Bio

Dr. Xinyu Chen (陈新宇) is now a Postdoctoral Associate at MIT, working with Prof. Jinhua Zhao on addressing a series of data-driven machine learning problems. He is currently involved in the Mens, Manus, and Machina (M3S) project and the Department of Energy (DOE) projects. Prior to joining MIT, he received his Ph.D. degree from the University of Montreal in Canada where he was fortunately supported by the IVADO PhD Excellence Scholarship. His Ph.D. thesis was entitled “Matrix and tensor models for spatiotemporal traffic data imputation and forecasting”. Currently, he focuses on developing some theoretical machine learning algorithms (e.g., unsupervised learning) for modeling a wide range of spatiotemporal data and (computational) social science data. These data are by nature multidimensional tensors collected from real-world systems, including human mobility, trajectory data, traffic flow, fluid flow, climate/weather data, energy consumption data, and international trade data. His research works have been published in top-tier scientific journals, including some latest papers such as

• 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]

and some under review manuscripts such as

• Xinyu Chen, Chengyuan Zhang, Xi-Le Zhao, Nicolas Saunier, Lijun Sun (2024). Forecasting sparse movement speed of urban road networks with nonstationary temporal matrix factorization. Transportation Science. (1st-round review)

All his research papers (~15) have been cited more than 1,000 times on Google Scholar (until August 2024). He is now leading the spatiotemporal data modeling project and addressing many scientific, mathematical, industrial, and engineering problems across the areas of data science, machine learning, and AI for science.