Welcome to the Data Intelligence Lab! We are a team of dedicated researchers who specialize in Data Science at HKU :)

👨‍💻✨ Our research works are open-sourced! Explore them on our Lab's GitHub Repository 🚀 

🚀 Accolades & Recognitions for Our Research:

• 🏆 KDD 2024 Top-1 Most Cited Paper at ADS Track (Rank 1st / 148 Accepted Papers)

• 🏆 KDD 2024 Top-5 Most Cited Paper at Research Track (Rank 5th / 411 Accepted Papers)

• 🏆 SIGIR 2024 Top-2 Most Cited Paper (Rank 2nd / 159 Accepted Papers)

• 🏆 WWW 2023 Most Influential Papers (Rank 2nd / 323 Accepted Papers)

• 🏆 WWW 2023 Most Influential Papers (Rank 4th / 323 Accepted Papers)

• 🏆 SIGIR 2023 Most Influential Papers (Rank 12th / 165 Accepted Papers)

• 🏆 SIGIR 2022 Most Influential Papers (Rank 2nd / 161 Accepted Papers) 

• 🏆 SIGIR 2022 Most Influential Papers (Rank 3rd / 161 Accepted Papers)

• 🏆 SIGIR 2021 Most Influential Papers (Rank 14th / 151 Accepted Papers)

• 🏆 KDD 2019 Most Influential Papers (Rank 3rd / 174 Accepted Papers)

• 🏆 WWW 2024 Top-1 Most Cited Paper (Rank 1st / 405 Accepted Papers)

• 🏆 SIGIR 2024 Top-2 Most Cited Paper (Rank 2nd / 159 Accepted Papers)

• 🏆 WSDM 2024 Top-1 Most Cited Paper (Rank 1st / 112 Accepted Papers)

• 🏆 WSDM 2023 Top-1 Most Cited Paper (Rank 1st / 123 Accepted Papers)

• 🏆 WSDM 2022 Top-3 Most Cited Paper (Rank 3rd / 159 Accepted Papers)

• 🏆 WWW 2023 Best Paper Candidate

• 🏆 WSDM 2022 Best Paper Candidate

• 🏆 WWW 2019 Best Paper Candidate 

Recent Research Interests:

• 🔥 Large Language Models

  • General Graph Foundation Models (e.g., [EMNLP'2024] OpenGraph, [Arxiv'2024] AnyGraph)

  • Large Language Models for Explainable Recommendation (e.g., [EMNLP'2024] XRec)

  • Heterogeneous Graph Language Models (e.g., [KDD'2024] HiGPT)

  • Spatio-Temporal Large Language Models (e.g., [KDD'2024] UrbanGPT)

  • Spatio-Temporal Fundation Models (e.g., [Arxiv'2024] OpenCity)

  • Large Language Models for Graph Structure Learning (e.g., [Arxiv'2024] GraphEdit)

  • General Large Language Models for Graphs (e.g., [SIGIR'2024] GraphGPT)

  • Large Language Models for Recommendation (e.g., [WWW'2024] RLMRec, [WSDM'2024] LLMRec, [Arxiv'2024] EasyRec)

• 🔥 Information Retrieval/Recommendation/Personalization

  • Contrastive Learning for Recommendation (e.g., [ICLR'2023] LightGCL, [SIGIR'2022] HCCF, [WSDM'2022] CML, [KDD'2023] AdaGCL)

  • Masked Autoencoder (e.g., [WWW'2023] AutoCF ); [SIGIR'2023] MAERec )

  • Disentangled Graph Learning  (e.g., [SIGIR'2023] DCCF, [ICDE'2023] DGNN )

  • Knowledge Distillation for Recommendation (e.g., [WWW'2023] SimRec )

  • Multi-Behavior Recommendation (e.g., [SIGIR'2021] MBGMN, [AAAI'2021] KHGT, [SIGIR'2020] MATN )

  • Sequential/Session-based Recommendation (e.g., [WWW'2023] DCRec, [KDD'2022] MBHT, [AAAI'2021] MTD )

  • Multi-Modal Recommender Systems (e.g., [WWW'2023] MMSSL )

  • Social Recommendation (e.g., [CIKM'2021] SMIN, [AAAI'2021] KCGN, [IJCAI'2023] DSL )

  • Prompt Learning for Recommender Systems (e.g., [WWW'2024] GraphPro, [WWW'2024] PromptMM)

• 🔥 Spatio-Temporal Data Mining/Urban Computing/Intelligent Transportation

  • Spatio-Temporal Pre-Training (e.g., [WWW'2023] AutoST, [ICML'2023] GraphST, [NeurIPS'2023] GPT-ST)

  • Spatio-Temporal Self-Supervised Learning (e.g., [AAAI'2023] ST-SSL, [CIKM'2023] CL4ST )

  • Spatio-Temporal Graph Neural Networks (e.g., [IJCAI'2021] ST-SHN )

  • Explainable Spatio-Temporal Learning (e.g., [CIKM'2023] STExplainer)

  • Crime Prediction (e.g., [ICDE'2022] ST-SHL )

  • Traffic Flow Prediction (e.g., [AAAI'2021] ST-GDN, [CIKM'2020] ST-CGA )

  • Human Mobility Modeling (e.g., [AAAI'2023] SRINet )

  • Anomaly Detection (e.g., [ICDE'2023] OASD )

• 🔥 Graph Mining/Graph Neural Networks/Knowledge Graph

  • Graph Transformer (e.g., [SIGIR'2023] GFormer, [KDD'2022] SHT )

  • Heterogenous Graph Neural Networks (e.g., [WSDM'2023] HGCL, [KDD'2022] MHGCN, [KDD'2019] HetGNN )

  • Knowledge Graph Learning (e.g., [SIGIR'2022] KGCL, [AAAI'2020] FSRL, [KDD'2023] KGRec )

  • Dynamic Graphs (e.g., [WWW'2021] MTSN, [CIKM'2020] DRLAN )

  • Graph Diffusion Model (e.g., [WSDM'2024] DiffKG)

  • Self-Supervised Graph Learning (e.g., [SIGIR'2024] SelfGNN)

🌟 "I am fortunate to work with such a talented group of researchers, whose dedication, expertise, and passion for their work never cease to inspire me" 📚

🌟 Current Members: 🌟

🎓 Prospective Students:

We (The Data Intelligence Lab) are actively recruiting self-motivated students who are excited about doing fun research about Data Science and AI.

(HKU is ranked #17 in QS World University Rankings 2025)

If fitting to the following cases, please feel free to drop an email at chaohuang75@gmail.com.

• ⭐️ Applicants for HKU Ph.D/MPhli Program:

• Please select my name as the potential supervisor in the application system. I will try to discuss your application case by case.

• Application detailed information and timeline can be found at HKU CS Ph.D. Admission.

• Several Self-financed Ph.D./MPhli student positions are available.

• ⭐️ Master and Undergraduate Students at HKU:

• Current master and undergraduate students at HKU are welcome to join our lab, if you are interested in our research projects.

• ⭐️ Research Assistant/Remote Interns/Visitors:

• Research assistants, remote research interns, visitors are welcome to collaborate with me on various research projects (preferably >= 6 months).

• Email:

• Use email subject as "Prospective Student: Your Name - Your Affiliation".

• Please describe your i) Education background; ii) Research experience and achievements; iii) Programming/Theoretical skills.

• Please attach your resume (including your publications, ranking/GPA, anything important) with a pdf file.