Student at University of Science and Technology Beijing , China.
Tianhong Qi is a talented and driven undergraduate student at the School of Intelligence Science and Technology, University of Science and Technology Beijing 🎓. With a passion for scientific research, Tianhong has made significant contributions to various fields, including intelligent image recognition, object detection, and data mining 🤖.
Professional Profile
scholar
🎓 Education
– *Primary School*: Graduated from Beijing Zhongguancun No.3 Primary School 📚– *Middle School*: Graduated from the Humanities Experimental Class of Beijing 101 High School 📚– *University*: Currently an undergraduate student at the School of Intelligence Science and Technology, University of Science and Technology Beijing 🎓
💼 Experience
– *Chinese Cursive Script Recognition*: Built a cursive script dataset and designed a skeleton-embedded Vision Transformer (ViT) model with progressive curriculum learning 📝– *Ground-Based Cloud Detection*: Proposed CurriCloud, a novel model integrating meteorological theory and loss-adaptive curriculum learning ☁️– *Intelligent Archival Document System*: Developed a multi-modal system for document recognition, automatic classification, and knowledge graph construction 📄– *Digital Twin for Smart Manufacturing*: First-authored paper on digital twin frameworks 🤖– *ZhiYuan Interactive Platform*: Created a multi-functional app integrating DFS-based puzzle games, real-time chat, and file transfer 📱– *AI Marathon Training System*: Combined biomechanics, meteorology, and graph computing to optimize training plans 🏃♂️
🔬 Research Interests
-Tianhong’s research interests lie in applying computer vision and data mining to scientific domains, including:
🏅 Awards
– Winner of multiple municipal and national youth and university-level science and technology innovation competitions 🏆
– Winner of the 2025 “Challenge Cup” Capital Academic Competition 🏆
– Prize winner of the 2024 “Cradle Cup” Innovation Competition (USTB)
📚Top Noted Publications
– Vision Transformer-Based Recognition of Chinese Cursive Calligraphy: A Curriculum Learning and Skeleton Embedding Approach 📄
– Loss Adaptive Curriculum Learning for Ground-Based Cloud Detection ☁️
– Digital Twin Frameworks for Smart Manufacturing 🤖
– Judicial Evidence Relationship Construction Method and Device (National patent) 📄
– Intelligent Archival System V1.0 (Software copyright) 📄
– ZhiYuan Interactive Platform (Software copyright) 📱
Conclusion
Tianhong Qi’s research experience, publication record, awards, and innovation make him a strong candidate for the Best Researcher Award. With further emphasis on interdisciplinary collaboration and expanding the scope of research, Tianhong could further solidify his position as a leading researcher in his field.