Chao Zhang

The goal of my research is to develop machine learning methods that model large-scale data to solve practical and challenging problems in science and engineering. I work a lot with spatiotemporal data, text data, and graph data. In particular, I have been focused on the following topics: (1) knowledge extraction and text mining; (2) spatiotemporal data analysis; (3) uncertainty quantification for deep learning; (4) learning from limited supervision; (5) generation and optimization. More broadly, I am interested in the following topics:

• Data Mining: Spatiotemporal Data Mining, Text Mining, Graph Mining
• Machine Learning: Self-Supervised Learning, Weakly-Supervised Learning, Uncertainty Quantification, Decision Making Under Uncertainty
• Natural Language Processing: Information Extraction

On the application side, I am passionate about interdisciplinary research and enjoy developing data-driven solutions to accelerate scientific discovery through close collaboration with domain experts. The techniques I develop are motivated by applications in material science, biomedical science, transportation, and public health.

• Spatiotemporal dynamics modeling: Space and time are two most important dimensions in many science and engineering applications. We develop deep learning models that can model and predict the dynamics of spatial systems, at both macro-level (e.g., transportation, epidemiology) and micro-level (e.g., molecular dynamics). We are also interested in jointly modeling humans' spatiotemporal activities and the associated semantics.
  • T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction, IEEE ITS 2020
  • A Linear Time Approach to Computing Time Series Similarity based on Deep Metric Learning, TKDE 2020
  • State-Sharing Sparse Hidden Markov Models for Personalized Sequences, KDD 2019
  • DeepMove: Predicting Human Mobility with Attentional Recurrent Networks, WWW 2018
  • Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning, WWW 2017
  • Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation, KDD 2017
  • TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams, KDD 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
• Knowledge extraction and text mining: The capability of automatically and accurately extracting knowledge from massive text data is crucial for many applications. We develop tools that can automatically extract information about entities, relations, taxonomies, and events from massive unstructured data (e.g., scientific literature). We focus on tackling the challenges of low-resource information extraction and multi-modal information extraction.
  • BERTifying Hidden Markov Models for Multi-Source Weakly Supervised Named Entity Recognition, ACL 2021
  • BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision, KDD 2020
  • STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths, KDD 2020
  • TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering, KDD 2018
• Uncertainty quantification and exploitation: For many applications, it's important that a model produces calibrated uncertainty to reflect its own confidence, and has the ability to say "I don't know" when encountering unknown situations. Unfortunately, many deep learning models produce uncertainty-agnostic point estimates or miscalibrated distributions. We develop techniques that quantify uncertainty for deep learning models and understand where the uncertainty comes from. We also study how to exploit uncertainty for active learning, interactive learning, and experiment design.
  • CAMUL: Calibrated and Accurate Multi-view Time-Series Forecasting, WWW 2022
  • AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models, NAACL 2022
  • When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting, NeurIPS 2021
  • SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates, ICML 2020
  • Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data, EMNLP 2020
  • SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup, EMNLP 2020
  • RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations, UbiComp 2018
• Learning with limited/weak supervision: The lack of training data is a major bottleneck for training deep learning models. We study weakly-supervised learning: how to learn accurate models using weakly-labeled data that can be easily obtained from external knowledge? Our recent works combine self-supervised learning with self-training as a generic approach to learning from weak supervision.
  • PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning, ACL 2022
  • Self-Training with Differentiable Teacher, NAACL-Findings 2022
  • CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data, NAACL 2022
  • Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization, NeurIPS 2021
  • Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach, NAACL 2021
  • Text Classification Using Label Names Only: A Language Model Self-Training Approach, EMNLP 2020
  • Weakly-Supervised Neural Text Classification, CIKM 2018
• Guided Generative Models: We study how to guide deep generative models to design samples that satisfy pre-determined properties.
  • Transformer-Based Neural Text Generation with Syntactic Guidance, preprint
  • A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization, ICML 2019

• Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
  Yinghao Li, Le Song, Chao Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2022
• Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction
  Rongzhi Zhang, Rebecca West, Xiquan Cui, Chao Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2022
• CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data
  Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022
• AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models
  Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022
• Self-Training with Differentiable Teacher
  Simiao Zuo, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang, Tuo Zhao, Hongyuan Zha
  Findings of Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-Findings), 2022
• PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
  Rongzhi Zhang, Yue Yu, Shetty Pranav, Le Song and Chao Zhang
  Annual Meeting of the Association for Computational Linguistics (ACL), 2022.
• Rule-Enhanced Active Learning for Semi-Automated Weak Supervision
  David Kartchner, Davi Nakajima An, Wendi Ren, Chao Zhang, Cassie S. Mitchell
  AI 3(1), 211-228, 2022
• CAMUL: Calibrated and Accurate Multi-view Time-Series Forecasting
  Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang and B. Aditya Prakash
  The Web Conference (WWW), 2022

• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
  Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2021
• Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
  Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, and Jiawei Han
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2021
• BERTifying Hidden Markov Models for Multi-Source Weakly Supervised Named Entity Recognition
  Yinghao Li, Pranav Shetty, Lucas Liu, Chao Zhang, Le Song
  Annual Meeting of the Association for Computational Linguistics (ACL), 2021
• Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
  Yue Yu*, Simiao Zuo*, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021
• Learning from Language: Low-shot Named Entity Recognition via Decomposed Framework
  Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao
  Findings of Conference on Empirical Methods in Natural Language Processing (EMNLP-Findings), 2021
• Semantics-Aware Hidden Markov Model for Human Mobility
  Hongzhi Shi, Yong Li, Hancheng Cao, Xiangxin Zhou, Chao Zhang, Vassilis Kostakos
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.
• Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning
  Jung-Hyun Kim, Chao Zhang, Simon I. Briceno and Dimitri N. Mavris
  AIAA Scitech Forum, 2021
• SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
  Yue Yu*, Kexin Huang*, Chao Zhang, Lucas M. Glass, Jimeng Sun, Cao Xiao
  Bioinformatics, 2021

• T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
  Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li.
  IEEE Transactions on Intelligent Transportation Systems (T-ITS), 21(9), 3848–3858, 2020
• ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision
  David Kartchner, Wendi Ren, Davi Nakajima An, Chao Zhang, Cassie Mitchell.
  NeurIPS 2020 HAMLETS workshop on Human and Model in the Loop Evaluation and Training Strategies
• Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
  Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao and Chao Zhang.
  Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
• Text Classification Using Label Names Only: A Language Model Self-Training Approach
  Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han.
  Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
• SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
  Rongzhi Zhang, Yue Yu and Chao Zhang.
  Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
• Denoising Multi-Source Weak Supervision for Neural Text Classification
  Wendi Ren, Yinghao Li, Hanting Su, David Kartchner, Cassie Mitchell, and Chao Zhang.
  Findings of Conference on Empirical Methods in Natural Language Processing (EMNLP-Findings), 2020
• SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
  Lingkai Kong, Jimeng Sun, Chao Zhang.
  International Conference on Machine Learning (ICML), 2020
• STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
  Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun and Chao Zhang.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2020
• BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision
  Chen Liang*, Yue Yu*, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao and Chao Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2020
• LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values
  Kejing Yin, Ardavan Afshar, Joyce Ho, William Cheung, Chao Zhang and Jimeng Sun
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2020
• Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
  Yu Meng, Yunyi Zhang, Jiaxin Huang, Yu Zhang, Chao Zhang and Jiawei Han
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2020
• Joint Aspect-Sentiment Analysis with Minimal User Guidance
  Honglei Zhuang, Fang Guo, Chao Zhang, Liyuan Liu and Jiawei Han.
  ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020.
• paper2repo: GitHub Repository Recommendation for Academic Papers
  Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang and Tarek Abdelzaher.
  The Web Conference (WWW), 2020
• Discriminative Topic Mining via Category-Name Guided Text Embedding
  Yu Meng, Jiaxin Huang, Guangyuan Wang, Zihan Wang, Chao Zhang, Yu Zhang and Jiawei Han.
  The Web Conference (WWW), 2020
• A Linear Time Approach to Computing Time Series Similarity based on Deep Metric Learning
  Di Yao, Gao Cong, Chao Zhang, Xuying Meng, Rongchang Duan, Jingping Bi
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020

