177 Huntington Avenue
Boston, MA 02115
ATTN: Rose Yu, 910 - 177
360 Huntington Avenue
Boston, MA 02115-5000
Dr. Yu is an Assistant Professor in the College of Computer and Information Science. Previously, she was a postdoctoral researcher in Caltech Computing and Mathematical Sciences. She earned her PhD in Computer Sciences at the University of Southern California and was a visiting researcher at Stanford University.
Her research focuses on developing machine learning techniques for large-scale time series and spatiotemporal data. She is generally interested in the theory and applications of sequential decision making, optimization, and spatiotemporal modeling. Her work has been successfully applied to intelligent transportation, climate informatics, and social media anomaly detection. Among her awards, she was nominated as one of the 2015 “MIT Rising Stars in EECS”.
Rose Yu, Dehua Cheng, Yan Liu. ”Accelerated Online Low-Rank Tensor Learning for Multivariate Spatiotemporal Streams.” In Proceedings of the 32th International Conference on Machine Learning (ICML), 2015
Low-rank tensor learning has many applications in machine learning. A series of batch learning algorithms have achieved great successes. However, in many emerging applications, such as climate data analysis, we are confronted with largescale tensor streams, which pose significant challenges to existing solutions. In this paper, we propose an accelerated online low-rank tensor learning algorithm (ALTO) to solve the problem. At each iteration, we project the current tensor to a low-dimensional tensor, using the information of the previous low-rank tensor, in order to perform efficient tensor decomposition, and then recover the low-rank approximation of the current tensor. By randomly selecting additional subspaces, we successfully overcome the issue of local optima at an extremely low computational cost. We evaluate our method on two tasks in online multivariate spatio-temporal analysis: online forecasting and multi-model ensemble. Experiment results show that our method achieves comparable predictive accuracy with significant speed-up.
Rose Yu, Yan Liu. ”Learning from Multiway Data: Simple and Efficient Tensor Regression.” In Proceedings of the 33th International Conference on Machine Learning (ICML), 2016
Tensor regression has shown to be advantageous in learning tasks with multi-directional relatedness. Given massive multiway data, traditional methods are often too slow to operate on or suffer from memory bottleneck. In this paper, we introduce subsampled tensor projected gradient to solve the problem. Our algorithm is impressively simple and efficient. It is built upon projected gradient method with fast tensor power iterations, leveraging randomized sketching for further acceleration. Theoretical analysis shows that our algorithm converges to the correct solution in fixed number of iterations. The memory requirement grows linearly with the size of the problem. We demonstrate superior empirical performance on both multi-linear multi-task learning and spatio-temporal applications.
Rose Yu*, Yaguang Li*, Ugur Demiryurek, Cyrus Shahabi, Yan Liu. ”Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting.” To Appear Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM), 2017
Traffic forecasting is a vital part of intelligent transportation systems. It becomes particularly challenging due to short-term (e.g., accidents, constructions) and long-term (e.g., peak-hour, seasonal, weather) traffic patterns. While most of the previously proposed techniques focus on normal condition forecasting, a single framework for extreme condition traffic forecasting does not exist. To address this need, we propose to take a deep learning approach. We build a deep neural network based on long short term memory (LSTM) units. We apply Deep LSTM to forecast peak-hour traffic and manage to identify unique characteristics of the traffic data. We further improve the model for post-accident forecasting with Mixture Deep LSTM model. It jointly models the normal condition traffic and the pattern of accidents. We evaluate our model on a real-world large-scale traffic dataset in Los Angeles. When trained end-to-end with suitable regularization, our approach achieves 30%–50% improvement over baselines. We also demonstrate a novel technique to interpret the model with signal stimulation. We note interesting observations from the trained neural network.
Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, Yan Liu, ”Latent Space Model for Road Networks to Predict Time-Varying Traffic”, In Proceeding of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016
Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamics associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learn the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly with given data. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the utility superiority of our framework for real-time traffic prediction on large road networks over competitors as well as a baseline graph-based LSM.
Rose Yu, Andrew Gelfand, Suju Rajan, Cyrus Shahabi, Yan Liu. ”Geographic Segmentation via Latent Poisson Factor Model.” in ACM International Conference on Web Search and Data Mining (WSDM), 2016
Discovering latent structures in spatial data is of critical importance to understanding the user behavior of location-based services. In this paper, we study the problem of geographic segmentation of spatial data, which involves dividing a collection of observations into distinct geo-spatial regions and uncovering abstract correlation structures in the data. We introduce a novel, Latent Poisson Factor (LPF) model to describe spatial count data. The model describes the spatial counts as a Poisson distribution with a mean that factors over a joint item-location latent space. The latent factors are constrained with weak labels to help uncover interesting spatial dependencies. We study the LPF model on a mobile app usage data set and a news article readership data set. We empirically demonstrate its effectiveness on a variety of prediction tasks on these two data sets.
• Rose Yu*, Mohammad Taha Bahadori*, Yan Liu. ”Fast Multivariate Spatio-temporal Analysis via Low-Rank Tensor Learning.” In Proceeding of Advances in Neural Information Processing Systems (NIPS), 2014 Spotlight
Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications. Existing models usually assume simple inter-dependence among variables, space, and time, and are computationally expensive. We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis, which can conveniently incorporate different properties in spatio-temporal data, such as spatial clustering and shared structure among variables. We demonstrate how the general framework can be applied to cokriging and forecasting tasks, and develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantee. We conduct experiments on both synthetic datasets and real application datasets to demonstrate that our method is not only significantly faster than existing methods but also achieves lower estimation error.
Rose Yu, Xinran He, Yan Liu. ”GLAD: Group Anomaly Detection in Social Media Analysis.” In Proceeding of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),2014
Traditional anomaly detection on social media mostly focuses on individual point anomalies while anomalous phenomena usually occur in groups. Therefore it is valuable to study the collective behavior of individuals and detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications. In this paper, we take a generative approach by proposing a hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD takes both pair-wise and point-wise data as input, automatically infers the groups and detects group anomalies simultaneously. To account for the dynamic properties of the social media data, we further generalize GLAD to its dynamic extension d-GLAD. We conduct extensive experiments to evaluate our models on both synthetic and real world datasets. The empirical results demonstrate that our approach is effective and robust in discovering latent groups and detecting group anomalies.