[학부/대학원] 통계학과 세미나(연자: Prof. Richard A. Olshen ) 안내

4월 25일에 개최되는 통계학과 세미나(연자: Prof. Richard A. Olshen) 안내를 아래와 같이 드리니, 참고 바랍니다.

<통계학과 세미나>

▪ 제목 : CLONALITY, RICHNESS, AND OTHER PARAMETERS CONCERNING V(D)J DIVERSITY

▪ 연사 : Prof. Richard A. Olshen (Stanford University)

▪ 일시 : 2017년 4월 25일(화) 1:30 PM – 3:20 PM (2 sessions)

▪ 장소 : 25동 210호

Abstract for the first talk:

My two talks will give an overview and then mathematical details of ongoing joint work with Lu Tian, Yi Liu, Andrew Fire, and Scott Boyd, and also with Jorg Goronzy. A quantitative understanding of the structures of biological populations is central to many questions in science and medicine. Recent advances in analytical chemistry have facilitated detailed description of biological macromolecules, particularly in the area of DNA sequencing. Sampling and classification of molecules from a complex population lead to a natural series of questions about the number of distinct classes of molecules, and their proportions in the population. At one extreme, the underlying population can consist of instances of a single class; at the other extreme, each individual in a population may be the unique representative of its class. The majority of biological systems operate between these two extremes.  In this talk, I will attempt to describe the overall problem and specific aims for developing statistical methods to allow making inferences on the distributions of clones. Once the methods are successfully developed, I expect to apply them to study data generated by Dr. Goronzy’s lab.

Abstract for the second talk:

Like the first of my talks, this talk is on joint work with Lu Tian, Yi Liu, Andrew Fire, and Scott Boyd, and also with Jorg Goronzy. This talk will include a brief introduction to the topic of V(D)J rearrangements of T cells and B cells of the adaptive human immune system.  There are many statistical problems that arise in understanding particular types of these cells.  This presentation will be my attempt to provide some mathematical and computational details that arise in trying to understand the data. Understanding these data are related to methodology for meta-analysis.