I am a strong advocate of open-source and reproducible research. My research projects are available on GitHub.

Granted Research Funding

  • City-Scale Traffic Data Imputation and Forecasting with Tensor Learning
    Abstract: With recent advances in sensing technologies, large-scale and multidimensional urban traffic data are collected on a continuous basis from both traditional fixed traffic sensing systems (e.g., loop detectors and video cameras) and emerging crowdsourcing/floating sensing systems (e.g., GPS trajectory from taxis/buses and Google Waze). These data sets have provided us with unprecedented opportunities for sensing and understanding urban traffic dynamics and developing efficient and reliable smart transportation solutions. For example, forecasting the demand and states (e.g., speed and volume) of urban traffic is essential to a wide range of intelligent transportation system applications such as trip planning, travel time estimation, route planning, traffic signal control, to name just a few. However, there are two critical issues that undermine the use of these data sets in real-world applications: (1) the missing data and noisy nature make it difficult to get the true signal, and (2) it is computationally expensive to process large-scale data sets for online applications (e.g., traffic prediction). The goal of this project is to develop new framework to better model local consistencies in spatiotemporal traffic data, such as the sensor dependencies and temporal dependencies resulting from traffic flow dynamics. The scientific objectives are to: (1) develop nonconvex low-rank matrix/tensor completion models considering spatiotemporal dependencies/correlations (e.g., graph Laplacian [spatial] and time series [temporal]) and traffic domain knowledge (e.g., fundamental diagram, traffic equilibrium, and network flow conservation); (2) incorporate Gaussian process kernels and neural network structure.
    Main contributors: Xinyu Chen (principal investigator), Nicolas Saunier (supervisor)
    Funding from: Institute for Data Valorisation (IVADO)
    Amount: $100,000

Open-Source Research Projects

  • transdim: Machine learning for transportation data imputation and prediction.
    Question: Machine learning models make important developments in the field of spatiotemporal data modeling - like how to forecast near-future traffic states of road networks. But what happens when these models are built on incomplete data commonly collected from real-world systems (e.g., transportation system)?
    Main contributors: Xinyu Chen, Jinming Yang, Yixian Chen, Nicolas Saunier, Lijun Sun
    Time: September 23, 2018 - present
    Impacts: 1,000+ stars & 280+ forks on GitHub.

  • tracebase: Multivariate time series forecasting on high-dimensional and sparse Uber movement speed data.
    About this project: Uber movement project provides data and tools for cities to more deeply understand and address urban transportation problems and challenges. Uber movement speed data measure hourly street speeds across a city (e.g., New York City, Seattle, and London) to enable data-driven city planning and decision making. These data are indeed multivariate time series with N road segments and T time steps (hours), and are featured as high-dimensional, sparse, and nonstationary. To overcome the challenge created by these complicated data behaviors, we propose a temporal matrix factorization framework for multivariate time series forecasting on high-dimensional and sparse Uber movement speed data.
    Main contributors: Xinyu Chen
    Time: November 29, 2020 - present
    Impacts: 40+ stars on GitHub.

Open-Source Book/Tutorial Projects

  • tensor-book: Tensor computations: An algebraic perspective (in Chinese).
    项目简介:本项目是从机器学习模型及数据科学问题出发,主要关注内容涵盖线性代数到张量计算,旨在提供一系列中文学习素材与教程。截至目前,正在更新的教程包括 《机器学习与时空数据建模》 (采用张量计算技术对时空数据进行分析与挖掘,以求揭示时空交通数据所蕴含的复杂数据本真特性,如动态性、高维性、多维性与稀疏性,同时,本教程会囊括一系列科学计算与人工智能相关研究。已更新超过100页)。
    Main contributors: 陈新宇、伍元凯、赵熙乐、孙立君
    Time: June 5, 2022 - present
    Impacts: 60+ stars on GitHub.

  • drawing

  • latex-cookbook: Academic writing with LaTeX: A tutorial (in Chinese).
    《LaTeX论文写作教程》简介:LaTeX作为一款高质量文档排版系统,是数学、物理、计算机等众多理工科领域科研工作者撰写科技论文的常用工具之一。 本教程从LaTeX的发展历程出发,以科技文档写作为中心,介绍LaTeX的使用方法,内容涵盖LaTeX文档类型介绍、文本编辑、公式编辑、图表设计、文献引用、幻灯片制作等。 为提高实操性并帮助读者快速熟悉LaTeX,本书就LaTeX使用方法提供了一系列实例与代码。
    Main contributors: Xinyu Chen, Jieling Jin, Qionghua Liao, Chengyuan Zhang, Xiaoxu Chen
    Time: March 28, 2021 - present
    Impacts: 1,100+ stars & 100+ forks on GitHub.
    Publication info: 陈新宇,金杰灵,廖琼华,张程远,陈晓旭. LaTeX论文写作教程 (Academic Writing with LaTeX). 清华大学出版社,2023.
    Paperback (纸质书): 清华大学出版社京东自营官方旗舰店


Open-Source Programming Projects