His current research interests include data mining and machine learning, especially

• Personalization, Recommender System, Search Engine, Online Advertising, and Information Retrieval
• Large Language Model, AGI, AutoML, Reinforcement Learning, Trustworthy AI, and Multimodal ML
• AI + X: Healthcare, Urban Computing & Smart City, Science, and Education

His research has been awarded KDD'25 Best Paper Award Runner Up, ICDM'22 and ICDM'21 Best-ranked Papers, Joint AAAI/ACM SIGAI Doctoral Dissertation Award Nomination, Global Chinese AI Rising Stars (Top 25 in Data Mining), CCF-Tencent Open Fund (twice), HW Innovation Research Program (twice), Tencent Focused Research Fund (twice), CCF-Alimama Research Fund, HW Young Scholar Funding Scheme, Kuaishou Consultation Service, HKIDS Early Career Research Grant, and MSU Dissertation Fellowship. He is the principal awardee of the HKRGC Key Projects Collaborative Research Fund (CRF) and Research Impact Fund (RIF), marking him as the ONLY assistant professor to lead both such key projects in Hong Kong. He guided the students to win the NeurIPS Competition 2024 Champion (1/1500+ global teams), KDD Cup 2024 Runner Up (2/500+ global teams), International Exhibition of Inventions Geneva 2023 Silver Medal, and iCAN 2022 Gold Medal. He is the awardee of the CityU Outstanding Research Award and Thetos Young Scholar Award (two awardees annually). He is among the Stanford World's Top 2% Scientists in 2024-2025.

He serves as the top-tier Artificial Intelligence and Data Science conference chair and organizer at WWW'25, ICICIP'25, ISNN'25, WAIM'24, NLPCC'24, and ADC'24, and the journal associate editor at Neural Networks, Nature NPJ Artificial Intelligence, ACM Transactions on the Web, and Frontiers in Big Data. He serves as the conference (senior) area chair and program committee member over 100 times. He serves as the workshop organizer at KDD'19, SIGIR'20/21/22/24, WWW'21/24, Recsys'23, CIKM'23, and a lead tutor at KDD'23/25, WWW'21/22/23, IJCAI'21/23 and WSDM'23. He also serves as the founding academic committee member of MLNLP, the largest Chinese AI community with millions of subscribers. The models and algorithms from his research have been launched in the online systems of various companies, such as Amazon, Google, Facebook, Linkedin, Criteo, Lyft, Baidu, Tencent, Ant Group, Kuaishou, and JD.com.

• I have PhD (ddl: HKPFS Dec 1, Normal PhD Dec 31) and Joint-PhD (双一流A类+国科大, ddl: Dec 5, 2024) positions every year
• Postdoc, Self-financed PhD, Part-time PhD, RA and Visiting Students positions are open year round
• Please click HERE for the admission criteria (CityU is ranked #54 in US News 2026, and #63 QS 2026, #3 in HK, #7 in Asia)

• AAAI'2026, Oral Presentation (%)
  Boosting Fine-Grained Urban Flow Inference via Lightweight Architecture and Focalized Optimization [link]
  
• AAAI'2026, Oral Presentation (%)
  Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval [link]
  
• KDD'2025, KDD'25 Best Paper Award Runner Up (ADS Track)
  Put Teacher in Student’s Shoes: Cross-Distillation for Ultra-compact Model Compression Framework [link]
  
• AAAI'2025, Oral Presentation (4.6%)
  GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [link]
  
• AAAI'2025, Oral Presentation (4.6%)
  LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation [link]
  
• AAAI'2025, Oral Presentation (4.6%)
  LLM-Powered Efficient User Simulator for Recommender System [link]
  
• AAAI'2025, Oral Presentation (4.6%)
  POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning [link]
  
• NeurIPS'2024, Spotlight (2%)
  LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation [link]
  
• NeurIPS'2024, Spotlight (2%)
  Association of Objects May Engender Stereotypes: Mitigating Association-Engendered Stereotypes in Text-to-Image Generation [link]
  
• WWW'2024, Oral Presentation (10%)
  Large Multimodal Model Compression via Efficient Pruning and Distillation [link]
  
• ICDM'2022, Best-ranked Papers Award
  AutoAssign: Automatic Shared Embedding Assignment in Streaming Recommendation [link]
  