• Multidimensional Mining of Massive Text Data
  Chao Zhang, Jiawei Han.
  Morgan & Claypool Publishers, 2019
• Spherical Text Embedding
  Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance Kaplan, Jiawei Han.
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2019
• State-Sharing Sparse Hidden Markov Models for Personalized Sequences
  Hongzhi Shi, Chao Zhang, Mingquan Yao, Yong Li, Funing Sun, Depeng Jin.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2019
• TopicMine: User-Guided Topic Mining by Category-Oriented Embedding
  Yu Meng, Jiaxin Huang, Zihan Wang, Chenyu Fan, Guangyuan Wang, Chao Zhang, Jingbo Shang, Lance Kaplan, Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2019
  (Demo)
• CubeNet: Multi-Facet Hierarchical Heterogeneous Network Construction, Analysis, and Mining
  Carl Yang, Dai Teng, Siyang Liu, Sayantani Basu, Jieyu Zhang, Jiaming Shen, Chao Zhang, Jingbo Shang, Lance Kaplan, Timothy Harratty, and Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2019
  (Demo)
• A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization
  Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng.
  International Conference on Machine Learning (ICML), 2019
• Weakly-Supervised Hierarchical Text Classification
  Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han.
  AAAI Conference on Artificial Intelligence (AAAI), 2019
• Computing Trajectory Similarity in Linear Time: A Generic Seed-Guided Neural Metric Learning Approach
  Di Yao, Gao Cong, Chao Zhang, Jingping Bi.
  IEEE International Conference on Data Engineering (ICDE), 2019
• DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data
  Jie Feng, Mingyang Zhang, Huandong Wang, Zeyu Yang, Chao Zhang, Yong Li, Depeng Jin.
  The Web Conference (WWW), 2019
• GeoAttn: Localization of Social Media Messages Via Attentional Memory Network
  Sha Li, Chao Zhang, Dongming Lei, Ji Li, Jiawei Han.
  SIAM International Conference on Data Mining (SDM), 2019
• Semantics-Aware Hidden Markov Model for Human Mobility
  Hongzhi Shi, Hancheng Cao, Xiangxin Zhou, Yong Li, Chao Zhang, Vassilis Kostakos, Funing Sun, Fanchao Meng.
  SIAM International Conference on Data Mining (SDM), 2019

• Multi-Dimensional Mining of Unstructured Data with Limited Supervision
  Chao Zhang
  Ph.D. Thesis
  (ACM SIGKDD 2019 Dissertation Runner-up Award)
• TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering
  Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2018
  (Code) (Data)
  
• HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
  Jiaming Shen, Zeqiu Wu, Dongming Lei, Chao Zhang, Xiang Ren, Michelle T. Vanni, Brian M. Sadler, Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2018
• Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
  Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2018
• Towards Multidimensional Analysis of Text Corpora
  Jingbo Shang, Chao Zhang, Jiaming Shen, Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2018
  (Tutorial)
• DeepMove: Predicting Human Mobility with Attentional Recurrent Networks
  Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, Depeng Jin.
  The International World Wide Web Conference (WWW), 2018
  (Code & Data)
  
• Weakly-Supervised Neural Text Classification
  Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han.
  ACM International Conference on Information and Knowledge Management (CIKM), 2018
  (Code)
• Open-Schema Event Profiling for Massive News Corpora
  Quan Yuan, Xiang Ren, Wenqi He, Chao Zhang, Xinhe Geng, Lifu Huang, Heng Ji, Chin-Yew Lin, Jiawei Han.
  ACM International Conference on Information and Knowledge Management (CIKM), 2018
• Spatiotemporal Activity Modeling Under Data Scarcity: A Graph-Regularized Cross-Modal Embedding Approach
  Chao Zhang, Mengxiong Liu, Zhengchao Liu, Carl Yang, Luming Zhang, and Jiawei Han.
  AAAI Conference on Artificial Intelligence (AAAI), 2018
• A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling
  Wanzheng Zhu +, Chao Zhang +, Shuochao Yao, Xiaobin Gao, and Jiawei Han.
  AAAI Conference on Artificial Intelligence (AAAI), 2018
• Doc2Cube: Allocating Documents to Text Cube without Labeled Data
  Fangbo Tao +, Chao Zhang +, Xiusi Chen, Meng Jiang, Tim Hanratty, Lance Kaplan, Jiawei Han.
  IEEE International Conference on Data Mining (ICDM), 2018
  (Code)
  
• RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations
  Shuochao Yao, Yiran Zhao, Huajie Shao, Aston Zhang, Chao Zhang, Shen Li, and Tarek Abdelzaher.
  Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2018
• SenseGAN: Enabling Deep Learning for Internet of Things with a Semi-Supervised Framework
  Shuochao Yao, Yiran Zhao, Huajie Shao, Chao Zhang, Aston Zhang, Shaohan Hu, Dongxin Liu, Shengzhong Liu, and Tarek Abdelzaher.
  Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2018
  (Distinguished Paper Award)
• Deep Learning for the Internet of Things
  Shuochao Yao, Yiran Zhao, Aston Zhang, Huajie Shao, Chao Zhang, Lu Su, Tarek Abdelzaher.
  IEEE Computer, 2018
• GeoBurst+: Effective and Real-Time Local Event Detection in Geo-Tagged Tweet Streams
  Chao Zhang, Dongming Lei, Quan Yuan, Honglei Zhuang, Lance Kaplan, Shaowen Wang, Jiawei Han.
  ACM Transactions on Intelligent Systems and Technology (TIST), 2018
• Leveraging the Power of Informative Users for Local Event Detection
  Hengtong Zhang, Fenglong Ma, Yaliang Li, Chao Zhang, Tianqi Wang, Yaqing Wang, Jing Gao, Lu Su.
  IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018
• Learning deep representation for trajectory clustering
  Di Yao, Chao Zhang, Zhihua Zhu, Qin Hu,heng Wang, Jianhui Huang, Jingping Bi.
  Expert Systems, 2018.
• Did You Enjoy the Ride: Understanding Passenger Experience via Heterogeneous Network Embedding
  Carl Yang, Chao Zhang, Jiawei Han, Xuewen Chen, and Jieping Ye.
  IEEE International Conference on Data Engineering (ICDE), 2018
• ApDeepSense: Deep Learning Uncertainty Estimation without the Pain for IoT Applications
  Shuochao Yao, Yiran Zhao, Huajie Shao, Chao Zhang, Aston Zhang, Dongxin Liu, Shengzhong Liu, Lu Su, Tarek Abdelzaher.
  IEEE International Conference on Distributed Computing Systems (ICDCS), 2018
• A Constrained Maximum Likelihood Estimator for Unguided Social Sensing
  Huajie Shao, Shuochao Yao, Yiran Zhao, Chao Zhang, Jinda Han, Lance Kaplan, Su Lu, and Tarek Abdelzaher.
  IEEE International Conference on Computer Communications (InfoCom), 2018
• Towards Personalized Activity Level Prediction in Community Question Answering Websites
  Zhenguang Liu, Yingjie Xia, Qi Liu, Qinming He, Yanxiang Chen, Chao Zhang, and Roger Zimmermann.
  ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2018

• TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams
  Chao Zhang, Liyuan Liu, Dongming Lei, Quan Yuan, Honglei Zhuang, Tim Hanratty, and Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2017
  (Slides) (Code) (Video) (Featured by Illinois Innovator)
• Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation
  Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan and Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2017.
  (Code & Data)
• ReAct: Online Multimodal Embedding for Recency-Aware Spatiotemporal Activity Modeling
  Chao Zhang, Keyang Zhang, Quan Yuan, Fangbo Tao, Luming Zhang, Tim Hanratty, and Jiawei Han.
  ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017
  (Slides) (Code) (Data)
• Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning
  Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, and Jiawei Han.
  International World Wide Web Conference (WWW), 2017
  (Slides) (Code & Data)
• Bringing Semantics to Spatiotemporal Data Mining: Challenges, Methods, and Applications
  Chao Zhang, Quan Yuan, and Jiawei Han.
  IEEE International Conference on Data Engineering (ICDE), 2017
  (Tutorial)
• PRED: Periodic Region Detection for Mobility Modeling of Social Media Users
  Quan Yuan, Wei Zhang, Chao Zhang, Xinhe Geng, Gao Cong, and Jiawei Han.
  ACM International Conference on Web Search and Data Mining (WSDM), 2017
  (Code & Data)
  
