Craig, Jin, and Xie Earn Promotions in 2007

July,  2007

Congratulations to Bruce A. Craig, Jiashun Jin, and Jun Xie on their recent promotions. Craig was promoted to Professor, and Jin and Xie were promoted to Associate Professor. Their positions were effective July 1, 2007.

Professor Bruce Craig Dr. Craig received his Ph.D. in Statistics from University of Wisconsin-Madison in 1996. He joined the Department of Statistics in the fall of that year and has been one of the leaders in the Department?s interdisciplinary efforts. Dr. Craig?s research is focused primarily on the utilization of hierarchical models to address problems in the life sciences. Areas of research include diagnostic testing, protein structure determination, plant and human genetics, wildlife population demographics, disease progression modeling, and medical decision making. Dr. Craig is also the Director of the Statistical Consulting Service (SCS), which provides university-wide advice and assistance with the design of experiments, data analysis, and interpretation of results. In 2005, Dr. Craig was appointed Purdue University Scholar.

Professor Jiashun Jin Dr. Jin received his Ph.D in Statistics from Stanford University in June 2003, and joined the Department of Statistics in August the same year.

Dr. Jin's major research interest is in the area of large-scale inference and high dimensional data analysis, where one must estimate very large numbers of parameters or test very large numbers of hypotheses. The topics that interest him often involve exploiting sparsity in large-scale inference. Here, sparsity refers to the situation where out of a large number of cases (parameters, genes, proteins, etc.), only a small proportion of them contains the real effect. In collaboration with a few of the finest statisticians, Dr. Jin has made interesting contributions to this area by developing new statistical theory and methods. They include Higher Criticism for detecting sparse heterogeneous mixture, Cai?Jin?Low confidence lower bound for the proportion of non-null hypotheses, and a family of oracle estimators for proportions based on the Fourier transform.

Dr. Jin is equally interested in applying the theory and methods that he developed with his collaborators to application problems. He has explored several application areas including astrophysics, genomics, and computer security. One topic that he has studied carefully is the the non-Gaussian signature detection in the cosmic microwave background (CMB). In fact, he is one of the key members of the Astrostatistics group at Purdue.

Professor Jun Xie Dr. Xie received her Ph.D. from UCLA in 2000 and joined the Department of Statistics in 2001.

The main focus of Professor Xie's research is on the development and application of new statistical methods for computational biology. The progress of the human genome project and development of biotechnologies have created a rich source of data. Typically, these data are noisy and of high dimension, making estimation of signals very difficult. By employing novel statistical approaches, Professor Xie is able to escape local optimal regions of estimation and provide better global results. The ideas generalize to other statistical computing fields to overcome convergence difficulties.

Professor Xie's statistical developments and the applications on biological sequence analysis are well supported by the National Science Foundation (NSF). In addition, Professor Xie is leading a collaborative group consisting of computer scientists from Indiana University Bloomington and statisticians from Purdue University in developing computational methods for identification of gene transcriptional regulation. Professor Xie is also collaborating with many experimental groups at Purdue to help biologists construct and support their biological hypothesis.

July 2007