• Professor
• Department of Artificial Intelligence
  College of Computer Science
  Sichuan University, China
• Basic Building 414
  Wangjiang Campus, Sichuan University
  No.24 South Section 1, Yihuan Road, Chengdu, China, 610065

News

• Again! We won the first place on Neural Architecture Search Challenge organized by AutoML24. [Media] (https://www.scu.edu.cn/info/1207/26284.htm)
• I gave one-hour talk as part of specialized tutorial on Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition in GECCO2024.
• Our work about optimizer automation motivated by NAS is accepted by ECCV2024.
• I gave two-hours tutorial on evolutionary neural architecture search in CEC2024.
• Our work about robust training of LLM is accepted by ICML2024.
• Three of our work about NAS are accepted by IJCAI2024.
• Our work about adversarial example generation is appected by FSE2024.
• Our work about imbalanced data learning is appected by TKDE.
• Our work about robust NAS is accepted by TNNLS.
• Two of our work about theory of ENAS are accepted by GECCO2024 and TETCI.
• Our work about robust NAS is accepted by CVPR2024.
• Yanan Sun is invited to be Associate Editor of IEEE Transactions on Neural Networks and Learning Systems.
• Our work about theory of ENAS is accepted by TEVC.
• We won the first and second place on Neural Architecture Search Challenge organized by CVPR23. [Media] [1] [2]
• Our work about Performance Predictor for NAS is accepted by TCYB.
• Yanan Sun is invited to be Associate Editor of IEEE Transactions on Evolutionary Computation from 2023.
• Our work about Performance Predictor for NAS is accepted by NeurIPS2022.
• Our work about Evolving Transformer is awarded as Best Paper by MLMI2022.
• Our work about Benchmark Platform of ENAS is accepted by TEVC.
• Our work about Constraint NAS is accepted by TNNLS.
• Our work about improving efficiency of NAS is accepted by ICCV21.

Research Interest

My research interest focus on theory and applications of automated machine learning, including:

• explainable/interpretable neural architecture search (NAS)
• convergence analysis for evolutionary computation-based NAS
• multi-/many-objective/ and constrained optimization for NAS
• low-energy consumption NAS with high-inference speed
• feature selection and construction
• auto data augmentation