Projects
Our projects bridge the gap between fundamental AI research and real-world applications. We develop intelligent systems that can predict, design and control complex phenomena across various domains, from molecular design to mechanical systems.
Featured
A graph neural network approach for predicting DNA origami shapes, enabling rational design of complex nanostructures. Published in Nature Materials.
Novel computational framework for solving saddle point problems in dielectric elastomers using adversarial training approaches.
On Going
Advancing VLA models to make robots smarter, more adaptive, and capable of intuitive interaction—bridging perception, language, and real-world action for the next generation of autonomous robotics.
Origami-inspired designs leverage folding geometry to create lightweight, flexible, and multifunctional mechanisms for soft robotics, deployable systems, and bio-inspired actuation.
Advancing compliant mechanism design and control to create soft manipulators that adapt to complex environments and perform delicate tasks with safety, precision, and dexterity.
Revolutionary approach integrating deep learning with physical laws to solve complex PDEs in computational science, enabling faster simulations and enhanced capabilities for inverse problems.