Po-Yen Chen

Statement of Affiliation

Dr. Po-Yen Chen's research integrates machine learning (ML)-enabled predictive and generative modeling with robotics-automated experimentation and simulation to accelerate the discovery and optimization of high-performance materials and electronic devices. By leveraging ML-driven design and interpretation, his work addresses longstanding challenges in multi-component formulation and multi-property optimization, moving beyond traditional trial-and-error approaches and one-variable-at-a-time methodologies.

This AI- and robotics-integrated framework accelerates the development of functional materials with programmable properties, directly tackling global challenges such as plastic pollution, resource scarcity, and the demand for high-performance green alternatives. Applications of these AI-discovered materials span soft electronics, conductive aerogels, smart soft robotics, and emerging sustainable technologies. Furthermore, Dr. Chen's work employs interpretable models and simulation techniques to reveal complex composition–structure–functionality relationships, enabling the rational design of materials with precisely tuned characteristics.

Dr. Chen's research aligns with the MATRIX Lab's mission to advance autonomous technologies and robotic systems through innovation and interdisciplinary collaboration. By integrating machine learning (ML)-enabled predictive and generative modeling with robotics-automated experimentation and simulation tools, he aims to accelerate the discovery and deployment of high-performance materials and electronics that can enhance the capabilities of autonomous and uncrewed systems. In parallel, he is committed to contributing to the lab’s educational mission by helping cultivate a skilled, future-ready workforce prepared to address the evolving challenges of autonomy, sustainability, and intelligent system integration.