AI-based air quality, toxicological risk, and process safety systems
- AI-driven inverse modeling for atmospheric emission source identification and tracking
- Data-centric QSAR-based molecular-level toxicological risk assessment for emerging contaminants
- High-throughput evaluation and optimization of best available techniques (BAT)
Greenest A.I. Lab
Led by Prof. SungKu Heo
We work for digital and autonomous solutions for a climate-resilient future, and push boundaries across autonomous systems, decarbonization, and circular innovation.