Chao Zhang
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Assistant Professor School of Computational Science and Engineering College of Computing Georgia Institute of Technology Office: CODA 1309 Address: 756 W Peachtree St NW, Atlanta, GA 30308 Email: chaozhang@gatech.edu |
Research
My research focuses on developing machine learning and data-driven models to address practical and challenging problems in science and engineering. I am particularly interested in the following topics:
- Large Language Models – Studying how to use language as a general interface and computation to solve different tasks.
- Learning from Weak Supervision – Teaching machines to learn from incomplete and limited data.
- Uncertainty Quantification and Decision Making – Developing machine learning models that can handle and account for uncertainties to make informed decisions.
- Spatiotemporal Dynamics and Design – Using machine learning to simulate and forecast spatiotemporal dynamics (e.g., molecular simulation) and optimize and design spatiotemporal systems.
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.
Acknowledgment: My work has been generously supported by research funding/gift from NSF (IIS CAREER-2144338, IIS-2106961, IIS-2008334), ONR MURI , Kolon, HomeDepot, and Adobe. My work has also been recognized by an NSF CAREER Award, a Facebook Faculty Award, an Amazon AWS Machine Learning Research Award, a Google Faculty Research Award, a Kolon Faculty Fellowship, an ACM SIGKDD Dissertation Runner-up Award, and several paper awards from IMWUT (UbiComp), ECML/PKDD, and ML4H.
Projects
Below are the main research projects at my group and some recent representative works:
- Learning from Limited/Weak Supervision: Extracting information (e.g., entities, relations, events) from unstructured documents is a crucial task, but it often faces the challenge of lacking annotated data for model training. To address this issue, we combine pre-trained language models (e.g., BERT) with weakly-labeled data induced from labeling rules. We have developed techniques for fine-tuning language models with weak supervision and discovering rules from language models for interactive weak supervision.
- Fine-tuning pre-trained models with weak supervision:
- Fine-Tuning Pre-trained Language Model with Weak Supervision, NAACL 2021
- BERTifying Hidden Markov Models for Multi-Source Weakly Supervised Named Entity Recognition, ACL 2021
- Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition, KDD 2022
- Text Classification Using Label Names Only: A Language Model Self-Training Approach, EMNLP 2020
- Interactive weakly-supervised learning to close the gap between weak & full supervision:
- Fine-tuning pre-trained models with weak supervision:
- Uncertainty Quantification & Decision Making: Uncertainty-aware ML models are crucial for building trustworthy AI systems. Unfortunately, many deep learning models produce uncertainty-agnostic point estimates or miscalibrated distributions. To address this, we have been developing techniques (1) for quantifying uncertainty in deep learning models and (2) for exploiting uncertainty to inform downstream decision-making.
- End-to-end Stochastic Programming with Energy-based Model, NeurIPS 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
- RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations, UbiComp 2018
- Spatiotemporal Dynamics and Design: We study spatiotemporal dynamics and design in scientific and engineering applications. Our focus spans from macro-level systems such as transportation and epidemiology to micro-level systems such as molecules. Our mission is to develop cutting-edge machine learning methods to model and forecast the dynamics of these complex systems. This includes learning to simulate dynamics and spatiotemporal forecasting. Additionally, we investigate methods for intervening in these systems to achieve desired outcomes, using techniques such as end-to-end learning for optimization tasks and deep generative models for inverse design.
