Advancing AI Technologies for Systems Informatics and Data Analytics
We focus on artificial intelligent technologies for system informatics and data analytics, with the objective to develop systematic analytical methodologies for effective inference and performance improvement for complex data. The methodologies comprise both theoretical and applied aspects of statistics and machine learning, including (1) effective information extraction, online change detection and adaptive sampling for high dimensional structured data (2) system control, intelligent overall decision-making, and data fusion techniques with domain knowledge integration for industrial applications such as semiconductor manufacturing, aircraft manufacturing and transportation systems.
Advancing superintelligence through cutting-edge AI, machine learning, and deep learning innovations
Leveraging advanced machine learning for effective information extraction, online change detection, and adaptive sampling in high-dimensional structured data. Our AI models handle complex patterns in real-time streaming data with unprecedented accuracy.
Developing next-generation deep learning architectures for time series forecasting, multimodal learning, and large language models. Our research spans functional data analysis, tensor decomposition, and specialized domain foundation models.
AI-driven decision-making frameworks integrating game theory, optimization algorithms, and data-driven control for complex networked systems. Our methods combine domain knowledge with reinforcement learning.
Transforming industries through AI—from semiconductor manufacturing quality prediction to aircraft production optimization, urban transportation intelligence, and supply chain resilience using state-of-the-art machine learning.
Statistical modeling meets AI—developing robust monitoring frameworks for complex systems through Bayesian networks, Gaussian processes, tensor learning, and graph neural networks for anomaly detection and predictive maintenance.
Pioneering graph relational learning and network analysis using GNNs for spatiotemporal prediction, community detection in functional data networks, and intelligent modeling of metro passenger flows and traffic systems.
Latest announcements and updates from our research lab
Our paper “Adaptive Change Detection in Partially Observable Dynamic Networks” has been accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).
Our paper “Wavelet-based Disentangled Adaptive Normalization for Non-stationary Time Series Forecasting” has been accepted by the AAAI Workshop on AI for Time Series.
Our paper “Function-on-Function Bayesian Optimization” has been accepted by the AAAI Conference on Artificial Intelligence (Main Track).
Chen Zhang received the INFORMS Quality, Statistics and Reliability (QSR) Best Teaching Award.
Our paper “FAME: Adaptive Functional Attention with Expert Routing for Function-on-Function Regression” has been accepted by NeurIPS.
Our paper “TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents” has been accepted by NeurIPS.
Highlights from our latest research contributions
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Join our mission to advance superintelligence through cutting-edge AI research and industrial applications.
Building the future of superintelligence through cutting-edge AI research and innovation
At the intersection of theoretical innovation and transformative industrial applications
We pioneer advanced deep learning architectures including Graph Neural Networks (GNNs), Transformers, and Recurrent Attention Models for complex data analysis. Our innovations include spatiotemporal graph networks for cellular KPI prediction, multi-task learning frameworks for medical imaging, and deep reinforcement learning for bilevel optimization in product design.
Bridging classical statistics with modern AI, we develop intelligent statistical models for real-time monitoring, anomaly detection, and predictive maintenance. Our methods include Bayesian networks for causal inference, Gaussian processes for robust parameter design, and Thompson sampling for partially observable online change detection.
We tackle the curse of dimensionality through innovative tensor methods and functional data analysis. Our research includes tensor decomposition for weakly-dependent data on graphs, low-rank robust subspace tensor clustering for metro flow modeling, and latent tensor Gaussian processes for spatial monitoring.
Advancing graph relational learning and network science through AI. We develop graph regularized tensor latent Dirichlet allocation for individualized passenger travel patterns, functional data edged networks for metro station clustering, and multi-view clustering based on functional data.
Creating AI-driven decision frameworks that integrate game theory, optimization, and reinforcement learning. Our research spans deep reinforcement learning for bilevel optimization, data-driven control of complex networked systems, and intelligent decision-making with domain knowledge integration.
Transforming industries through practical AI deployments. Our applications span semiconductor manufacturing, aircraft manufacturing, urban transportation, supply chain management, and financial systems. We deploy AI solutions at scale with industry leaders including Huawei, Tencent, Alibaba, Baidu, ByteDance, and Meituan.
