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Li Xiaoli

LI Xiaoli

Head of Pillar (Information Systems Technology and Design)
EMAIL
xiaoli_li
sutd.edu.sg
RESEARCH AREAS
Artificial and Augmented Intelligence
Data Science
Interactive Computing
Visual Computing

Biography

Prof Li Xiaoli joined SUTD on 15 August 2025 as Head of the Information Systems Technology and Design (ISTD) Pillar. In this role, he provides strategic leadership in academic programme development, research direction, and industry engagement across the Pillar.


Prof Li brings over 30 years of combined experience in academia and industry. Before joining SUTD, he was the Department Head of Machine Intellection at A*STAR Institute for Infocomm Research (I²R), where he led more than 100 scientists to develop cutting-edge AI technologies that delivered high-impact solutions across diverse sectors. He also served as the Technical Director of the Sectoral AI Centre of Excellence for Manufacturing (AIMfg)—a national initiative supported by the Ministry of Trade and Industry (MTI) with a funding of S$35.8 million.


He has been a trusted advisor and technical panel member for several key government agencies, including the Infocomm Media Development Authority (IMDA), Ministry of Education (MOE), Ministry of Health (MOH), and the Smart Nation and Digital Government Office (SNDGO) under the Prime Minister’s Office. In academia, he has held adjunct faculty appointments at both the National University of Singapore (NUS) and Nanyang Technological University (NTU).


Prof Li is internationally recognised in the AI community and has held leadership roles at top-tier conferences including NeurIPS, ICLR, KDD, ICDM, WWW, IJCAI, AAAI, ACL, and EMNLP—serving as Conference Chair, Area Chair, Workshop Chair, and Session Chair.


He has published over 380 research papers in leading AI and data science conferences and journals. He is currently an Associate Editor of IEEE Transactions on Artificial Intelligence. His research excellence is widely acknowledged—he has been named a Clarivate Highly Cited Researcher and is listed among the world’s top 2% scientists by Stanford University.


In addition to his academic credentials, Prof Li has deep industry experience. He has led more than 10 collaborative R&D projects with partners across verticals including aerospace, telecommunications, insurance, and aviation. He has also served as joint lab director with industry leaders such as DBS, Singtel, and KPMG. His expertise in AI, machine learning, and data mining has enabled him to develop innovative solutions addressing complex real-world challenges.
Education
  • PhD, Institute of Computing Technology, Chinese Academy of Sciences, 2001
Research interests

Prof Li’s research spans machine learning, data mining, graph learning, and time series analysis, with real-world applications in smart manufacturing, semiconductors, and the digital economy.
Achievements and recognition

Prof Li is internationally recognised for his pioneering work in time series sensor data analytics, with over 4,000 citations. As one of the first researchers to formulate the sensor feature learning problem using deep neural networks, his 2015 IJCAI paper on this topic has been cited over 1,600 times. His research on remaining useful life prediction has also been widely adopted, with over 1,100 citations. He has received two Best Paper Awards at IEEE conferences for his contributions in this domain.

He is a leading contributor to positive-unlabeled (PU) learning, having co-authored the seminal 2005 ECML paper with Prof Bing Liu that coined the term. His earlier work in ICML, IJCAI, and ICDM has collectively attracted over 3,000 citations and continues to influence research in this area.

