I am a postdoctoral researcher in Caltech Computing and Mathematical Sciences. Before that, I received my Ph.D. degree in Computer Science from University of Southern California.

I work on machine learning for time series/spatiotemporal data and its applications, especially in the field of computational sustainability. I am generally interested in sequential decision making, optimization and spatiotemporal modeling. My work has been applied to intelligent transportation, climate informatics, and social media anomaly detection. For more details, see my curriculum vitae and research statement.

In fall 2018, I will be joining Northeastern University as an assistant professor. And I am looking for talented students who are excited about machine learning! Read More

Contact Info

  • E-mail: rose [at] caltech [dot] edu.
  • Address: 1200 E California Blvd 305-16, Pasadena, CA 91125


Old News

Tensor Learning for Spatiotemporal Analysis

A scalable framework for analyzing spatiotemporal data

Large-scale spatiotemporal data such as climate measurements, location-based social networks have complex structures. In this project, we develop a scalable framework based on low-rank tensor learning to analyze them. We propose a series of algorithms ranging from offline, online to memory efficient learning to solve the problem. We show that our framework can produce accurate predictions in a scalable way and discover interesting patterns in spatiotemporal data.

ICML 2016 ICML 2015 NIPS 2014

Spatiotemporal Models for Traffic Prediction

A spatiotemporal approach to predict road network traffic

Given a series of road network snapshots, we propose a latent space model to capture its topological and temporal properties. We present an incremental online algorithm which sequentially and adaptively learns the spatiotemporal patterns. Our framework enables real-time traffic prediction on a large volumne of Los Angeles traffic sensor data.

SDM 2017 KDD 2016

Social Media Group Anomaly Detection

A probabilistic model to detect anomalous groups in social media

Traditional anomaly detection techniques focus on individual point anomalies. With the recent popularity of social media, new types of anomalous behaviors arise. We study the collective behavior of individuals and detect group anomalies. We take a generative approach by proposing a hierarchical Bayes model. Our approach provides a robust and interpretable way of discovering latent groups and detecting group anomalies.

KDD Exploration 2016 TKDD 2015 KDD 2014


  • Long-term Forecasting using Tensor-Train RNNs
    Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue.
  • Tensor Regression Meets Gaussian Processes
    Rose Yu, Guangyu Li, Yan Liu.
  • Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
    Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu.
  • Socratic Learning: Empowering the Generative Model
    Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher R‌é.
  • Conferences

  • Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting
    Rose Yu*, Yaguang Li*, Ugur Demiryurek, Cyrus Shahabi, Yan Liu.
    SIAM International Conference on Data Mining (SDM), 2017
  • Learning from Multiway Data: Simple and Efficient Tensor Regression
    Rose Yu, Yan Liu.
    International Conference on Machine Learning (ICML), 2016
    [Paper] [Slide] [BibTex]
  • Latent Space Model for Road Networks to Predict Time-Varying Traffic
    Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, Yan Liu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016
  • Geographic Segmentation via Latent Poisson Factor Model
    Rose Yu, Andrew Gelfand, Suju Rajan, Cyrus Shahabi, Yan Liu.
    ACM International Conference on Web Search and Data Mining (WSDM), 2016
    [Paper] [BibTex]
  • Accelerated Online Low-Rank Tensor Learning for Multivariate Spatio-Temporal Streams
    Rose Yu, Dehua Cheng, Yan Liu.
    International Conference on Machine Learning (ICML), 2015
    [Code] [Paper] [Supplementary] [Video] [Slide] [BibTex]
  • Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
    Rose Yu*, Mohammad Taha Bahadori*, Yan Liu. (*Equal Contributions)
    Advances in Neural Information Processing Systems (NIPS), 2014 Spotlight
    [Code] [Paper] [Supplementary] [BibTex]
  • GLAD: Group Anomaly Detection in Social Media Analysis
    Rose Yu, Xinran He, Yan Liu.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014
    [Code] [ACM Dataset] [Slide] [Paper-Extended Version] [BibTex]
  • A Feasible Nonconvex Relaxation Approach to Feature Selection
    Cuixia Gao, Naiyan Wang, Qi Yu, Zhihua Zhang.
    AAAI Conference on Artificial Intelligence (AAAI), 2011
    [Paper] [BibTex]

  • Dark Pixel Detection: A Novel Single Image Dehaze Approach
    Qi Yu*, Zhihao Ding*, RongRong, Zhenyue Zhang, Donghui Wang.
    Image and Vision Computing New Zealand (IVCNZ), 2011
    [Paper] [BibTex]
  • Journals

  • Spatio-Temporal Analysis of Social Media Data
    Rose Yu, Yan Liu.
    Encyclopedia of GIS , 2016
  • A Survey on Social Media Anomaly Detection
    Rose Yu, Huida Qiu, Zhen Wen, Ching-Yung Lin, Yan Liu
    ACM SIGKDD Explorations , 2016
  • GLAD: Group Anomaly Detection in Social Media Analysis (journal version)
    Rose Yu, Xinran He, Yan Liu.
    ACM Transactions on Knowledge Discovery in Data (TKDD), 2015