• Towards Space and Time Coupled Social Media Analysis
  Chao Zhang, Quan Yuan, Shi Zhi, Sha Li, and Jiawei Han.
  2017 ACM International Conference on Information and Knowledge Management (CIKM), 2017
  (Tutorial)
• Detecting Multiple Periods and Periodic Patterns in Event Time Sequences
  Quan Yuan, Jingbo Shang, Xin Cao, Chao Zhang, Xinhe Geng, Jiawei Han.
  ACM International Conference on Information and Knowledge Management (CIKM), 2017
• SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories
  Di Yao, Chao Zhang, Jianhui Huang, and Jingping Bi
  ACM International Conference on Information and Knowledge Management (CIKM), 2017
  (Code & Data)
• Urbanity: A System for Interactive Exploration of Urban Dynamics from Streaming Human Sensing Data
  Mengxiong Liu, Zhengchao Liu, Chao Zhang, Keyang Zhang, Quan Yuan, Tim Hanratty, and Jiawei Han
  ACM International Conference on Information and Knowledge Management (CIKM), 2017
  (Demo)
• ClaimVerif: A Real-time Claim Verification System Using the Web and Fact Databases
  Shi Zhi, Yicheng Sun, Jiayi Liu, Chao Zhang, and Jiawei Han.
  ACM International Conference on Information and Knowledge Management (CIKM), 2017
• Trajectory Clustering via Deep Representation Learning
  Di Yao, Chao Zhang, Zhihua Zhu, Jianhui Huang, and Jingping Bi.
  International Joint Conference on Neural Networks (IJCNN), 2017
  (Code)
• pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
  Julie Yixuan Zhu +, Chao Zhang +, Huichu Zhang, Shi Zhi, Victor O.K. Li, Jiawei Han, and Yu Zheng.
  IEEE Transactions on Big Data (TBD), 2017
• Geographical Data Mining
  Chao Zhang and Jiawei Han.
  The International Encyclopedia of Geography: People, the Earth, Environment and Technology, 2017
• A Survey on Spatiotemporal and Semantic Data Mining
  Quan Yuan, Chao Zhang, Jiawei Han.
  Trends in Spatial Analysis and Modelling, Springer, 2017

• GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media
  Chao Zhang, Keyang Zhang, Quan Yuan, Luming Zhang, Tim Hanratty, and Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2016
  (Slides) (Code & Data)
  
• GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams
  Chao Zhang, Guangyu Zhou, Quan Yuan, Honglei Zhuang, Yu Zheng, Lance Kaplan, Shaowen Wang, Jiawei Han.
  ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016
  (Slides) (Code & Data)
  
• Mining Contiguous Sequential Generators in Biological Sequences
  Jingsong Zhang, Yinglin Wang, Chao Zhang, and Yongyong Shi
  Transactions on Computational Biology and Bioinformatics (TCBB), 13(5): 855–867, 2016
• Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data
  Chao Zhang, Yu Zheng, Xiuli Ma, Jiawei Han.
  ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2015
• Fast Inbound Top-K Query for Random Walk with Restart
  Chao Zhang, Shan Jiang, Yucheng Chen, Yidan Sun, Jiawei Han.
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2015
  (Best Student Paper Runner-up Award)
• StreamCube: Hierarchical Spatio-temporal Hashtag Clustering for Event Exploration over the Twitter Stream
  Wei Feng, Chao Zhang, Wei Zhang, Jiawei Han, Jianyong Wang, Charu Aggarwal, Jianbin Huang.
  IEEE International Conference on Data Engineering (ICDE), 2015

• Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories
  Chao Zhang, Jiawei Han, Lidan Shou, Jiajun Lu, Thomas La Porta.
  International Conference on Very Large Data Bases (VLDB), 2014
  (Slides) (Code & Data)
  
• Trendspedia: An Internet Observatory for Analyzing and Visualizing the Evolving Web
  Wei Kang, Anthony K. H. Tung, Wei Chen, Xinyu Li, Qiyue Song, Chao Zhang, Feng Zhao, Xiajuan Zhou.
  IEEE International Conference on Data Engineering (ICDE), 2014
• Supporting Pattern-Preserving Anonymization for Time-Series Data
  Lidan Shou, Xuan Shang, Ke Chen, Gang Chen, Chao Zhang.
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 25(4): 877-892, 2013
• Evaluating Geo-Social Influence in Location-Based Social Networks
  Chao Zhang, Lidan Shou, Ke Chen, Gang Chen, Yijun Bei.
  ACM International Conference on Information and Knowledge Management (CIKM), 2012
• See-To-Retrieve: Efficient Processing of Spatio-Visual Keyword Queries
  Chao Zhang, Lidan Shou, Ke Chen, Gang Chen.
  ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2012
• What-You-Retrieve-Is-What-You-See: A Preliminary Cyber-Physical Search Engine
  Lidan Shou, Ke Chen, Gang Chen, Chao Zhang, Yi Ma, Xian Zhang.
  ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2011