INTelligent information REtrieval and BIg Data

The Intelligent Information and Big Data Laboratory (Intrebid Lab) is dedicated to research in artificial intelligence and big data, led by the National Leading Talent in the field. The lab has published over 200 papers in top international conferences and journals, including more than 120 classified as CCF A-level. It has achieved a series of innovative outcomes with international impact in the field of AI and big data, widely adopted by major IT companies such as Microsoft, Google, Amazon, Huawei, and AT&T. Members of the lab have received numerous prestigious international awards, including the 2017 Google Faculty Research Awards in the area of structured data management (one of only three global recipients that year) and Chris Wallace Award for Outstanding Research by the Computing Research and Education Association of Australasia (CORE, awarded to only one recipient annually in the region). The lab has also received best paper awards at several top international conferences, including the Best Paper Nomination at WSDM 2024, a premier conference in information retrieval, and the Best Paper Award at ACM SIGKDD 2016, a flagship conference in data mining. Recently, the lab’s advancement on PMG technology in personalized multi-modal generation have been reported by the leading tech media “Quantum Bits.” The laboratory is equipped with ample computing resources.

The lab is continuously recruiting PhD students, Master's students, PostDocs(Annual salary: 300-600K RMB), and junior faculty members with attractive salaries and scholarships! Please send your resume to intrebid@hust.edu.cn
Applicants for PhD and Master's positions should also send the undergraduate and graduate transcripts and proof of English proficiency. Outstanding students have opportunities for recommendation to internships at leading companies and exchange programs at top universities.

The specific research areas are as follows:

Artificial Intelligence

• Large Language Models and Applications, Research on Next-Generation Large Models
• Complex Reasoning for Large Language/Multimodal Models (e.g., ChatGPT-4o, DeepSeek)
• Information Retrieval, Retrieval-Augmented Generation (RAG)
• Multimodal Understanding and Generation
• Recommender Systems
• Knowledge Graphs, Knowledge Extraction, Relation Extraction, Entity Alignment

Big Data, Data Mining

• Databases, Vector Search and Querying, AI4DB, Database/Table Operations and Question Answering based on Large Language Models, Text2SQL
• Graph Mining, Graph Data Management
• Management and Mining of Spatial and Temporal Data