I am an assistant professor in Northeastern University. I work on machine learning for large-scale spatiotemporal data and its applications. I am generally interested in optimization, deep learning and spatiotemporal modeling. My work has been applied to learning dynamical systems, sports analytics, intelligent transportation, and climate informatics. For more details, see my curriculum vitae and research statement. For prospective students, please read this before emailing me.

Contact Info

  • E-mail: roseyu [at] northeastern [dot] edu.
  • phone: (617) 373-6455
  • Address: 177 Huntington Ave Office 921, Boston, MA 02115

News

Old News

Deep Sequential Models for Spatiotemporal Dynamics

Expressive models for learning complex spatiotemporal dynamics

Our physical world is intrinsically spatiotemporal. We design deep learning models that bridge the expressiveness of neural networks and the rich spatiotemporal structures from the data, addressing fundamental challenges of high-dimensionality, high-order correlation, non-linear dynamics and multi-resolution dependencies.

Read More
  • Long Range Sequence Generation via Multiresolution Adversarial Training
    Yukai Liu, Rose Yu, Stephan Zheng, Yisong Yue.
    [Paper]
  • Neural Lander: Stable Drone Landing Control using Learned Dynamics
    Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung.
    [Paper] [Demo]
  • Long-term Forecasting using Tensor-Train RNNs
    Rose Yu, Stephan Zheng,Anima Anandkumar, Yisong Yue.
    [Paper] [Code]
  • Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
    Yaguang Li, Rose Yu, Cyrus Shahabi,Yan Liu. International Conference on Learning Representations (ICLR), 2018
    [Paper] [Code]

Deep Generative Models for Spatiotemporal Graphs

Structured learning models for spatiotemporal graphs

A graphical model can conveniently encode high-level structures. The nodes of the graph typically represent the problem components, and the edges capture their spatiotemporal interactions. We aim to study a novel deep learning framework with a primary focus on combining graphical models with black-box deep neural networks.

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  • A Neural Framework for Learning DAG to DAG Translation
    M. Clara De Paolis Kaluza, Saeed Amizadeh, Rose Yu.
    [Paper]
  • Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
    Yaguang Li, Rose Yu, Cyrus Shahabi,Yan Liu. International Conference on Learning Representations (ICLR), 2018
    [Paper] [Code]

Interpretable Tensor Models for Spatiotemporal Analysis

High-order tensor models with interpretable factors for domain scientists

Providing interpretable insights for domain scientists is one of the core missions in data-driven decision making. We leverage the efficiency and simplicity of tensor factor models to uncover the hidden spatiotemporal patterns from the data.

Read More
  • Learning Tensor Latent Features
    Sung-En Chang, Xun Zheng, Ian E.H. Yen, Pradeep Ravikumar, Rose Yu.
    [Paper]
  • Multi-resolution Tensor Learning for Large-Scale Spatial Data
    Stephan Zheng, Rose Yu, Yisong Yue.
    [Paper]
  • Tensor Regression Meets Gaussian Processes
    Rose Yu, Guangyu Li, Yan Liu. International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
    [Paper]

Preprints

  • Neural Lander: Stable Drone Landing Control using Learned Dynamics
    Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    [Paper] [Demo]
  • Learning Tensor Latent Features
    Sung-En Chang, Xun Zheng, Ian E.H. Yen ,Pradeep Ravikumar, Rose Yu.
    [Paper]
  • Multi-resolution Tensor Learning for Large-Scale Spatial Data
    Stephan Zheng, Rose Yu, Yisong Yue .
    [Paper]
  • Long-term Forecasting using Tensor-Train RNNs
    Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue.
    [Paper] [Code]
  • Socratic Learning: Empowering the Generative Model
    Paroma Varma, Bryan He, Dan Iter,Peng Xu, Rose Yu, Christopher De Sa, Christopher R‌é.
    [Paper]
  • Conferences

  • Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
    Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu.
    International Conference on Learning Representations (ICLR), 2018
    [Paper] [Code]
  • Tensor Regression Meets Gaussian Processes
    Rose Yu, Guangyu Li, Yan Liu.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
    [Paper]
  • 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
    [Paper]
  • 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
    [Paper]
  • 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]

  • 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
    [Paper]
  • 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
    [Paper]

  • CS 6140 Machine Learning

    Fall 2018

  • Course Website
  • Course Syllabus
  • CS 7180 Special Topics in AI: Deep Learning

    Spring 2019

  • Course Website