- Spatiotemporal dynamics and time series:
- Spatiotemporal optimization and inverse design:
- Autoregressive Diffusion Model for Graph Generation, ICML 2023
- End-to-end Stochastic Programming with Energy-based Model, NeurIPS 2022
- A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization, ICML 2019
Publications
(* denotes equal contribution)
2023
- Local Boosting for Weakly-Supervised Learning
Rongzhi Zhang, Yue Yu, Jiaming Shen, Xiquan Cui, Chao Zhang
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2023 - DyGen: Fine-Tuning Language Models with Noisy Labels by Dynamics-Enhanced Generative Modeling
Yuchen Zhuang, Yue Yu, Lingkai Kong, Xiang Chen, Chao Zhang
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2023 - When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2023 - Cold-start Data Selection for Better Few-shot Fine-tuning of Pretrained Language Models
Yue Yu, Rongzhi Zhang, Ran Xu, Jieyu Zhang, Jiaming Shen and Chao Zhang
Annual Meeting of the Association for Computational Linguistics (ACL), 2023 - Zero-Shot Text Classification by Training Data Creation with Progressive Dense Retrieval
Yue Yu, Yuchen Zhuang, Rongzhi Zhang, Yu Meng, Jiaming Shen and Chao Zhang
Findings of Annual Meeting of the Association for Computational Linguistics (ACL), 2023 - Graph Reasoning for Question Answering with Triplet Retrieval
Shiyang Li, Yifan Gao, Haoming Jiang, Qingyu Yin, Zheng Li, Xifeng Yan, Chao Zhang and Bing Yin
Findings of Annual Meeting of the Association for Computational Linguistics (ACL), 2023 - Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites
Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang, Tuo Zhao
Annual Meeting of the Association for Computational Linguistics (ACL), 2023 - Extracting Shopping Interest-Related Product Types from the Web
Yinghao Li, Colin Lockard, Prashant Shiralkar and Chao Zhang
Findings of Annual Meeting of the Association for Computational Linguistics (ACL), 2023 - Autoregressive Diffusion Model for Graph Generation
Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, Chao Zhang
International Conference on Machine Learning (ICML), 2023 - SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Zichong Li, Yanbo Xu, Simiao Zuo, Haoming Jiang, Chao Zhang, Tuo Zhao, Hongyuan Zha
International Conference on Machine Learning (ICML), 2023 - Unsupervised Event Chain Mining from Multiple Documents
Yizhu Jiao, Ming Zhong, Jiaming Shen, Yunyi Zhang, Chao Zhang and Jiawei Han
The Web Conference (WWW), 2023 - Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning
Ruijie Wang, Zheng Li, Jingfeng Yang, Tianyu Cao, Chao Zhang, Bing Yin, Tarek Abdelzaher
The Web Conference (WWW), 2023 - Neighborhood-regularized Self-Training for Learning with Few Labels
Ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce C. Ho, Chao Zhang and Carl Yang.
AAAI Conference on Artificial Intelligence (AAAI), 2023. - A General-Purpose Material Property Data Extraction Pipeline from Large Polymer Corpora Using Natural Language Processing
Pranav Shetty, Arunkumar Chitteth Rajan, Christopher Kuenneth, Sonkakshi Gupta, Lakshmi Prerana Panchumarti, Lauren Holm, Chao Zhang, Rampi Ramprasad
npj Comput Materials 9(52), 2023
2022
- End-to-end Stochastic Optimization with Energy-based Model
Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang
Annual Conference on Neural Information Processing Systems (NeurIPS), 2022
(Selected as Oral) - UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
Yanbo Xu, Alind Khare, Glenn Matlin, Monish Ramadoss, Rishikesan Kamaleswaran, Chao Zhang, Alexey Tumanov
Annual Conference on Neural Information Processing Systems (NeurIPS), 2022 - Shift-Robust Node Classification via Graph Clustering Co-training
Qi Zhu, Chao Zhang, Chanyoung Park, Carl Yang, Jiawei Han
NeurIPS GLFrontiers Workshop, 2022 - 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 - 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 - Precise Clinical Predictions via Counterfactual and Factual Reasoning over Hypergraphs of Electronic Health Records
Ran Xu, Yue Yu, Chao Zhang, Mohammed K Ali, Joyce Ho, Carl Yang
Machine Learning for Health (ML4H), 2022
(Outstanding Paper Award) - Deep DAG Learning on Brain Networks for fMRI Analysis
Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang
Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), 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 - 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. - ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
Yuchen Zhuang, Yinghao Li, Junyang Zhang, Yue Yu, Yingjun Mou, Xiang Chen, Le Song and Chao Zhang
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022 - COCO-DR: Combating the Distribution Shift in Zero-Shot Dense Retrieval with Contrastive and Distributional Robust Learning
Yue Yu, Chenyan Xiong, Si Sun, Chao Zhang and Arnold Overwijk
Conference on Empirical Methods in Natural Language Processing (EMNLP), 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
2021
- 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
2020
- 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 - 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 - 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 - 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 - 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 - 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.
