Chao Zhang

Chao Zhang
Research
I build LLM agents for open-ended discovery — agents that navigate vast design spaces, form and revise hypotheses, and learn from sparse, delayed, and noisy feedback. My group develops diversity-driven search and long-horizon learning methods, validated on ML research automation and scientific discovery.
Diversity-Driven Agent Search
— Quality-diversity methods, evolutionary search (A*), novelty-seeking exploration, and uncertainty-guided selection over vast design spaces.
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Long-Horizon Agent Learning
— Multi-turn RL from sparse, delayed outcomes, continual pre-training for core agent capabilities, self-rewarding without external reward models, black-box model adaptation.
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Automating ML Research
— Training environments and evaluation infrastructure for discovery agents (MLE-Dojo), multi-agent research pipelines (MLE-Smith).
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AI for Scientific Discovery
— Molecular design, materials discovery, LLM-augmented synthesis planning.
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Diversity-Driven Agent Search
Discovery spaces are combinatorially vast: molecular space contains more candidates than atoms in the observable universe; the space of possible ML algorithms, architectures, and training procedures is effectively unbounded. Random exploration is hopeless in these spaces, and naive language model sampling wastes the structured knowledge that could guide search toward promising regions.
The core challenge is not just longer reasoning traces; it is choosing which hypotheses, candidates, or experiments to try next when the space is vast and each evaluation is expensive. We combine tree search over compositional action and design spaces, quality-diversity methods that preserve breadth, and uncertainty-guided selection that prioritizes the most informative next evaluations.
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
, ICLR'24
Efficient Evolutionary Search Over Chemical Space with Large Language Models
, ICLR'25
AdaPlanner: Adaptive Planning from Feedback with Language Models
, NeurIPS'23
Language Model Uncertainty Quantification with Attention Chain
, COLM'25
Long-Horizon Agent Learning
Discovery agents must learn from feedback that is sparse, delayed, expensive, and noisy. A failed experiment is not wasted — it carries information. Even strong execution-agent recipes break down when supervision comes as sparse, delayed, domain-specific outcomes rather than correct traces or preference labels.
We study how discovery agents should update from weak real-world feedback: multi-turn RL from partial outcomes, continual pre-training to strengthen core agent capabilities, and lightweight adaptation methods that specialize models to new domains without overwriting prior capabilities.
Webagent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning
, EMNLP'25
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training
, NAACL'25
Self-Rewarding PPO: Aligning Large Language Models with Demonstrations Only
, COLM'25
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
, ICML'24
Application Domains
Automating ML Research
We deploy the full stack — search, learning, and environments — to build ML agents that automate the research cycle from hypothesis to experiment to iteration. MLE-Dojo provides interactive training environments that capture the structure of real ML research workflows. MLE-Smith scales this with automated multi-agent pipelines. These systems enable agents to tackle open-ended research tasks end-to-end.
MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
, NeurIPS'25
MLE-Smith: Scaling MLE Tasks with Automated Multi-agent Pipeline
, ICLR'26
Science: Chemistry, Materials, and Molecular Design
In chemistry and materials science, we navigate molecular and materials design spaces where each candidate requires synthesis and physical characterization. Our work spans LLM-augmented synthesis planning, evolutionary search over chemical space, autonomous materials discovery, and uncertainty-aware molecular property prediction.
LLM-Augmented Chemical Synthesis and Design Decision Programs
, ICML'25
Efficient Evolutionary Search Over Chemical Space with Large Language Models
, ICLR'25
LLMatDesign: Autonomous Materials Discovery with Large Language Models
, preprint
Contrastive Fitness Learning: Reprogramming Protein Language Models for Low-N Learning
, RECOMB'24
MUBen: Benchmarking the Uncertainty of Pre-Trained Models for Molecular Property Prediction
, TMLR'24
Awards & Recognition
Awards
2024 GaTech CoC Outstanding Junior Faculty Award
2022 NSF Career Award
2022 ML4H Outstanding Paper 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
Funding
Supported by NSF (
CAREER-2144338
IIS-2403240
ACED-2435754
IIS-2106961
IIS-2008334
),
ONR MURI
, and industry partners including Amazon, Google, Meta, Adobe, HomeDepot, and Kolon.
