I received my B.Sc. degree from UESTC (University of Electronic Science and Technology of China) in June 2019. In the same year, I was admitted to study for an M.Sc. degree at Nanjing University without an entrance examination. I received my M.Sc. degree from the School of Artificial Intelligence, Nanjing University, in June 2022. In the same year, I was admitted to pursue a Ph.D. degree in the School of Artificial Intelligence at Nanjing University, where I belong to the LAMDA Group led by Prof. Zhi-Hua Zhou(周志华). I am currently supervised by Prof. De-Chuan Zhan(詹德川) and Prof. Han-Jia Ye(叶翰嘉).
🔬 Research Interests
My research mainly focuses on machine learning, especially:
Class-imbalanced Learning
Class-imbalanced learning addresses the challenge of datasets with imbalanced class distributions, where one or more classes have significantly fewer instances compared to others.
Semi-supervised Learning
Semi-supervised learning utilizes labeled data to ground predictions and unlabeled data to learn the larger data distribution, achieving strong results with fractions of labeled data.
Federated Learning
Federated learning is a decentralized approach that enables training machine learning models on distributed data sources while keeping the data local and preserving privacy.
📝 Publications
💻 Conference & Journal Papers
BOFA: Orthogonal Low-Rank Bridge-Layer Fusion for CLIP-Based Class-Incremental Learning. Lan Li, Da-Wei Zhou, Tao Hu, Jia-Qi Yang, Han-jia Ye, De-Chuan Zhan. AAAI 2026 (CCF-A).
Enhancing Class-Imbalanced Learning with Pre-trained Guidance through Class-Conditional Knowledge Distillation. Lan Li, Xin-Chun Li, De-Chuan Zhan, Han-jia Ye. paper, ICML 2024 (CCF-A).
Twice Class Bias Correction for Imbalanced Semi-Supervised Learning. Lan Li, Bo-wen Tao, Lu Han, De-Chuan Zhan, Han-jia Ye. paper, AAAI 2024 (CCF-A).
Aligning Model Outputs for Class Imbalanced Non-IID Federated Learning. Lan Li, De-Chuan Zhan, Xin-Chun Li. paper, Machine Learning (CCF-B).
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning. Bowen Tao, Lan Li, Xin-Chun Li, De-Chuan Zhan. paper, ICASSP 2024 (CCF-B).
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks. Xin-Chun Li, Jin-Lin Tang, Bo Zhang, Lan Li, De-Chuan Zhan. paper, NeurIPS 2024 (CCF-A).
📚 Manuscripts
Preliminary Steps Towards Federated Sentiment Classification. Xin-Chun Li, Lan Li, De-Chuan Zhan, Yunfeng Shao, Bingshuai Li, Shaoming Song. paper.
🎖️ Selected Awards & Honors
- 2024: China Mobile Corporate Scholarship
- 2023: HUAWEI Fellowship for Outstanding Contribution
- 2023: Bank of Jiangsu Scholarship
- 2022, 2023: Excellent Graduate Student from Nanjing University
- 2019: Fuyou Scholarship
- 2019: Outstanding Graduate of Sichuan Province
- 2019: Outstanding Graduate of UESTC
- 2018: Outstanding Winner in MCM/ICM (MCM/ICM is Mathematical Contest in Modeling/Interdisciplinary Contest in Modeling)
- 2018: Wu-Liang-Ye Scholarship (Enterprise Scholarship)
- 2017: National Undergraduate Scholarship
💻 Projects
- Distributed Model Reuse Technology under Non-IID Data Distribution (Phase II) (2019-2022, HUAWEI-NJU Joint Lab)
- Distributed Model Reuse Technology under Non-IID Data Distribution (Phase I) (2019-2021, HUAWEI-NJU Joint Lab)
- Major Project of ‘New Generation Artificial Intelligence’ by STI 2030 – Key Technologies and Applications of Intelligent Physician Assistant (2020-2023)
- Jiangsu Provincial Graduate Research Innovation Program (2024-2025) [Link]
🧑🏫 Teaching Assistant
- Spring 2024: Advanced Machine Learning (for undergraduates and graduates; Prof. De-Chuan Zhan) [Link]
- Spring 2024: Intelligent System Design and Application (for graduates; Prof. De-Chuan Zhan)
- Fall 2022: Advanced Machine Learning (for undergraduates and graduates; Prof. De-Chuan Zhan) [Link]
- Fall 2020: Advanced Machine Learning (for undergraduates and graduates; Prof. De-Chuan Zhan) [Link]
📬 Correspondence
- Email: lil [at] lamda.nju.edu.cn, mrlilan [at] foxmail.com
- Office: Room A201, Shaoyifu Building, Xianlin Campus of Nanjing University
- Address: Lan Li, National Key Laboratory for Novel Software Technology, Nanjing University, Xianlin Campus Mailbox 603, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China