서울대학교 생물정보연구소 세미나를 아래와 같이 열고자 하오니, 많은 참여 바랍니다.

 

 

 

Biological data is characterized by being extremely sparse, high-dimensional, and noisy. Conversely, models used in biology are complex, ad hoc, and difficulty to analyze. Here, I propose that geometric representations can help with both analysis of models and novel learning approaches. I present two example cases: (1) using algebraic geometry analysis to analyze tree graph models and (2) empirical kernel embedding for RNA and protein structure prediction. Then, I discuss new single cell RNA sequencing data and its geometric character, and algorithmic approaches for unsupervised learning of cell types.

 

Junhyong Kim, PhD is an expert in Genomics, Single Cell Biology, Bioinformatics, and Precision Medicine. His laboratory is currently focused on mammalian neurons and their RNA biology. He is one of the pioneers of the field of Single Cell Biology as well as Algebraic Statistics for biological data analysis. Dr. Kim received his PhD in Ecology and Evolution at SUNY Stony Brook in 1992. From 1994-2002 he held positions of Assistant Professor and Associate Professor at Yale University. In 2002 he moved to University of Pennsylvania where he is the Patricia M. Williams Term Endowed Professor of Biology, Adjunct Professor of Computer and Information Science, and the Co-Director of Penn Program in Single Cell Biology. He is the recipient of Sloan Foundation Young Investigator Award, Yale Junior and Senior Faculty Award, J. S. Guggenheim Fellow, and Ellison Medical Foundation Senior Scholar Award. Dr. Kim’s honors include Visiting Fellow at the Newton Institute at Cambridge, Visiting Fellow at Merton College, Oxford, and Visiting Fellow at the Institut des Hautes Etudes Scientifique.

 

서울대학교 생물정보연구소, 컴퓨터공학부 공동주최