AI & Machine Learning

A natural extension of computational materials science

My interest is in scientifically grounded AI/ML where data-driven methods can complement mechanistic models and accelerate industrial R&D.

Directions

• Materials informatics
• Surrogate modeling
• Inverse problems
• Scientific data analysis
• Property prediction
• Data-driven computational workflows

Physics / experiment
       ↓
Scientific data
       ↓
Representation
       ↓
ML model
       ↓
Prediction / inference
       ↓
Physics-based validation