Group
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 PhD Students
Rui Feng
(2019–)
Kuan Wang
(2021–)
Haorui Wang
(2022–)
Haotian Sun
(2023–)
Rushi Qiang
(2024–)
Changhao Li
(2024–)
Liqin Ye
(2024–)
Jing Peng
(2025–)
Yanbin Yin
(2025–)
Alumni
Agam A. Shah
, 2026 (→ Building a startup in Fintech)
Yuchen Zhuang
, 2025 (→ Research Scientist @ Google DeepMind)
Yinghao Li
, 2025 (→ Research Scientist @ Amazon AWS)
Rongzhi Zhang
, 2025 (→ Research Scientist @ Amazon)
Yue Yu
, 2024 (→ Research Scientist @ Meta GenAI)
Lingkai Kong
, 2024 (→ Postdoc Fellow @ Harvard)
Yanbo Xu
, 2023 (→ Research Scientist @ Microsoft Research)
Binghong Chen
, 2023 (→ Quant @ Citadel)
Pranav Shetty
, 2023 (→ Research Scientist @ JP Morgan Chase)
Publications
(* denotes equal contribution)
2026
MLE-Smith: Scaling MLE Tasks with Automated Multi-agent Pipeline
Rushi Qiang, Yuchen Zhuang, Anikait Singh, Percy Liang, Chao Zhang, Sherry Yang, Bo Dai
International Conference on Learning Representations (
ICLR
), 2026
WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning
Yuchen Zhuang, Di Jin, Jiaao Chen, Wenqi Shi, Hanrui Wang, Chao Zhang
Conference of the European Chapter of the Association for Computational Linguistics (
EACL
), 2026
2025
LLM-Augmented Chemical Synthesis and Design Decision Programs
Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du, Chao Zhang
International Conference on Machine Learning (
ICML
), 2025
MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
Rushi Qiang, Yuchen Zhuang, Yinghao Li, Dingu Sagar V K, Rongzhi Zhang, ChangHao Li, Ian Shu-Hei Wong, Sherry Yang, Percy Liang, Chao Zhang, Bo Dai
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2025
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Haorui Wang, Marta Skreta, Cher Tian Ser, Wenhao Gao, Lingkai Kong, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang
International Conference on Learning Representations (
ICLR
), 2025
Ensembles of Low-Rank Expert Adapters
Yinghao Li, Vianne R. Gao, Chao Zhang, MohamadAli Torkamani
International Conference on Learning Representations (
ICLR
), 2025
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Dongxia Wu, Haorui Wang, Aaron M Ferber, Yian Ma, Carla P Gomes, Chao Zhang
International Conference on Artificial Intelligence and Statistics (
AISTATS
), 2025
Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
Liqin Ye, Agam Shah, Chao Zhang, Sudheer Chava
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (
KDD
), 2025
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training
Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
Annual Conference of the North American Chapter of the Association for Computational Linguistics (
NAACL
), 2025
Adapting LLM Agents with Universal Communication Feedback
Kuan Wang, Yadong Lu, Michael Santacroce, Yeyun Gong, Chao Zhang, Yelong Shen
Annual Conference of the North American Chapter of the Association for Computational Linguistics (
NAACL
), 2025
Self-Generated Critiques Boost Reward Modeling for Language Models
Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou
Annual Conference of the North American Chapter of the Association for Computational Linguistics (
NAACL
), 2025
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs
Nicholas E. Corrado, Julian Katz-Samuels, Adithya M Devraj, Hyokun Yun, Chao Zhang, Yi Xu, Yi Pan, Bing Yin, Trishul Chilimbi
Annual Meeting of the Association for Computational Linguistics (
ACL
), 2025
Webagent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning
Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
Conference on Empirical Methods in Natural Language Processing (
EMNLP
), 2025
DORM: Preference Data Weights Optimization for Reward Modeling in LLM Alignment
Rongzhi Zhang, Chenwei Zhang, Xinyang Zhang, Liang Qiu, Haoming Jiang, Yuchen Zhuang, Qingru Zhang, Hyokun Yun, Xian Li, Bing Yin, Tuo Zhao, Chao Zhang
Findings of Conference on Empirical Methods in Natural Language Processing (
EMNLP
-Findings), 2025
Self-Rewarding PPO: Aligning Large Language Models with Demonstrations Only
Qingru Zhang, Liang Qiu, Ilgee Hong, Zhenghao Xu, Tianyi Liu, Shiyang Li, Rongzhi Zhang, Zheng Li, Lihong Li, Bing Yin, Chao Zhang, Jianshu Chen, Haoming Jiang, Tuo Zhao
