Honours

• Fellow, IEEE (2024)
• Fellow, Asia-Pacific Artificial Intelligence Association (AAIA), 2023
• Clarivate Highly Cited Researcher
• Named among World’s Top 2% Scientists (Stanford University)

Awards

• Paper An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG has been selected as the 2023 IEEE Engineering in Medicine and Biology Prize Paper Award of The IEEE Engineering in Medicine & Biology Society, 2023.
• Paper Multi-task Self-Supervised Adaptation for Reinforcement Learning has been selected as the BEST PAPER AWARD of The 18th IEEE Conference on Industrial Electronics and Applications (ICIEA 2022).
• Paper HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems has been selected as the BEST PAPER AWARD Runner-Up of The 13th ACM International WSDM Conference (WSDM 2020)
• Paper Adversarial Transfer Learning for Machine Remaining Useful Life Prediction has been selected as the Finalist Academic Paper Award of The IEEE International Conference on Prognostics and Health Management (ICPHM 2020)
• Paper Drug-target interaction prediction via class imbalance-aware ensemble learning has been selected as the BEST PAPER AWARD of the International Conference on Bioinformatics 2016
• Paper “Network Motif Discovery: A GPU Approach was invited to the TKDE special issue on the Best Papers of ICDE of The 31st IEEE International Conference on Data Engineering 2015
• Paper ECODE: Event-Based Community Detection from Social Networks has been selected as the Best Paper Runner-Up Award of the 16th International Conference on Database Systems for Advanced Applications (DASFAA 2011), Hong Kong
• Champions of Opportunity Activity Recognition Challenge (Conducted by EU Consortium), Task B1: Automatic Activity Data Segmentation and Task B2: Multimodal activity recognition: Gestures, 2011.
• Poster Mining for domain dependency sets from protein interactions has been selected as the Best POSTER of the 12th Annual International Conference Research in Computational Molecular Biology (RECOMB 2008), Singapore.
• Research A Probabilistic Graph-theoretic Approach to Integrate Multiple Predictions has been selected as Best Performance Award of the Protein-Protein Subnetwork Challenge, the 2nd Dialogue for Reverse Engineering Assessments and Methods (DREAM 2007), USA
• Paper Interaction Graph Mining for Protein Complexes Using Local Clique Merging has been selected as the Best PAPER of the 16th International Conference on Genome Informatics (GIW 2005), Japan

Achievements

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.