2019
- 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
2018
- 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
2017
- 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
(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 - 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
Earlier
- 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 - 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 - 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 - 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
Awards
- 2022 ML4H Outstanding Paper Award
- 2022 NSF Career Award
- 2021 Facebook Faculty Research Award
- 2021 Kolon Faculty Fellowship
- 2020 Amazon AWS Machine Learning Research Award
- 2020 Google Faculty Research Award
- 2019 ACM SIGKDD Dissertation Award Runner-up
- 2018 ACM IMWUT Distinguished Paper Award
- 2015 ECML/PKDD Best Student Paper Runner-up Award
- 2013 Chiang Chen Overseas Graduate Fellowship
Software
- SDE-Net: Efficient uncertainty estimation for deep neural networks
- CHMM: BERT-conditional hidden Markov model for multi-source weakly-supervised learning
- COSINE: Language model fine-tuning with weak supervision
- BOND: Distantly-supervised named entity recognition
- STEAM: Automatic taxonomy expansion
- TaxoGen: Unsupervised topic taxonomy construction from text corpus
- WestClass: Weakly-supervised text classification
- GeoBurst: Unsupervised spatiotemporal event detection
Teaching
- 2023 Spring: CX4240: Introduction to Computational Data Analysis
- 2022 Spring: CX4240: Introduction to Computational Data Analysis
- 2021 Fall: CSE8803-DLT: Deep Learning for Text Data
- 2021 Spring: CX4240: Introduction to Computational Data Analysis
- 2020 Spring: CX4240: Introduction to Computational Data Analysis
- 2020 Fall: CSE8803-DLT: Deep Learning for Text Data
- 2019 Fall: CSE8803-DLT: Deep Learning for Text Data
- 2019 Spring: CX4240: Introduction to Computational Data Analysis
Students
Prospective students: I am always looking for strong and motivated students to join our group. If you are interested in working with me, you can either email me or fill out this form.
Current:
- Rui Feng: Ph.D. Student in CS
- Lingkai Kong: Ph.D. Student in CSE
- Yinghao Li: Ph.D. Student in ML
- Haorui Wang: Ph.D. Student in CSE
- Kuan Wang: Ph.D. Student in CSE
- Yue Yu: Ph.D. Student in CSE
- Rongzhi Zhang: Ph.D. Student in ML
- Yuchen Zhuang: Ph.D. Student in ML
- Binghong Chen: Ph.D. Student in CSE (co-advised with Prof. Le Song)
- Pranav Shetty: Ph.D. Student in ML (JP Morgan AI Ph.D. Fellowship, co-advised with Prof. Rampi Ramprasad)
- Vidit Jain: M.S. Student in CS
- Mukund Rungta: M.S. Student in CS
- Junyang Zhang: M.S. Student in CS
- Haotian Sun: M.S. Student in CS
Alumni:
- Yanbo Xu: Ph.D., Graduated in 2023 (First Employment: Microsoft Research)
- Piyush Patil: M.S. Student in CS
- Mengyang Liu: M.S. Student in CSE
- Isaac Rehg: M.S. in CS
- Wendi Ren: M.S. in CSE
- Ruijia Wang: M.S. in CSE
- Yi Rong: Visiting Ph.D. Student