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About

Zeyu Feng is a researcher whose work spans two distinct and cutting-edge domains: AI-driven materials science and safe reinforcement learning. His most notable contribution, "Inverse Design of Chiral Functional Films by a Robotic AI-Guided System" (2023), has garnered an impressive 61 citations, demonstrating significant impact within the scientific community. This work addresses the challenge of designing artificial chiral materials with precisely tunable chiroptical properties — controlling the sign, magnitude, and wavelength distribution of chiral responses — with promising applications in chiral sensing, enantioselective catalysis, and chiroptical devices. By integrating robotic automation with AI-guided inverse design, Feng's research accelerates materials discovery in ways that traditional experimental approaches cannot match. Beyond materials science, Feng has also contributed to the field of safe reinforcement learning, exploring how existing policies can be intelligently transferred to new tasks while respecting safety constraints — a critical consideration for real-world, safety-critical applications. Together, these contributions reflect a researcher with a rare interdisciplinary breadth, combining expertise in AI methodology with applied materials engineering, positioning Feng as an emerging voice at the intersection of machine learning and physical sciences.