Our research has resulted in 70+ publications in premier venues including IEEE Transactions (Medical Imaging, Knowledge and Data Engineering, Automation Science and Engineering, Industrial Informatics), IISE Transactions, Technometrics, Journal of Quality Technology, and top-tier AI conferences including KDD, IJCAI, AAAI, and ECML-PKDD.
We collaborate extensively with industry leaders, deploying AI solutions that impact millions of users daily in transportation, manufacturing, e-commerce, and telecommunications sectors.
Latest research contributions from our lab
Our research contributions in AI, machine learning, and intelligent systems
Peer-reviewed articles in top-tier journals and conferences
Research metrics and achievements
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Recognizing excellence in research, teaching, and scholarly publications
Society, national, international, teaching, and publication-related recognitions
Meet the researchers advancing AI and data analytics
Department of Industrial Engineering, Tsinghua University
Yujie Ma is a Postdoctoral Fellow in the Department of Industrial Engineering at Tsinghua University, China. She received her Ph.D. in Management Science and Engineering from Tianjin University in January 2022. Her research interests include supply chain resilience, bi-level programming, and deep reinforcement learning.
Research GraghPostdoctoral research in advanced machine learning methodologies and their applications to complex systems.
Yifei Gao is a Postdoctoral Fellow in the Department of Industrial Engineering, Tsinghua University, China. He received the Doctor of Engineering degree in Management Science and Engineering at Beijing University of Posts and Telecommunications in June 2024. His research focuses on deep learning-based time series and functional data analysis.
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Manrui Jiang is a Postdoctoral Fellow in the Department of Industrial Engineering, Tsinghua University, China. She received the doctor degree in School of Management and Engineerin at Capital University of Economics and Business in June 2024. Her research focuses on deep learning, complex networks, and financial risk management.
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Haijie Xu is currently a PhD student in the Department of Industrial Engineering of Tsinghua University. His research focuses on change point detection, functionl data analysis, tensor data analysis and causal inference.
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Tian Lan is a PhD student in the department of Industrial Engineering. His research focuses on multimodal LLM and time-series anomaly detection.
Xuming An is currently a Ph.D Student in the Department of Industrial Engineering, Tsinghua University. He received the Master degree in Control Science and Engineering from Wuhan University in 2020. His current research interests include optimization algorithms, game theory and data-driven control of complex networked systems.
Research GraghResearch in advanced machine learning and statistical methodologies.
Hao Duong Le is a PhD candidate in the Industrial Engineering Department at Tsinghua University, focusing his research on deep learning foundation models, large language models (LLMs), and time series foundational models. He holds a Bachelor of Arts in Applied Foreign Languages from the Taiwan University of Science and Technology and a Master of Science in Engineering from Tsinghua University.
Supporting research in data analytics and machine learning applications.
Jia Cao received her Master’s degree from the Department of Computer Science, Faculty of Engineering, The University of Hong Kong, and her Bachelor’s degree from the School of Software, Dalian University of Technology. She is currently a research assistant at the Department of Industrial Engineering, Tsinghua University. Her research interests include video anomaly detection, medical image analysis, and medical large language models.
Yingyuan Yang is currently a Research Assistant in the Department of Industrial Engineering, Tsinghua University. He received a Master's degree in Signal and Information Processing from the Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences in 2025. His current research interests include time-series anomaly detection based on multimodal large language models (MLLMs) and interpretable analysis of time-series MLLMs.
Peiyao Liu received her Ph.D. in Industrial Engineering in 2025 (advised by Dr. Chen Zhang) and her B.Eng. in Precision Instrument in 2020, both from Tsinghua University. Her research focuses on functional data analysis, tensor decomposition, Gaussian processes, state space models, and Bayesian networks, with applications in advanced manufacturing and medical testing. Dr. Liu has published in leading international journals in Quality, Statistics, and Reliability, including IISE Transactions, Journal of Quality Technology, and IEEE Transactions on Knowledge and Data Engineering. Her work has been recognized with multiple prestigious awards, including the ICQSR, INFORMS-DMDA, INFORMS-QSR, IISE-QCRE, and QRSE. Currently, she is a Research Fellow in the Department of Industrial Systems Engineering and Management at the National University of Singapore, working under the guidance of Dr. Nan Chen.
