Lin Li

PhD student @ King's College London, Associate Member @ Sea AI Lab

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Department of Informatics

King's College London

London, WC2B 4BG, UK

I am a PhD student in machine learning under the supervision of Dr Michael Spratling and Dr Dimitrios Letsios at the Department of Informatics, King’s College London. My PhD study is funded by King’s-China Scholarship Council PhD Scholarship. Prior to coming to King’s, I received a MSc degree in computing with my thesis advised by Professor Wayne Luk from Imperial College London. I also received a B.B.M. in finance with my thesis advised by Professor Zheng Qiao from Xiamen University.

My research interests include

  • Trustworthy ML: robustness, safety and interpretability
  • Data-centric ML: generative models for data augmentation
  • AI+ Applications: healthcare, finance

Collaboration: my collaborators and I are looking for new mates to join the team to develop new methods for robust large multimodal models and using generative models for data augmentation. If interested, you are more than welcome to contact me to discuss.

news

selected publications

  1. One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models
    Lin Li, Haoyan Guan, Jianing Qiu, and 1 more author
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  2. Data Augmentation Alone Can Improve Adversarial Training
    Lin Li, and Michael Spratling
    In International Conference on Learning Representations, 2023
  3. Understanding and combating robust overfitting via input loss landscape analysis and regularization
    Lin Li, and Michael Spratling
    Pattern Recognition, 2023
  4. Large AI Models in Health Informatics: Applications, Challenges, and the Future
    Jianing Qiu, Lin Li, Jiankai Sun, and 10 more authors
    IEEE Journal of Biomedical and Health Informatics (JBHI), 2023
  5. OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift
    Lin Li, Yifei Wang, Chawin Sitawarin, and 1 more author
    ICLR 2024 Workshop Data-centric Machine Learning Research (DMLR), 2024