, 2015.
    [pdf] [data]
    
    
27. Support Matrix Machines.
    Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li.
    Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015.
    [pdf] [code]
    
    
28. Multicategory large margin classification methods: hinge losses vs. coherence functions.
    Zhihua Zhang, Cheng Chen, Guang Dai, Wu-Jun Li, Dit-Yan Yeung.
    Artificial Intelligence, 215: 55-78, 2014.
    [pdf]
    
    
29. Distributed Power-law Graph Computing: Theoretical and Empirical Analysis.
    Cong Xie*, Ling Yan*, Wu-Jun Li, Zhihua Zhang.
    Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014.
    [pdf] [longer version] [code]
    
    
30. Distributed Stochastic ADMM for Matrix Factorization.
    Zhi-Qin Yu*, Xing-Jian Shi*, Ling Yan*, Wu-Jun Li.
    Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), 2014.
    [pdf]
    
    
31. Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising.
    Ling Yan*, Wu-Jun Li, Gui-Rong Xue, Dingyi Han.
    Proceedings of the 31st International Conference on Machine Learning (ICML), 2014.
    [pdf]
    
    
32. Supervised Hashing with Latent Factor Models.
    Peichao Zhang*, Wei Zhang*, Wu-Jun Li, Minyi Guo.
    Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2014.
    [pdf][MATLAB code]
    
    
33. Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization.
    Dongqing Zhang*, Wu-Jun Li.
    Proceedings of theTwenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2014.
    [pdf][MATLAB code]
    
    
34. Online Egocentric models for citation networks.
    Hao Wang*, Wu-Jun Li.
    Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI), 2013.
    [pdf]
    
    
35. Collaborative topic regression with social regularization for tag recommendation.
    Hao Wang*, Binyi Chen*, Wu-Jun Li.
    Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI), 2013.
    [pdf] [data]
    
    
36. Isotropic hashing.
    Weihao Kong*, Wu-Jun Li.
    Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012.
    [pdf] [MATLAB code]
    
    
37. Manhattan hashing for large-scale image retrieval.
    Weihao Kong*, Wu-Jun Li, Minyi Guo.
    Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2012.
    [pdf] [MATLAB code]
    
    
38. Double-bit quantization for hashing.
    Weihao Kong*, Wu-Jun Li.
    Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2012.
    [pdf] [MATLAB code]
    
    
39. Emoticon smoothed language models for Twitter sentiment analysis.
    Kun-Lin Liu*, Wu-Jun Li, Minyi Guo.
    Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2012.
    [pdf]
    
    
40. Sparse probabilistic relational projection.
    Wu-Jun Li, Dit-Yan Yeung.
    Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2012.
    [pdf]
    
    
41. Social relations model for collaborative filtering.
    Wu-Jun Li, Dit-Yan Yeung.
    Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2011.
    [pdf]
    
    
42. Generalized latent factor models for social network analysis.
    Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang.
    Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI), 2011.
    [pdf][MATLAB code][Cora data]
    
    
43. MILD: Multiple-instance learning via disambiguation.
    Wu-Jun Li, Dit-Yan Yeung.
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 22 (1): 76-89, 2010.
    [pdf] [MATLAB code and data]
    
    
44. Gaussian process latent random field.
    Guoqiang Zhong, Wu-Jun Li, Dit-Yan Yeung, Cheng-Lin Liu, Xinwen Hou.
    Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2010.
    [pdf]
    
    
45. Probabilistic relational PCA.
    Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang.
    Proceedings of the Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS), 2009.
    [pdf] [MATLAB code] [longer version]
    
    
46. Relation regularized matrix factorization.
    Wu-Jun Li, Dit-Yan Yeung.
    Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI), 2009.
    [pdf] [slides] [MATLAB code] [data]
    
    
47. Localized content-based image retrieval through evidence region identification.
    Wu-Jun Li, Dit-Yan Yeung.
    Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
    [pdf] [MATLAB code] [SIVAL data set] [COREL data set] [longer version]
    
    
48. TagiCoFi: Tag informed collaborative filtering.
    Yi Zhen, Wu-Jun Li, Dit-Yan Yeung.
    Proceedings of the Third ACM Conference on Recommender Systems (RecSys), 2009.
    [pdf] [code]
    
    
49. Latent Wishart processes for relational kernel learning.
    Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung.
    Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 5, pp. 336-343, 2009.
    [pdf] [slides] [MATLAB code] [data]
    
    
50. Coherence functions for multicategory margin-based classification methods.
    Zhihua Zhang, Michael Jordan, Wu-Jun Li, Dit-Yan Yeung.
    Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 5, pp. 647-654, 2009.
    [pdf]
    
    
51. Joint boosting feature selection for robust face recognition.
     Rong Xiao, Wu-Jun Li , Yuandong Tian, Xiaoou Tang.
    Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2):1415-1422, 2006.
    [pdf]

PhD Thesis:

Latent Factor Models for Statistical Relational Learning. Department of Computer Science and Engineering, Hong Kong University of Science and Technology. 2010.
[pdf]

Book (in Chinese):

1. 周憬宇，李武军，过敏意.《飞天开放平台编程指南-阿里云计算的实践》. 电子工业出版社，2013年3月.