• IJCAI'2022, Long Oral Presentation (3.75%)
  MLP4Rec: A Pure MLP Architecture for Sequential Recommendations [link]
  
• WSDM'2021, Top-4 Most Cited Paper 4 / 155 Accepted Papers
  Towards Long-term Fairness in Recommendation [link]
  
• ICDM'2021, Best-ranked Papers Award, Top-10 Most Cited Paper 10 / 208 Accepted Papers
  AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations [link]
  
• PhD Thesis, 2021 Joint AAAI/ACM SIGAI Doctoral Dissertation Award Nomination
  Adaptive and Automated Deep Recommender Systems, extended abstract at ACM SIGWEB Newsletter [link]
  
• ACM SIGWEB Newsletter, Top-2 Most Cited Paper 2 / 551 in History
  Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey [link]
  
• RecSys'2018, Top-1 Most Cited Paper 1 / 117 Accepted Papers
  Deep Reinforcement Learning for Page-wise Recommendations [link]
  
• KDD'2018, Top-20 Most Cited Paper 19 / 294 Accepted Papers
  Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning [link]
  
• CIKM'2017, Top-20 Most Cited Paper 20 / 350 Accepted Papers
  Modeling Temporal-Spatial Correlations for Crime Prediction [link]
  

• Nature npj Digital Medicine, Shanghai General Hospital:
  Enhancing Clinical Documentation with Voice Processing and Large Language Models: A Study on the LAOS System [link]
  
• KDD'2025, Alipay: deployed in 10 scenarios, serving 10 million active users daily
  Put Teacher in Student’s Shoes: Cross-Distillation for Ultra-compact Model Compression Framework [link]
  
• KDD'2025, Xiaohongshu:
  NoteLLM-2: Multimodal Large Representation Models for Recommendation [link]
  
• CIKM'2025, HW:
  Prompt Tuning as User Inherent Profile Inference Machine [link]
  
• SIGIR'2025, Kuaishou:
  Generative Auto-Bidding with Value-Guided Explorations [link]
  
• WWW'2025, HW:
  SampleLLM: Optimizing Tabular Data Synthesis in Recommendations [link]
  
• WWW'2024, Alipay: deployed in 5 scenarios, saving 75 million kWh annually
  Large Multimodal Model Compression via Efficient Pruning and Distillation [link]
  
• CIKM'2024, HW:
  HierRec: Scenario-Aware Hierarchical Modeling for Multi-scenario Recommendations [link]
  
• KDD'2024, Kuaishou:
  Modeling User Retention through Generative Flow Networks [link]
  
• AAAI'2024, HW:
  D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations [link]
  

• Deep Research: A Survey of Autonomous Research Agents [link]
• Function Calling in Large Language Models: Industrial Practices, Challenges, and Future Directions [link]
• A Survey of Personalization: From RAG to Agent [link]
• Embedding in Recommender Systems: A Survey [link]
• Joint Modeling in Recommendations: A Survey [link]
• Large Language Model Enhanced Recommender Systems: Taxonomy, Trend, Application and Future (KDD'26) [link]
• Recommender Systems in the Era of Large Language Models (LLMs) (IEEE TKDE) [link]
• Large Language Models for Generative Information Extraction: A Survey (Frontiers of Computer Science) [link]
• Automated Machine Learning for Deep Recommender Systems: A Survey (ACM TORS) [link]
• Multi-Task Deep Recommender Systems: A Survey [link]
• Multimodal Recommender Systems: A Survey (ACM CSUR) [link]
• A Comprehensive Survey on Trustworthy Recommender Systems [link]
• Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey (ACM SIGWEB Newsletter) [link]
• Data Augmentation on Graphs: A Technical Survey (ACM CSUR) [link]
• Mobile Trajectory Anomaly Detection: Taxonomy, Methodology, Challenges, and Directions (IEEE IoT) [link]
• Crime in Urban Areas: A Data Mining Perspective (ACM SIGKDD Explorations) [link]
• On the Opportunities of Green Computing: A Survey [link]

• Tutorial Proposal @ ACM The Web Conference'25 (WWW'25) [Website]
• IEEE ICICIP'25 [Website], ISNN'25 [Website], NLPCC'24 [Website], ADC'24 [Website]