Who I Am
Hi! I’m Qin Xuqiang, an AI researcher and mathematician passionate about exploring the fascinating intersection of artificial intelligence, machine learning, and mathematical theory. My work focuses on understanding how mathematical structures can inform and improve machine learning systems, and how AI can advance our understanding of complex mathematical problems.
With a background spanning both pure mathematics and applied AI research, I bring a unique perspective to problems in modern artificial intelligence. I’m particularly enthusiastic about:
- 🧠 Deep Learning & Neural Networks - Developing and analyzing architectures that push the boundaries of what AI can achieve
- 💬 Natural Language Processing - Creating systems that truly understand and generate human language
- 📐 Mathematical Foundations of AI - Exploring the theoretical underpinnings that make intelligent systems work
- 🤝 Collaborative Research - Working with others to solve challenging problems in AI and mathematics
My Journey
My path into AI research has been driven by a deep curiosity about how intelligent systems work and a love for mathematical elegance. I’ve had the privilege of working on problems that span from pure mathematical theory to practical machine learning applications.
Research Experience
Research Interests
My research sits at the exciting intersection of several fields:
Artificial Intelligence & Machine Learning
I’m fascinated by how we can build systems that learn from data and make intelligent decisions. My work explores:
- Novel neural network architectures
- Optimization methods for deep learning
- Transfer learning and model adaptation
- Interpretability and explainability of AI systems
Natural Language Processing
Language is one of the most complex and beautiful aspects of human intelligence. I work on:
- Large language models and their applications
- Text generation and understanding
- Multilingual NLP systems
- Efficient training methods for language models
Mathematical Foundations
My mathematical background informs my approach to AI research. I’m particularly interested in:
- Algebraic geometry and its applications to machine learning
- Representation theory and neural networks
- Optimization theory
- Statistical learning theory
Technical Skills
Programming & Tools:
- Python (PyTorch, TensorFlow, Hugging Face Transformers)
- Machine Learning & Deep Learning frameworks
- Scientific computing (NumPy, SciPy, Pandas)
- Data visualization and analysis
Research & Mathematics:
- Mathematical modeling and analysis
- Statistical methods
- Academic writing and publication
- Peer review and collaboration
Development:
- Git version control
- Jupyter notebooks
- Linux/Unix systems
- Cloud computing platforms
Publications & Projects
I’m committed to open research and sharing knowledge with the community. You can find my publications on Google Scholar and my code on GitHub. I also share models and datasets on Hugging Face.
Check out my Publications page for a complete list of my research papers, and my Projects page to see what I’m currently building.
Philosophy
I believe that the best research happens at the intersection of deep theoretical understanding and practical application. Mathematics provides the rigorous foundation needed to truly understand AI systems, while practical implementation grounds theoretical ideas in reality and reveals new questions to explore.
I’m also passionate about:
- Open Science - Making research accessible to everyone
- Collaboration - Great ideas emerge from diverse perspectives
- Teaching - Sharing knowledge and helping others grow
- Continuous Learning - Always exploring new ideas and techniques
Let’s Connect
I’m always eager to discuss new ideas, potential collaborations, or interesting problems in AI and mathematics. Whether you’re a researcher, student, or just someone curious about these fields, I’d love to hear from you!
📧 Email: russellqin@gmail.com
🐙 GitHub: github.com/qinxuqiang
🤗 Hugging Face: qinxuqiang1990
🎓 Google Scholar: My Profile
Last updated: November 2025