Yimin Luo
About Yimin Luo
Yimin Luo is an Assistant Professor at Yale University, specializing in chemical engineering with a focus on colloids, complex fluids, and biomaterials. He has a diverse background in research and education, having worked at several prestigious institutions including Rice University, UC Santa Barbara, and the University of Pennsylvania.
Current Position at Yale University
Yimin Luo currently serves as an Assistant Professor at Yale University, a position he has held since 2023. His role involves teaching and conducting research in the field of Chemical Engineering. He is based in New Haven, Connecticut, where he contributes to the academic community through his expertise in colloids, complex fluids, and biomaterials.
Previous Experience at Rice University
Before joining Yale University, Yimin Luo worked at Rice University in various capacities. He served as an Undergraduate Research Assistant from 2011 to 2013 and later as a Lab Assistant from 2009 to 2010. During his time there, he gained practical experience in research and laboratory settings, which laid the foundation for his future academic pursuits.
Education and Advanced Degrees
Yimin Luo completed his Bachelor of Science (BS) in Chemical Engineering at Rice University from 2009 to 2013. He then pursued further education at the University of Pennsylvania, where he obtained both a Master of Science (M.S.) in 2014 and a Doctor of Philosophy (Ph.D.) in 2018, specializing in Chemical Engineering.
Postdoctoral Research Experience
Yimin Luo has extensive postdoctoral research experience. He worked as a Postdoctoral Researcher at the University of Delaware from 2018 to 2020 and at UC Santa Barbara from 2020 to 2022. His postdoctoral roles involved advanced research in chemical engineering, contributing to his expertise in experimental design and statistical approaches.
Research Interests and Contributions
Yimin Luo's research interests focus on colloids, complex fluids, and biomaterials, particularly in systems under shear flow, phase separation, or migration. He develops statistical and machine learning methods to automate experimental processes and optimize experimental designs. His work includes designing high-throughput experiments to gather microscopy, rheology, and scattering measurements.