Chongjie Si ☕️
Chongjie Si

Ph.D Candidate

About Me

Chongjie Si is a Ph.D Candidate at the Artificial Intelligence Institute, Shanghai Jiao Tong University (SJTU), advised by Prof. Wei Shen. His research interests are as diverse as his personal hobbies. If you are interested in collaboration, feel free to get in touch.

Interests
  • AI For Math
  • Parameter Efficient Fine-tuning
  • Weakly Supervised Learning
Education
  • Ph.D Artificial Intelligence

    Shanghai Jiao Tong University

  • BSc Chien-Shiung Wu College

    Southeast University

📚 My Research

I have experience in the fields of machine learning, computer vision, and natural language processing, and have worked on projects in all of these areas.

I’m now interested in AI for math and LLM related areas.

Please reach out for collaboration! 😃

💥 Recent News

🎁 The book ''Hands on CV'' which we spent three years writing is finally on the market.

Please refer to this page for more details

🎯 We conduct extensive observations and analyses of LFM’s weights and arrive at a series of striking conclusions.

Please refer to this page for more details

📘 Two papers have been accepted to ICLR 2025.

🎁 Our work Subspace Tuning has been reported by MIT Technology Review.

Please refer to this page for more details

🎁 I have won the 2024 Doctoral National Scholarship.

🎯 We propose LoRA-Dash, which unleashes the power of task-specific directions in parameter efficient fine-tuning.

Please refer to this page for more details

🎁 Our work Subspace Tuning and FLoRA has been invited for presentation at AiDD 2024, Beijing Station.

Please refer to this page for more details.

🎯 We propose Subspace Tuning for PEFT.

Please refer to this page for more details.

📘 One paper has been accepted to ECCV 2024.

🎯 We propose FLoRA, aiming to preserve the topological structure of N-dimensional parameter space while seeking low-rank representations.

Please refer to this page for more details.

📘 One paper has been accepted to AAAI 2024 as an Oral Presentation.

📘 One paper has been accepted to TKDE 2023.

📘 One paper has been accepted to KDD 2023 as an Oral Presentation.