HUANG Qiang 黄强
HUANG Qiang 黄强
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Professor
Professor
L0228, Information Building
School of Intelligence Science and Engineering,
Harbin Institute of Technology (Shenzhen), China
Email: huangqiang AT hit.edu.cn
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Biography
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 around 40 first- or corresponding-author papers at leading venues, 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 oral presentation at ICML, ACL, and AAAI and as a NeurIPS 2025 Spotlight. He also won the ACM ICAIF 2024 Cryptocurrency Market Risk Assessment Simulation Competition. He is an Area Chair for ICLR 2027, regularly serves on the program committees of ICML, NeurIPS, and ICLR, and reviews for TPAMI, VLDBJ, and TKDE. His service has been recognized with the ICLR 2021 Outstanding Reviewer Award and ICDE 2026 Distinguished PC Award, and he served as an ICDE 2023 Session Chair.
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
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.
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