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Junpeng Lin is currently an Algorithm Engineer at Huawei Technologies Co., Ltd., focusing on communication network operations and maintenance. He received his Master of Engineering degree in Management Science and Engineering from Tsinghua University in 2025. Prior to that, he obtained his Bachelor of Engineering degree in Industrial Engineering from Tsinghua University in 2022. During his graduate studies, his research interests included spatiotemporal data modeling and deep learning.
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Bingjie He was a graduate student in Industrial Engineering, Tsinghua University. She received her Bachelor’s degree in Tsinghua University in 2020. Her research interest was transportation application, including urban flow prediction and anomaly detection. She is now pursuing her PhD degree in UC Berkeley, the US.
Congyu Han was a Research Assistant in the Department of Industrial Engineering, Tsinghua University, China. She has graduated from the Master of Information Systems Management program (Business Intelligence & Data Analytics pathway) at Carnegie Mellon University in December 2020. Her research focuses on adaptive sampling in the context of deep learning framwork. She is now pursuing her PhD degree in National University of Singapore.
Xin Xia has graduated from the M.S. program of Electrical and Computer Engineering in Georgia Institute of Technology, the US, in 2022 and received his bachelor degree of Electrical Engineering and Automation from Tianjin University, China, in 2020. He was a Research Assistant in the Department of Industrial Engineering, Tsinghua University. His research interest focuses on deep reinforcement learning and multi-modal data fusion and feature extraction based on pretrained language model. He is now pursuing his PhD degree in University of Wisconsin–Madison.
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Xing Yang is currently an associate researcher in Shenzhen University. She received her Ph.D. degree in the Department of Industrial Engineering, Tsinghua University, and her B.S. degree in industrial engineering from Huazhong University of Science and Technology, Wuhan, Hubei Province, China, in 2016. Her research interests include Bayesian network, discrete event prediction, ontology modeling.
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Hao Qiu was once a Research Assistant in the Department of Industrial Engineering at Tsinghua University. He holds a Master of Statistics from Rice University and a B.S. in Actuarial Science and B.S. in Economics from University of Delaware. His research focuses on Time Series/ Spatial-Temporal data, Statistical Learning, and Stochastic Process Modeling and Estimation.
Jie Guo received her Ph.D. degree and bachelor degree in Industrial Engineering from Tsinghua University. And she is now an associate professor in Nanjing University of Aeronautics and Astronautics. Her interest research area is adaptive sampling, high dimensional data monitoring and reinforcement learning.
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Wanshan Li is currently a lecturer in Jinan University. She received her Ph.D. degree in the Department of Industrial Engineering, Tsinghua University, China, and her BSc in Control Science and Engineering from Shandong University, China. Her research focuses on maintenance strategies optimization of multiple‐component systems with complex networked structure.
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Peng Zhou is currently a associate professor at the School of Mechanical Engineering, Guizhou University, Guiyang, China. His current research interests include in Statistical modeling and monitoring for complex systems, Intelligent maintenance and health management.
Collaborating with leading technology companies and research institutions
We partner with industry leaders to tackle real-world challenges through innovative solutions
We collaborate with renowned research institutions to address advanced scientific issues
Building bridges between research and industry
We collaborate on large-scale data science projects addressing real-world industrial challenges in manufacturing, logistics, and technology sectors.
Our research methodologies are designed for practical implementation, with ongoing technology transfer to industry partners for real-world deployment.
We work closely with partners to co-develop innovative solutions that bridge academic research and industrial applications through collaborative R&D programs.
Get in touch with our research group
Join us in advancing AI and data analytics research
Room 602, Shunde Building
Tsinghua University
Beijing, 100084, China
+86-10-62796135
We're actively recruiting at all levels
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Explore our research implementations and contributions
Discover the code behind our cutting-edge research in artificial intelligence, machine learning, and industrial applications. All our projects are open source and available for the research community.
Browse through our open source projects, from research implementations to practical tools.
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