Research interests

Our research goal is to enable machines to turn massive data into actionable insights and intelligent decisions faster and with less supervision. Revolving around this goal, we study the following topics in data mining and machine learning:
- Data Mining: text mining; spatiotemporal data mining; time series analysis
- Machine Learning: semi-supervised learning; few-shot learning; deep learning

Representative publications

Please find below some of our representative works:
- Weakly-Supervised Neural Text Classification, CIKM 2018
- TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering, KDD 2018
- SenseGAN: Enabling Deep Learning for Internet of Things with a Semi-Supervised Framework, IMWUT 2018
- Doc2Cube: Allocating Documents to Text Cube without Labeled Data, ICDM 2018
- Spatiotemporal Activity Modeling Under Data Scarcity: A Graph-Regularized Cross-Modal Embedding Approach, AAAI 2018
- Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation, KDD 2017
- Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning, WWW 2017
- GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media, KDD 2016
- GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams, SIGIR 2016

Ongoing projects

Low-Resource Text Mining

Turning text into knowledge with weak supervision.

Multimodal Data Analytics

Multimodal learning on text-rich spatiotemporal data.