Scholar
Chunming He
Google Scholar ID: m0aEH2YAAAAJ
Duke University | Tsinghua University
Citations & Impact
Citations
1,403
H-index
19
i10-index
24
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- 1. June 2025: Collecting papers about Multimodal Large Language Models in Low-Level Vision
- 2. May 2025: One paper accepted to ICML 2025 and one paper accepted to IEEE TPAMI (IF=20.80)
- 3. Feb. 2025: One paper accepted to IEEE TPAMI (IF=20.80), focusing on Diffusion Models in Low-Level Vision
- 4. Jan. 2025: One paper accepted to ICLR 2025 as a Spotlight
- 5. Dec. 2024: One survey accepted to CAAI AIR, one paper accepted to AAAI 2025
- 6. Sept. 2024: One paper accepted to NeurIPS 2024 as a Spotlight, one paper accepted to IEEE TCSVT (IF=8.3)
- 7. July 2024: Received “Pratt-Gardner Fellowship” and “John Chambers Fellowship” at Duke University, one paper accepted to ECCV 2024
- 8. Jan. 2024: One paper accepted to ICLR 2024
- 9. Sept. 2023: One paper accepted to NeurIPS 2023, one paper accepted to IEEE TNNLS (IF=14.26)
- 10. July 2023: One paper accepted to ICCV 2023
- 11. May 2023: One paper accepted to MICCAI 2023
- 12. Feb. 2023: One paper accepted to CVPR 2023
- 13. Jan. 2023: One paper accepted to IEEE TNNLS (IF=14.26)
- 14. Oct. 2022: One paper accepted to IEEE TITS (IF=9.55)
- 15. Sept. 2022: One paper accepted to MedIA (IF=13.83)
- 16. Aug. 2022: One paper accepted to IEEE TITS (IF=9.55)
Research Experience
- Currently working closely with Prof. Vahid Tarokh (NAE member), Dr. Kai Li, Prof. Yulun Zhang, and Prof. Deng-Ping Fang. Previously received a master's degree from Tsinghua University and a bachelor's degree from Nanjing University of Posts and Telecommunications.
Education
- 1. Ph.D. in Biomedical Engineering, Duke University, 2024 - Present, Advisor: Sina Farsiu
- 2. M.S. in Computer Science, Tsinghua University, 2021 - 2024, Advisor: Xiu Li
- 3. B.S., Nanjing University of Posts and Telecommunications, Advisors: Hu Zhu, Guoxia Xu
Background
- Research interests include low-level vision, concealed object segmentation, medical data analysis, and multimodal large language models. Emphasizes the use of prior knowledge to enhance the robustness and generalization capabilities of computer vision algorithms.
Miscellany
- Open to any kinds of collaboration. Please feel free to contact directly through email. Looking for research assistants, interested candidates should email directly and cc Prof. Sina Farsiu.
Co-authors