At Woxsen University, I teach deep learning, AI, and computational physics using interactive visualizations and first principles. My courses bridge theory with real-world applications—from nuclear reactors to medical AI.
Physics-first approach to understanding neural networks, backpropagation, and modern AI architectures.
Medical image analysis, clinical NLP, drug discovery, and ethical AI in medicine.
Numerical methods, Monte Carlo simulations, and physics-informed neural networks.
"Physics First, Code Second"
I believe the best way to teach AI is through first principles. Before students write a single line of code, they understand the physics and mathematics behind every algorithm.
My courses use interactive visualizations to make abstract concepts tangible. Students see gradients flowing, neurons activating, and models learning in real-time.
This approach produces graduates who don't just use AI—they understand it deeply and can innovate confidently.
I collaborate with universities worldwide on course design, guest lectures, and faculty training programs. Let's build the next generation of AI talent together.