Machine Learning is the discipline that studies how to enable computers to simulate or achieve human learning activities. On one hand, it explores learning mechanisms, aiming to simulate human learning processes. On the other hand, it focuses on effectively utilizing information and extracting hidden, valuable, and interpretable knowledge from data. Its methods and techniques support various fields of artificial intelligence.

The main research areas include machine learning theory, supervised learning, weakly supervised learning, unsupervised learning, reinforcement learning, efficient training algorithm, and applications of machine learning. The main objective is to empower machines with the ability to autonomously model and make decisions, continuously improving their modeling and decision-making capabilities during operation.

Relevant achievements have been published in top journals and conferences such as PNAS, Proc. IEEE, IEEE TPAMI, JMLR, COLT, ICML, NeurIPS, and ICLR, with more than 100 papers. The team has also published five academic monographs and filed over 40 patent applications in China (with more than 20 granted patents). The team has received accolades including the first prize of 2020 CCF Nature Science Award (ranked first by Lin Zhouchen), the ECML-PKDD (CCF Class B) 2021 Best Paper Award, AAMAS 2016 Best Paper Nomination, the Champion of OGB Large-Scale Challenge (OGB-LSC) KDD Cup 2021, NeurIPS 2021 Catalyst Molecular Dynamics Simulation Challenge Champion, the 2021 CAAI Excellent Ph.D. Dissertation, the 2020 ACM China SIGAI Subcommission Excellent Ph.D. Dissertation, and the 2021 CCF Excellent Ph.D. Dissertation Nomination. The team consists of one with National Science Fund for Distinguished Young Scholars, one with National Science Fund for Excellent Young Scholars, and two with Science Fund for Excellent Young Scholars (Overseas).