Research Areas

Scientific Machine Learning

Our group is dedicated to the development of advanced scientific machine learning algorithms tailored for predicting material behaviors and properties, catering to a wide range of engineering applications.

Enhancing Reliability for Semiconductors

We utilize cutting-edge computational simulations and machine learning techniques to model failure mechanisms in semiconductors, aiming to significantly improve the reliability predictions and performance of microelectronic devices.

Designing Materials for Sustainable Energy

Our research employs advanced computational techniques to create cutting-edge materials and structures that address the needs of sustainable energy solutions, including advancements in nuclear energy and Li-ion battery technologies.

Innovating Material Interfaces

We explore the impact of various interfaces on material properties to uncover fundamental mechanisms, empowering us to design advanced materials through strategic interfacial engineering.