Doctoral student, University of Chinese Academy of Sciences, China
Qianqian Zhang is a doctoral student at the University of Chinese Academy of Sciences, specializing in computer application technology within the School of Computer Science and Technology and the National Space Science Center. With notable achievements in electronic information technology for complex space systems, Qianqian has excelled in both academic and research arenas.
Profile
ORCID
🎓 Education:
- Bachelor’s Degree: Hefei University of Technology, School of Computer Science and Information, Electronic Information Science and Technology, 2017-2021 (Graduated with the highest average score).
- Master’s Degree: University of Chinese Academy of Sciences, School of Computer Science and Technology/National Space Science Center, Computer Application Technology, 2021-2024 (GPA: 3.7/4.0).
- Ph.D. Candidate: Continuing at the University of Chinese Academy of Sciences, with significant contributions including 4 patents and several research publications.
🛠 Experience:
Qianqian Zhang has 3 years of experience in the field of computer application technology, with a focus on AI, deep learning, computer vision, and electronic design. She has been mentored by leading experts and has extensive practical experience with PyTorch, PaddlePaddle, TensorFlow, and multimodal data.
🔬 Research Interests:
Her research interests include multimodal data processing, small target detection, model lightweighting, video compression, and efficient deployment of models. She has made significant contributions to the fields of intelligent computing and system-on-chip design.
🏆 Awards:
Qianqian has received numerous national and regional awards, including top prizes in robotics control competitions. Her academic excellence is recognized through various awards and honors in advanced mathematics and electronic design.
📚 Publications:
“Real-time Recognition Algorithm of Small Target for UAV Infrared Detection” (SCI Journal, 2023) [Cited by 5 articles]
“Design of H.264 Video Compression System Based on Domestic CPU+GPU” (Conference Paper, 2023) [Cited by 2 articles]
“Multi-YOLO: Small Target Detection Algorithm Based on Visible and Infrared Multimodal Fusion” (Contributing Paper, 2024) [Cited by 3 articles]