Research-oriented builder at the intersection of Bioinformatics · Machine Learning · Practical AI Systems

🎓 Background in Electronic Information Engineering

I focus on turning research ideas into executable systems, especially in:

protein & peptide modeling · biological interaction prediction · graph learning · reproducible ML workflows · cross-domain engineering systems

I work on problems that require both theoretical understanding and practical implementation.

My interests sit at the intersection of:

• 🧬 Biology × Artificial Intelligence
• 📊 Structured Data × Representation Learning
• 🧪 Research Ideas × Engineering Execution
• 🔬 Scientific Modeling × Reproducible Workflows
• ⚙️ Electronics × Intelligent Systems

I enjoy breaking down complex problems and turning abstract ideas into systems that actually run.

• Protein / peptide property prediction
• Protein–protein interaction (PPI) prediction
• Cold-start biological relation inference
• Multimodal biological data fusion
• Knowledge-guided learning
• Graph representation learning
• Equivariant & diffusion-style modeling

• PyTorch-based training pipelines
• Experiment design & ablation studies
• Data preprocessing workflows
• Structured logging & result analysis
• Reproducible research pipelines

• Structured data processing (Pandas)
• Statistical reasoning & interpretation
• Scientific visualization
• Geographic / panel / DID-style modeling

• Circuit simulation: Multisim · Proteus
• PCB design: Altium Designer
• Embedded systems: Arduino · 8051 · STM32F
• FPGA / industrial tools: Quartus · GX Develop

• Integer / Nonlinear Programming
• Constraint optimization
• Global optimization algorithms
• Real-world applied optimization

• Game development with Unity · Godot

Languages Python · C · MATLAB · SQL · Markdown

AI / Data PyTorch · PyTorch Lightning · Pandas · NumPy · Scikit-learn

Engineering Multisim · Proteus · Altium Designer · Quartus

Tools VS Code · PyCharm · AutoDL · Git · Jupyter

• Knowledge-guided multimodal frameworks for cold-start PPI prediction
• Research workflows for scientific modeling
• Practical automation tools with Python
• Systems connecting biological data · modeling · interpretability

• Prefer clear frameworks over vague ideas
• Focus on usefulness, structure, and execution
• Turn messy problems into organized systems
• Value depth over surface-level knowledge
• Optimize workflows after understanding the full system
• Move smoothly between research thinking ↔ engineering implementation

Build useful things. Understand first principles. Prefer substance over appearance. Iterate continuously.

Open to collaboration, research discussion, and interesting technical projects.