Other Articles :

Research Skills Training for Undergraduate Students (In Chinese, 浅谈本科生科研能力培养).
Wu-Jun Li.
Invited by Communications of the China Computer Federation, 2013.
[pdf]

Talks

• Aug 2019. Learning to Hash. Alibaba. Hangzhou.
• Aug 2019. Learning to Hash. ByteDance AI Camp. Beijing.
• July 2019. Communication Optimization in Distributed Machine Learning. The Second Conference on Big Data and Artificial Intelligence Organized by CSIAM (CSIAM-BDAI 2019), Kunming.
• Jun 2019. Big Data Machine Learning. Southeast University, Nanjing.
• Nov 2018. Big Data Machine Learning. The 8th China Intelligence Industry Summit, Chengdu.
• Oct 2018. Big Data Machine Learning. Shanghai Jiao Tong University, Shanghai.
• Aug 2018. Big Data Machine Learning. International Conference of Development and Applications on AI, Yiwu.
• July 2018. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. The First Conference on Big Data and Artificial Intelligence Organized by CSIAM (CSIAM-BDAI 2018), Chongqing.
• May 2018. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. University of Electronic Science and Technology of China, Chendu.
• Nov 2017. Big Data Machine Learning. Seminar on Big Data and Artificial Intelligence in Earth Science, Beijing.
• Oct 2017. Big Data Machine Learning. Nantong University, Nantong, Jiangsu.
• Sep 2017. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. IEEE Signal and Data Science Forum (SIDAS), Tsinghua University, Beijing.
• Jun 2017. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. Institute of Computational Mathematics and Scientific/Engineering Computing, Chinese Academy of Science, Beijing.
• Jun 2017. Big Data Machine Learning. Research Institute of Petroleum Exploration & Development, Beijing.
• Jun 2017. Big Data Machine Learning. Intel-SJTU HPC & AI Seminar, Hangzhou.
• Jun 2017. Big Data Machine Learning. School of Electronic & Information Engineering, Nanjing University of Information Science and Technology, Nanjing.
• May 2017. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. Seminar on Artificial Intelligence for Geoscience and Engineering, State Key Laboratory of Marine Geology, Tongji University, Shanghai.
• May 2017. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. School of Data Science, Fudan University, Shanghai.
• Dec 2016. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. The China Conference on R Language, Shanghai. [Slides]
• Nov 2016. Big Data Machine Learning. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics.
• Oct 2016. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. Joint Seminar Organized by Intel - Jiangsu High Performance Computing Society.
• May 2016. Parallel and Distributed Stochastic Learning - Towards Scalable Learning for Big Data Intelligence. Tsinghua University - Nanjing University Joint Seminar on Machine Learning.
• Dec 2015. Big Data Machine Learning. College of CST, Nanjing University of Aeronautics and Astronautics. [Slides]
• Nov 2015. Learning to Hash for Big Data: A Tutorial. CCF Advanced Disciplines Lectures (ADL). [Slides]
• Nov 2015. Big Data Machine Learning. National Laboratory of Pattern Recognition. Institute of Automation, Chinese Academy of Science. [Slides]
• Jul 2015. Big Data Machine Learning. Northeastern University at Qinhuangdao. [Slides]
• Jun 2015. Big Data Machine Learning. Focus Technology Co., Ltd. [Slides]
• May 2015. Learning to Hash for Big Data. Vision And Learning SEmilar (VALSE). [Slides]
• Dec 2014. Big Data Machine Learning. Ocean University of China. [Slides]
• Nov 2014. Learning to Hash for Big Data. University of Electronic Science and Technology of China. [Slides]
• Nov 2014. Big Data Machine Learning. Sichuan University. [Slides]
• Nov 2014. Big Data Machine Learning. School of Information and Control, Nanjing University of Information Science and Technology. [Slides]
• Nov 2014. Big Data Machine Learning. XI'AN University of Technology. [Slides]
• Nov 2014. Big Data Machine Learning. China Workshop on Machine Learning and Applications. [Slides]
• Nov 2014. Learning to Hash for Big Data. Tutorial at CIKM 2014 [link].
• Oct 2014. Big Data Machine Learning. Youth Academic Forum. National Key Laboratory for Novel Software Technology, Nanjing University. [Slides]
• May 2014. Learning to Hash for Big Data. Young Scientist Forum on Big Data and Mobile Internet. Organized by China Association for Science and Technology. [Slides]
• May 2014. Learning to Hash for Big Data. Zhejiang Normal University. [Slides]
• Dec 2013. Learning to Hash for Big Data Retrieval and Mining. Key Lab of Intelligent Information Processing. Institute of Computing Technology, Chinese Academy of Science.[Slides]
• Dec 2013. Big Data Machine Learning. Huazhong University of Science and Technology.
• Nov 2013. Learning to Hash for Big Data Retrieval and Mining. Shandong University, Invited by YOCSEF Jinan. [Slides]
• Nov 2013. Learning to Hash for Big Data Retrieval and Mining. Forum on Big Data Machine Learning, Tianjin University, Invited by YOCSEF Tianjin. [Slides]
• May 2013. Big Data Machine Learning. BesTV, Shanghai.
• Jan 2013. Learning to Hash for Big Data Retrieval and Mining. The Workshop on Data Science and Information Industry. Shanghai Jiao Tong University. [Slides]