Research

My research combines software systems, algorithms, and computational models for large-scale brain simulation and brain-inspired intelligence.

Core Areas

• Brain simulation: scalable infrastructure for multi-scale neural simulation and efficient execution on modern hardware.
• Brain modeling: computational models of neural circuits, synaptic plasticity, and large-scale cortical dynamics.
• Brain-inspired computing: differentiable and biologically grounded frameworks for new intelligent computing paradigms.
• Computational neuroscience: theory-driven and data-driven methods for understanding neural representation, dynamics, and learning.

Open-Source Ecosystem

I contribute to and maintain open-source tools that support scientific computing and brain dynamics programming, including BrainPy, BrainTaichi, BrainScale, and SAIUnit/BrainUnit. These projects aim to make large-scale neural modeling more flexible, reproducible, and computationally efficient.

Current Interests

• Differentiable simulation for neural systems
• Large-scale network generation and synaptic plasticity
• Cross-scale brain models linking biophysics and computation
• Brain-inspired algorithms and benchmark systems

I welcome contact from prospective collaborators and trainees interested in neural computation, scientific machine learning, and scalable brain modeling.