👋 I'm Xinyu Chen, now a Postdoctoral Associate at MIT (👉 visit MIT sites). Prior to joining MIT, I received PhD degree from University of Montreal in Canada.
- 🌱 A strong advocate for open-source & reproducible research.
- 💬 Create new ideas about spatiotemporal data computing.
- 📫 How to reach me: chenxy346@gmail.com
Selected Publications
- Xinyu Chen, Dingyi Zhuang, HanQin Cai, Shenhao Wang, Jinhua Zhao (2025). Dynamic autoregressive tensor factorization for pattern discovery of spatiotemporal systems. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47 (10): 8524-8537. [PDF] [Slides] [OPP Explanation]
- Xinyu Chen, HanQin Cai, Fuqiang Liu, Jinhua Zhao (2025). Correlating time series with interpretable convolutional kernels. IEEE Transactions on Knowledge and Data Engineering. 37 (6): 3272-3283. [PDF] [Blog post] [Data & Python code]
- Xinyu Chen, Chengyuan Zhang, Xi-Le Zhao, Nicolas Saunier, Lijun Sun (2025). Forecasting sparse movement speed of urban road networks with nonstationary temporal matrix factorization. Transportation Science. 59 (3): 670-687. [PDF] [Slides] [Data & Python code]
- Xinyu Chen, Xi-Le Zhao, Chun Cheng (2024). Forecasting urban traffic states with sparse data using Hankel temporal matrix factorization. INFORMS Journal on Computing. 37 (4): 945-961. [PDF] [Data & Python code]
- Xinyu Chen, Zhanhong Cheng, HanQin Cai, Nicolas Saunier, Lijun Sun (2024). Laplacian convolutional representation for traffic time series imputation. IEEE Transactions on Knowledge and Data Engineering. 36 (11): 6490-6502. [PDF] [Slides] [Blog post] [Data & Python code]
- Xinyu Chen, Chengyuan Zhang, Xiaoxu Chen, Nicolas Saunier, Lijun Sun (2024). Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression. IEEE Transactions on Knowledge and Data Engineering. 36 (2): 504-517. [PDF] [Blog post] [Data & Python code]
- Xinyu Chen, Lijun Sun (2022). Bayesian temporal factorization for multidimensional time series prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44 (9): 4659-4673. [Slides] [Data & Python code]
Selected Blog Posts
- 🔨 Time Series Convolution. Convolutional kernel approaches for understanding the importance of time series trends and interpreting temporal patterns, allowing one to improve the performance of time series imputation and learn sparse representations of temporal correlations.
Research Visualization
- Time Series Periodicity. An interactive visualization tool for showing time series periodicity of Chicago ridesharing trips in two weeks since April 1, 2024.