Xingjian Shi

施行健

Senior Applied Scientist, Amazon Web Services

: Amazon SJC25,; 2795 Augustine Dr, Santa Clara; CA 95054, United States
: xshiab AT connect DOT ust DOT hk
: My Github
: My Google Scholar

About Me

I obtained Ph.D degree in CSE from the Hong Kong University of Science and Technology in 2018. My Ph.D supervisor was Prof. Dit-Yan Yeung. Before that, I received my B.E. degree from Shanghai Jiao Tong University in 2014 under the supervision of Prof. Wu-Jun Li and Prof. Shi-Lin Wang. In 2019, I started my job as an applied scientist in Amazon Web Services (AWS) in the group lead by Dr. Mu Li and Dr. Alex Smola.

At Amazon, I'm working on democratizing machine learning on multimodal data to everyone via AutoGluon. I'm also a committer of GluonNLP and Apache/MXNet (incubating). In addition, I'm interested in spatiotemporal analysis and the general area of Deep Learning for Earth Science.

We are hiring interns! If you are interested in solving challenging problems related to multimodal AutoML, deep learning for earth science and code analysis, feel free to drop me an email!

Research Interest

Machine Learning and Deep Learning
Spatiotemporal Analysis
Computer Vision
Natural Language Processing

Selected Publications

Benchmarking Multimodal AutoML for Tabular Data with Text Fields.

Xingjian Shi*, Jonas Mueller*, Nick Erickson, Mu Li, Alexander J. Smola (* indicates equal contribution)

Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2021.

[paper] [project page]
Symbolic Music Generation with Transformer-GANs.

Aashiq Muhamed*, Liang Li*, Xingjian Shi, Suri Yaddanapudi, Wayne Chi, Dylan Jackson, Rahul Suresh, Zachary C. Lipton, Alexander J. Smola (* indicates equal contribution)

Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021.

[paper] [project page]
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing.

Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu

Journal of Machine Learning Research (JMLR), 2020.

[paper]
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems.

Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King

Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), 2019.

[paper] [project page]
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs.

Xingjian Shi*, Jiani Zhang*, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung (* indicates equal contribution)

Thirty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI), 2018.

[paper] [project page]
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model.

Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun Woo

Thirty-First Annual Conference on Neural Information Processing Systems (NeurIPS), 2017. (Accepted as Spotlight)

[paper] [project page] [poster]
Spatiotemporal Modeling for Crowd Counting in Videos.

Feng Xiong, Xingjian Shi, Dit-Yan Yeung

Sixteenth IEEE International Conference on Computer Vision (ICCV), 2017.

[paper] [project page]
Dynamic Key-Value Memory Networks for Knowledge Tracing.

Jiani Zhang, Xingjian Shi, Irwin King, Dit-Yan Yeung

Twenty-Sixth International Conference on World Wide Web (WWW), 2017.

[pdf] [project page]
Relational Deep Learning: A Deep Latent Variable Model for Link Prediction.

Hao Wang, Xingjian Shi, Dit-Yan Yeung

Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017.

[pdf] [code and data]
Natural Parameter Networks: A Class of Probabilistic Neural Networks.

Hao Wang, Xingjian Shi, Dit-Yan Yeung

Thirtieth Annual Conference on Neural Information Processing Systems (NeurIPS), 2016.

[pdf] [supplementary] [code and data]
Collaborative Recurrent Autoencoder: Recommend While Learning to Fill in the Blanks.

Hao Wang, Xingjian Shi, Dit-Yan Yeung

Thirtieth Annual Conference on Neural Information Processing Systems (NeurIPS), 2016.

[pdf] [supplementary] [code and data]
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.

Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun Woo

Twenty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2015.

[pdf] [supplementary] [code and data]
Relational Stacked Denoising Autoencoder for Tag Recommendation.

Hao Wang, Xingjian Shi, Dit-Yan Yeung

Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015.

[pdf] [supplementary] [code] [data]
Distributed Stochastic ADMM for Matrix Factorization.

Zhi-Qin Yu, Xingjian Shi, Ling Yan, Wu-Jun Li

Twenty-Third ACM International Conference on Information and Knowledge Management (CIKM), 2014.

[pdf]

Book Chapters

Deep learning and the weather forecasting problem -- precipitation nowcasting.

Zhihan Gao, Xingjian Shi*, Hao Wang, Dit-Yan Yeung, Wang-chun Woo, and Wai-Kin Wong (* indicates corresponding author)

Deep learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences, G. Camps-Valls, D. Tuia, X.X. Zhu, and M. Reichstein (eds.), Wiley & Sons, 2021.

[Book Preview]

Academic Service

PC Member: IJCAI 2015, AAAI 2018-2022
Senior PC Member: IJCAI 2021
Conference Reviewer: NeurIPS 2016, AAAI 2016, ICML 2019-2021, NeurIPS 2019-2021, ICLR 2021-2022
Journal Reviewer:
- ACM Transactions on Intelligent Systems and Technology
- ACM Transactions on Knowledge Discovery from Data
- Computational Intelligence
- Data Mining and Knowledge Discovery
- Frontiers of Computer Science
- IEEE Journal of Biomedical and Health Informatics
- IEEE Transactions on Big Data
- IEEE Transactions on Image Processing
- IEEE Transactions on Information Forensics and Security
- IEEE Transactions on Knowledge and Data Engineering
- IEEE Transactions on Neural Networks and Learning Systems
- International Journal of Data Science and Analytics
- Neural Computation
- Sustainable Cities and Society
- Weather and Forecasting

Working Experiences

- Senior Applied Scientist in AWS
- Apr, 2021-Now
- Applied Scientist in AWS
- Jul, 2019-Apr, 2021
- Applied Scientist Intern in AWS
- Nov, 2017-Apr, 2018

Selected Awards

- Hong Kong PhD Fellowship
- 2014-2018
- NeurIPS Travel Award
- 2015, 2017
- National Scholarship, Shanghai Jiao Tong University (Top 1%)
- 2011-2013

Presentations

Practical Automated Machine Learning with Tabular, Text, and Image Data, KDD Tutorial, 2020

[Project Page]
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model, NeurIPS Spotlight Presentation, 2017

[Download Slide][Video]
Dynamic Key-Value Memory Networks for Knowledge Tracing, VALSE, 2017

[Download Slide]
모두를 위한 MXNET, AWS Seoul Summit, 2017

[Download Slide] [Project Page]
A Practitioner's Guide to MXNet, HKUST CSE Seminar, 2017

[Download Slide]
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, VALSE Seminar, 2016

[Download Slide]