Conference on Language Modeling (
COLM
), 2025
Language Model Uncertainty Quantification with Attention Chain
Yinghao Li, Rushi Qiang, Lama Moukheiber, Chao Zhang
Conference on Language Modeling (
COLM
), 2025
2024
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
Haorui Wang, Rongzhi Zhang, Yinghao Li, Lingkai Kong, Yuchen Zhuang, Xiusi Chen, Chao Zhang
Conference on Language Modeling (
COLM
), 2024
Aligning Large Language Models with Representation Editing: A Control Perspective
Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, Chao Zhang
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2024
RankRAG: Unifying Retrieval-Augmented Generation and Context Ranking in LLMs
Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, Bryan Catanzaro
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2024
HYDRA: Model Factorization Framework for Black-Box LLM Personalization
Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, Bo Dai
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2024
Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis
Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B. Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, B. Aditya Prakash
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2024
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor Bursztyn, Ryan A. Rossi, Somdeb Sarkhel, Chao Zhang
International Conference on Learning Representations (
ICLR
), 2024
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai
International Conference on Machine Learning (
ICML
), 2024
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang, B. Aditya Prakash
International Conference on Machine Learning (
ICML
), 2024
Knowledge Distillation with Perturbed Loss: From a Vanilla Teacher to a Proxy Teacher
Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Jialu Liu, Michael Bendersky, Marc Najork, Chao Zhang
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (
KDD
), 2024
Two Birds with One Stone: Enhancing Calibration and Interpretability with Graph Functional Neural Process
Lingkai Kong*, Haotian Sun*, Yuchen Zhuang, Haorui Wang and Chao Zhang
International Conference on Artificial Intelligence and Statistics (
AISTATS
), 2024
Towards Modeling Uncertainties of Self-explaining Neural Networks
Wei Qian, Chenxu Zhao, Yangyi Li, Fenglong Ma, Chao Zhang, Mengdi Huai
AAAI Conference on Artificial Intelligence (
AAAI
), 2024
Contrastive Fitness Learning: Reprogramming Protein Language Models for Low-N Learning of Protein Fitness Landscape
Junming Zhao, Chao Zhang, and Yunan Luo
Research in Computational Molecular Biology (
RECOMB
), 2024
MUBen: Benchmarking the Uncertainty of Pre-Trained Models for Molecular Property Prediction
Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang
Transactions on Machine Learning Research (
TMLR
), 2024
ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling
LingXi Zhang, Yue Yu, Kuan Wang, Chao Zhang
Annual Meeting of the Association for Computational Linguistics (
ACL
), 2024
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning
Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
Annual Meeting of the Association for Computational Linguistics (
ACL
), 2024
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs
Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang
Annual Meeting of the Association for Computational Linguistics (
ACL
-Findings), 2024
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models
Yuzhao Heng, Chunyuan Deng, Yitong Li, Yue Yu, Yinghao Li, Rongzhi Zhang, Chao Zhang
Annual Meeting of the Association for Computational Linguistics (
ACL
-Findings), 2024
Data Diversity Matters for Robust Instruction Tuning
Alexander Bukharin, Shiyang Li, Zhengyang Wang, Jingfeng Yang, Bing Yin, Xian Li, Chao Zhang, Tuo Zhao, Haoming Jiang
Findings of Conference on Empirical Methods in Natural Language Processing (
EMNLP
-Findings), 2024
A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction
Yinghao Li, Rampi Ramprasad, Chao Zhang
Findings of Conference on Empirical Methods in Natural Language Processing (
EMNLP
-Findings), 2024
