Biography

HUANG Qiang is a full professor and Ph.D. advisor at the School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen). He was selected for the National Young Talent Program and serves as a co-supervisor for doctoral students at Peng Cheng Laboratory. He received his B.Eng. and Ph.D. from Sun Yat-sen University in 2012 and 2017, respectively, under the supervision of Professor Jianlin Feng. Before joining HIT (Shenzhen), he was a senior research fellow in the School of Computing at the National University of Singapore (NUS), proudly working closely with Professor Anthony K. H. Tung. From November 2011 to May 2012, he was a research intern at the School of Computer Science, University of Birmingham, advised by Associate Professor Shan He. 

He has published over 30 first- or corresponding-author papers in leading conferences and journals, including NeurIPS, ICML, ICLR, SIGMOD, VLDB, KDD, ACL, CVPR, VLDBJ, and TKDE. His work received a VLDB 2024 Best Research Paper Award Nomination and was selected for ICML/ACL/AAAI Oral presentations and NeurIPS 2025 Spotlight. He also won first place in the ACM ICAIF 2024 Cryptocurrency Market Risk Assessment Simulation Competition. He regularly serves on the program committees of ICML, NeurIPS, and ICLR and reviews for leading journals such as TPAMI, VLDBJ, and TKDE. His professional service has been recognized with the ICLR 2021 Outstanding Reviewer Award and the ICDE 2026 Distinguished PC Award; he also served as a Session Chair at ICDE 2023.

Join Us in 2027! Our group at Harbin Institute of Technology (Shenzhen) is recruiting self-motivated master’s and Ph.D. students, as well as postdoctoral researchers; research-driven undergraduates are also warmly welcome to join us early. Please get in touch if interested! 

Research Interests

• Large Language Models (LLMs): Developing efficient, reliable, and multimodal LLMs through compact architectures, knowledge distillation, retrieval-augmented generation, efficient inference, and safety alignment. 

• Agentic AI: Building cost-efficient, interactive, and trustworthy AI agents that integrate planning, retrieval, tool use, multi-agent collaboration, and agent–inference co-optimization for long-horizon tasks.

Useful Links

• M3AIL Research Group

• AI Conference Deadlines