Jaewon Kim

Welcome! I am an undergraduate student in Semiconductor Engineering at Korea University and a researcher at Complex Data Analytics Lab (CDAL) under the guidance of Prof. Junhee Seok.

What excites me most about AI research is diving deep into the mathematical foundations that make these systems work. Rather than just implementing existing solutions, I love breaking down complex problems into their basic components and building them back up – it’s like solving a puzzle where each piece has its own mathematical beauty.

My research focuses on improving the efficiency of generative models and reinforcement learning while exploring Physical AI—a field that integrates principles of physics with AI systems to enhance model design, optimization, and real-world applicability. Physical AI involves applying physical laws and constraints to inform AI model development, making the models more robust and interpretable in practical scenarios.

I share insights and technical breakdowns of deep learning and AI concepts on my tech blog. If you are interested in research that blends deep learning, efficient algorithms, and the physics-inspired side of AI, let’s connect.

Research Interests

• Generative Models: Diffusion models, GANs, with emphasis on precise control and optimization
• Reinforcement Learning: Both model-based and model-free approaches, sample efficiency
• Physical AI: Integrating physical principles in AI systems for enhanced real-world problem solving