👋 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 of open-source & reproducible research.
- 🤔 Besides coding, I enjoy reading & traveling.
- 💬 Create new ideas about spatiotemporal data modeling.
- 📫 How to reach me: [email protected]
Latest Publications
- Xinyu Chen, Xi-Le Zhao, Chun Cheng (2024). Forecasting urban traffic states with sparse data using Hankel temporal matrix factorization. INFORMS Journal on Computing. [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]
Latest Posts
- 🔨 Time Series Convolution. A convolutional kernel approach for reinforcing the modeling of time series trends and interpreting temporal patterns, allowing one to leverage Fourier transforms and learn sparse representations. The interpretable machine learning models such as sparse regression unlock opportunities to better capture the long-term changes and temporal patterns of real-world time series.