Dr. Xinyu Chen (陈新宇) is now a Postdoctoral Associate at MIT, working with Prof. Jinhua Zhao on addressing a wide range of data-driven machine learning challenges. 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 from the Montreal Institute for Data Valorization. His Ph.D. thesis was entitled “Matrix and tensor models for spatiotemporal traffic data imputation and forecasting”. Currently, he focuses on developing theoretical and interpretable 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 the top-tier scientific journals of the fields of Data Science, Machine Learning, Optimization, and Transportation.