Chu Xin (Cloris) Cheng

Chu Xin (Cloris) Cheng is a researcher working at the intersection of machine learning and science, developing AI-driven methods to accelerate scientific discovery.

Born in Hong Kong, Cheng is pursuing a bachelor’s degree at the California Institute of Technology, where she conducted research on AI for experimental design with applications in protein engineering, exoskeleton gait optimization and small-molecule discovery. A defining moment in her journey was the development of PS-BAX, a scalable Bayesian algorithm designed to efficiently identify arbitrary properties of black-box functions. This method has since been applied to antibody portfolio optimization in collaboration with Lawrence Livermore National Laboratory and was recognized as a finalist for the Undergraduate Operations Research Prize at the INFORMS Annual Meeting. In addition to PS-BAX, Cheng has published multiple research articles in leading machine learning conferences and journals. Combining theoretical rigor with practical impact, she seeks to tackle intellectually challenging problems and drive meaningful advancements in science.

Passionate about interdisciplinary research, Cheng has collaborated with teams across machine learning, chemistry and biology to develop models that bridge computation and scientific intuition. Her academic excellence and research contributions have been recognized with the Jack E. Froehlich Memorial Award and the Henry Ford II Scholar Award at Caltech.

Outside of research, Cheng enjoys ceramics, outdoor adventures and uncovering the hidden patterns that shape the world.