In social and biological network mining, Prof Li’s work has earned three Best Paper Awards and led to important advances in community detection, gene function prediction, and drug discovery across social and biological networks. He has also made impactful contributions to natural language processing and text analytics, with over 20 publications in top venues such as ACL, EMNLP, and WWW. His work in this area spans sentiment analysis, relation extraction, and weakly supervised learning for text classification, addressing key challenges in understanding and mining textual data at scale.
Honours and awards
  • Fellow, IEEE (2024)
  • Fellow, Asia-Pacific Artificial Intelligence Association (AAIA), 2023
  • Clarivate Highly Cited Researcher
  • Named among World’s Top 2% Scientists (Stanford University)
Selected publications
  1. Aye Phyu Phyu Aung, Xinrun Wang, Ruiyu Wang, Hau Chan, Bo An, Xiaoli Li, J. Senthilnath”, Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models”, IEEE Transactions on Image Processing, 2025. 
  2. Xue Geng, Zhe Wang, Chunyun Chen, Qing Xu, Kaixin Xu, Chao Jin, Manas Gupta, Xulei Yang; Chen, Zhenghua Chen, Mohamed Sabry Aly, Jie Lin, Min Wu, Xiaoli Li, “From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks”, IEEE Transactions on Neural Networks and Learning Systems 2024. 
  3. Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, “Label-efficient Time Series Representation Learning: A Review”, IEEE Transactions on Artificial Intelligence (TAI) 2024. 
  4. Yucheng Wang, Yuecong Xu, Jianfei Yang, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen, “SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024.
  5. Keyu Wu, Shengkai Chen, Min Wu, Shili Xiang, Ruibing Jin, Yuecong Xu, Xiaoli Li, Zhenghua Chen, “Reinforced Reweighting for Self-supervised Partial Domain Adaptation”, IEEE Transactions on Artificial Intelligence, 2024. 
  6. Qing Xu, Keyu Wu, Min Wu, Kezhi Mao, Xiaoli Li, Zhenghua Chen, “Reinforced knowledge distillation for time series regression”, IEEE Transactions on Artificial Intelligence (TAI) 2024. 
  7. Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, Cuntai Guan, “Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.
  8. Zhenghua Chen, Min Wu, Alvin Chan, Xiaoli Li, Yew-Soon Ong, “A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges”, IEEE Computational Intelligence Magazine 2023. 
  9. Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, “Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation”, IEEE in Transactions on Neural Systems & Rehabilitation Engineering 2023.
  10. Yucheng Wang, Min Wu, Ruibing Jin, Xiaoli Li, Lihua Xie, Zhenghua Chen, “Local-Global Correlation Fusion based Graph Neural Network for Remaining Useful Life Predictionn”, IEEE Transactions on Neural Networks and Learning Systems, 2023.
  11. Mohamed Ragab Mohamed Adam, Emadeldeen Eldele, Wee Ling Tan, Foo Chuan Sheng, Chen Zhenghua, Wu Min, Kwoh Chee-Keong, Xiaoli Li, ” ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data”, ACM Transactions on Knowledge Discovery from Data (TKDD), 2023. 
  12. Jin Ruibing, Zhou Duo, Wu Min, Xiaoli Li, Chen Zhenghua, “An adaptive and dynamical neural network for machine remaining useful life prediction”, IEEE Transactions on Industrial Informatics, 2023. 
  13. Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen, Min Wu, Chee-Keong Kwoh and Xiao-Li Li, “Self-supervised Autoregressive Domain Adaptation for Time Series Data”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. 
  14. Devki Nandan Jha, Zhenghua Chen, Shudong Liu, Min Wu, Jiahan Zhang, Graham Morgan, Rajiv Ranjan, and Xiao-Li Li, “A hybrid accuracy- and energy-aware human activity recognition model in IoT environment”, IEEE Transactions on Sustainable Computing, 2022.
  15. Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee-Keong Kwoh, Jinmiao Chen, Jiawei Luo and Xiaoli Li, “Pre-training Graph Neural Networks for Link Prediction in Biomedical Networks”, Bioinformatics 2022. 
Selected conference papers
  1. Yao Xiao, Hai Ye, Linyao Chen, Hwee Tou Ng, Lidong Bing, Xiaoli Li, Roy Ka-Wei Lee, “Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization “, ACL 2025. 
  2. Jiaxuan Zhang, Emadeldeen Eldele, Fuyuan CAO, Yang Wang, Xiaoli Li, Jiye Liang, “Counterfactual Contrastive Learning with Normalizing Flows for Robust Treatment Effect Estimation “, ICML 2025. 
  3. Peiliang Gong, Mohamed Ragab, Min Wu, Zhenghua Chen, Yongyi Su, Xiaoli Li, Daoqiang Zhang, “Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation”, KDD 2025. 
  4. Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Xiaoli Li, “TSLANet: Rethinking 
  5. Transformers for Time Series Representation Learning”, ICML 2024.
  6. Qing Xu, Min Wu, Xiaoli Li, Kezhi Mao, Zhenghua Chen, “Reinforced Cross-Domain Knowledge Distillation on Time Series Data”, NeurIPS 2024.
  7. Yucheng Wang, Yuecong Xu, Zhenghua Chen, Min Wu, Xiaoli Li, “SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation”, AAAI 2023. 
  8. Wang Jing, Aixin Sun, Hao Zhang and Xiaoli Li, “MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction”, ACL 2023. 
  9. Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, Xiaoli Li, “DO-GAN: A double oracle framework for generative adversarial networks”, CVPR 2022. 
  10. Keyu Wu, Wu Min, Chen Zhenghua, Xu Yuecong and Xiao-Li Li, “Generalizing Reinforcement Learning through Fusing Self-Supervised Learning into Intrinsic Motivation”, AAAI 2022. 
  11. Keyu Wu, Min Wu, Jianfei Yang, Zhenghua Chen, Zhengguo Li, and Xiao-Li Li, “Deep Reinforcement Learning Boosted Partial Domain Adaptation”, IJCAI 2021. 
  12. Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiao-Li Li and Cuntai Guan, “Time-Series Representation Learning via Temporal and Contextual Contrasting”, IJCAI 2021.