Kyung In Kim
About Kyung In Kim
Kyung In Kim is a Principal Scientist at Bristol Myers Squibb with a background in biostatistics and extensive experience in computational science and statistical research.
Company
Currently, Kyung In Kim is working at Bristol Myers Squibb in the United States as a Principal Scientist. This role involves the application of vast scientific expertise in the biopharmaceutical domain, contributing to the company's research and development initiatives.
Title
Kyung In Kim holds the title of Principal Scientist, a position that denotes a high level of expertise and contribution in the scientific community. This role signifies a leadership position in research projects and the development of innovative solutions in the field.
Education and Expertise
Kyung In Kim achieved a Ph.D. in Biostatistics from Eindhoven University of Technology, completing this program from 2004 to 2008. Prior to this, Kim obtained a Master’s degree in Applied Mathematics (2001-2003) and a Bachelor's degree in Mathematics (1992-1997) from the Korea Advanced Institute of Science and Technology. This extensive educational background has established a strong foundation in both theoretical and applied aspects of mathematics and biostatistics.
Background
Before becoming a Principal Scientist, Kyung In Kim held several positions at Bristol Myers Squibb, including Senior Research Scientist II from 2019 to 2020. Kim has also worked as an Associate Computational Scientist and Assistant Computational Scientist at The Jackson Laboratory in Connecticut, and as an Assistant Professor of Statistics at Inha University in Korea. Earlier roles include a Postdoctoral Scientist at Columbia University Irving Medical Center and a Visiting Fellow at the National Cancer Institute’s Biometric Research Branch.
Achievements
Kyung In Kim has contributed significantly to the field of computational biology and biostatistics. Kim developed an automatic outlier detection system for RNA-Seq batch effect modeling and led the development of a linear contrast method for linear and generalized linear models. Additionally, Kim pioneered a treatment group selection method for multi-stage trials involving multiple PD biomarkers and has deep expertise in analyzing multiplex IHC data using hierarchical Bayesian modeling. Collaborative efforts include working with pathologists to calculate the minimum tissue area necessary for evaluating IHC biomarker data.