Nan Li (李楠)
📌 He was selected for the inaugural China Association for Science and Technology (CAST) Young Talent Support Program’s PhD Special Project. To date, he has published 20 papers in prestigious journals and conferences including ACM CSUR (IF: 28.06), NeurIPS, IJCAI, IEEE TEVC, IEEE TFS, and IEEE TCYB (including 4 ESI highly cited papers, 1 hot paper, and 3 research frontier papers). He serves as a reviewer for over 30 SCI/EI journals. Additionally, he has organized sessions and workshops on evolutionary architecture search at multiple international conferences as Session/Workshop Chair (e.g., ICME, IJCNN, and CEC).
- Lecturer
- School of Computer and Information Technology, Institute of Big Data Science and Industry, Shanxi University, No.92, Wucheng Road, Taiyuan, Shanxi Province, China
- 🤖 I am happy to mentor proactive and ambitious undergraduate students to publish high-quality academic papers as first author. 我非常乐意指导积极进取、主动投入的本科生以第一作者身份发表高质量学术论文。
- ✨Don’t be shy, have a try! Send me an email! [lnnner[at]163.com or linan10[at]sxu.edu.cn]
📢 News
- Our work about Symbolic Regression for Predictor is accepted by TEVC.
- Four papers are accepted by IJCNN-2026. Congratulations to the four outstanding undergraduates
- Our book about Performance Predictor in ENAS is published by Springer. This is the first monograph on performance predictors in the ENAS field
- Our work about Performance Predictor for GFNAS is accepted by TEVC. Congratulations to Aohan!
- Our work about Generalizable MPQ is accepted by NeurIPS-2025.
- One work about Performance Predictor for ENAS becomes ESI Highly Cited Paper.
- Our work about Rank Performance Predictor for NAS is accepted by IJCAI-2025.
- Our work about Performance Predictor for ENAS is accepted by TEVC.
- Our work about Performance Predictor for ENAS is accepted by SWEVO.
⭐ Research Interest
My research interest focus on theory and applications of Automated Machine Learning (AutoML), including:
- Neural Architecture Search
- Performance Predictor
- Feature Selection
- Ordinal Learning
- Evolutionary Computation