汪鹏，东南大学计算机科学与工程学院/人工智能学院教授，博士生导师。主要研究方向为大模型、知识图谱、自然语言处理等。已在国内外重要学术期刊(SCIS等)和会议(IJCAI、AAAI、NeurIPS、SIGIR、ACL、EMNLP、COLING、ISWC、CIKM等)上发表学术论文140余篇。担任IJCAI、AAAI、NeurIPS、ICML、ICLR、ACL等重要国际会议的程序委员和审稿人百余次，同时也是IEEE TKDE、中国科学、计算机学报等数十个国内外重要期刊审稿人。研究工作获得国家自然科学基金、国家重点研发计划等资助，并在多个重大场景得到落地应用。
详情参见主页：
https://cs.seu.edu.cn/2023/1024/c23024a469544/page.htm
I am Professor and Ph.D. supervisor of School of Computer Science and Enginnering at Southeast University. My main research area is Large Language Models (LLMs) and Knowledge Graphs (KGs), focusing on exploring novel paradigms for future general AI by fusing LLMs and classical symbolism KGs. My goal is to improve the efficiency of real-world operations and enhance the interpretability of complex decisions by deeply integrating advanced LLMs and knowledge engineering technologies, combining the understanding and generating capabilities of implicit knowledge in LLMs with the explanatory capabilities of explicit knowledge in KGs, and building intelligent systems. I have published over 140 academic papers in top conferences and journals (Such as IJCAI, AAAI, NeurIPS, SIGIR, ACL, EMNLP, COLING, ISWC, CIKM) and graduated over 60 students, who have moved on to leadership positions in academia and industries.
Admissions Requirements
Prospective 2~3 PhD students each year and 5~6 Master students each year should have a strong background in computer science and a keen interest in AI.
请将个人简历和成绩单发送到pwang@seu.edu.cn。
What's new
[2024-09] One paper from KGcode Lab is accepted by NeurIPS 2024, addressing the topics that adaptively optimizing the intrinsic rank of low-rank adaptation (LoRA) matrices.
[2024-05] One paper from KGcode Lab is accepted by ACL 2024, addressing the topics that leveraging the rich real-world knowledge and image synthesis capabilities of LLMs for NER.
[2024-04] Eight papers from KGcode Lab are accepted by IJCAI 2024, addressing topics such as LLMs, Knowledge Graph, and NLP.
[2024-02] One paper from KGcode Lab is accepted by LREC-COLING 2024, addressing an instructive in-context learning method for knowledge extraction.
[2023-12] Four papers from KGcode Lab are accepted by AAAI-24, addressing topics such as LLMs, Knowledge Graph, and Continual Learning.
