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 a 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 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 Award in structured data management (one of only three global recipients that year), the 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), and have been consecutively listed in the Stanford University–Elsevier World’s Top 2% Scientists. 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). 

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 tointrebid@hust.edu.cn

Lab alumni include numerous PhD and Master's graduates who now hold faculty positions at prestigious universities (including institutions in the U.S. and the University of Melbourne in Australia) or work at major tech companies such as Microsoft, Google, Oracle, Amazon, Twitter, ByteDance, Tencent, Alibaba, and Huawei.

The specific research areas are as follows:

Artificial Intelligence

• Large Language Models and Applications, Agents

• Complex Reasoning for Large Language Models

• Information Retrieval, Retrieval-Augmented Generation (RAG)

• Multimodal Understanding and GenerationRecommender 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 LLMs, Text2SQL

• Graph Mining, Graph Data Management