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce C. Ho, Chao Zhang, Carl Yang
Conference on Empirical Methods in Natural Language Processing (
EMNLP
), 2024
Accelerating Materials Discovery for Polymer Solar Cells: Data-driven Insights Enabled by Natural Language Processing
Pranav Shetty, Aishat Adeboye, Sonakshi Gupta, Chao Zhang, Rampi Ramprasad
Chemistry of Materials
36 (16), 2024
POLYIE: A Dataset of Information Extraction from Polymer Material Scientific Literature
Jerry Junyang Cheung, Yuchen Zhuang, Yinghao Li, Pranav Shetty, Wantian Zhao, Sanjeev Grampurohit, Rampi Ramprasad, Chao Zhang
Annual Conference of the North American Chapter of the Association for Computational Linguistics (
NAACL
), 2024
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
Yinghao Li, Haorui Wang, Chao Zhang
Annual Conference of the North American Chapter of the Association for Computational Linguistics (
NAACL
), 2024
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification
Vidit Jain, Mukund Rungta, Yuchen Zhuang, Yue Yu, Zeyu Wang, Mu Gao, Jeffrey Skolnick, Chao Zhang
Conference of the European Chapter of the Association for Computational Linguistics (
EACL
), 2024
2023
AdaPlanner: Adaptive Planning from Feedback with Language Models
Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2023
ToolQA: A Dataset for LLM Question Answering with External Tools
Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, Chao Zhang
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2023
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2023
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
Rui Feng, Qi Zhu, Huan Tran, Binghong Chen, Aubrey Toland, Rampi Ramprasad, Chao Zhang
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2023
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao
Annual Conference on Neural Information Processing Systems (
NeurIPS
), 2023
Uncertainty Quantification in Deep Learning
Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B. Aditya Prakash, 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
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
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
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
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
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
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
Improving Consistency for Text Summarization with Energy Functions
Qi Zeng, Qingyu Yin, Zheng Li, Yifan Gao, Sreyashi Nag, Zhengyang Wang, Bing Yin, Heng Ji, Chao Zhang
Findings of Conference on Empirical Methods in Natural Language Processing (
EMNLP
-Findings), 2023
Knowledge-Selective Pretraining for Attribute Value Extraction
Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Yang Wang, Xiaodan Zhu
Findings of Conference on Empirical Methods in Natural Language Processing (
EMNLP
-Findings), 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
TransEHR: Self-Supervised Transformer for Clinical Time Series Data
Yanbo Xu, Shangqing Xu, Manav Ramprasad, Alexey Tumanov, Chao Zhang
Machine Learning for Health (
ML4H
), 2023
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
Yuchen Zhuang, Xin Shen, Yan Zhao, Chaosheng Dong, Ming Wang, Jin Li and Chao Zhang
ACM International Conference on Information and Knowledge Management (
CIKM
), 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
Accelerated Scheme to Predict Ring-Opening Polymerization Enthalpy: Simulation-Experimental Data Fusion and Multitask Machine Learning
Aubrey Toland, Huan Tran, Lihua Chen, Yinghao Li, Chao Zhang, Will Gutekunst, and Rampi Ramprasad
Journal of Physical Chemistry A, 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, 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, Zheng 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
Earlier
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
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
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
Teaching
CX 4240: Introduction to Computational Data Analysis
CSE 8803 DLT: Deep Learning for Text Data
Contact
Office: CODA E1358B
Address: 756 W Peachtree St NW, Atlanta, GA 30308
Email:
chaozhang@gatech.edu