Kun Xiong
About Kun Xiong
Kun Xiong is a Postdoctoral Associate at Yale University, specializing in computational biology. He holds a PhD in Molecular and Cellular Biology from the University of Arizona, where he developed a program to simulate the evolution of genetic networks.
Work at Yale University
Kun Xiong has been serving as a Postdoctoral Associate at Yale University since 2019. In this role, he engages in advanced research within the field of computational biology. His work involves exploring new questions in genomics and bioinformatics, utilizing computational methods to analyze complex biological data. This position allows him to contribute to the academic community at Yale and collaborate with other researchers in the field.
Education and Expertise
Kun Xiong holds a Doctor of Philosophy (PhD) in Molecular and Cellular Biology from the University of Arizona, where he studied from 2013 to 2019. His doctoral research included the development of a program to simulate the evolution of genetic networks. Prior to this, he earned a Master of Science (MS) in Chemical Biology from Peking University Health Science Center from 2010 to 2013, and a Bachelor's degree in Biotechnology from Peking University from 2006 to 2010. His educational background provides a strong foundation in both wet lab and computational biology.
Background
Kun Xiong transitioned from wet lab research to a focus on computational biology, emphasizing the description of molecules and reactions through mathematical equations and simulations. This shift reflects his interest in leveraging computational techniques to address complex biological questions. His diverse educational background and research experience enable him to approach problems in genomics and bioinformatics with a unique perspective.
Research Development
During his PhD research at the University of Arizona, Kun Xiong developed a program aimed at simulating the evolution of genetic networks. This innovative work contributes to the understanding of genetic interactions and the dynamics of biological systems. His research interests continue to evolve as he explores new avenues in genomics and bioinformatics, applying computational approaches to enhance scientific knowledge in these areas.