Dr. Tom Brandenburger served 20 years as a commissioned officer in the US Navy including both active duty and reserve positions. He served as department head of the Officer Math and Physics department at the Navy Nuclear Power School, Communications Officer for Naval Coastal Warfare Squadron 31, Operations Officer for Mobile Inshore and Undersea Warfare Unit 103 and Commanding Officer of Naval Security Force Sasebo Japan. Additionally he worked as an IT consultant for Perot Systems and systems administrator for Dakota State University. He has worked collaboratively with several departments on campus, and also maintains an active collaboration with the regional financial and insurance industry that has resulted in substantial, ongoing private sector support for his work.
Dr. Gemechis Djira is a biostatistician whose main research interests are simultaneous inferences, longitudinal data analysis, bioassay problems and computer intensive techniques. He teaches graduate level statistics courses including Statistical Inference, Regression Analysis, Biostatistics and Design of Experiments. He also successfully advised several graduate students to completion. Dr. Djira enjoys statistical consulting and serves SDSU extensively in this capacity.
Dr. Xijin Ge is an associate professor of bioinformatics at Department of Mathematics and Statistics. He teaches and conducts research in the general area of bioinformatics and computational biology. Dr. Ge’s research focuses on comparative and functional genomics through a combination of experimental and computational approaches. Dr. Ge’s team applies data mining techniques to large number of gene expression datasets to discover shared molecular mechanisms behind diverse perturbation and biological processes. Another emphasis concerns development of tools and resources for the interpretation of high‐throughput genomics data. In addition, Dr. Ge also enjoys working with a large network of collaborators in a supporting role to study various topics spanning from plant genomics to cancer.
Dr. Gary Hatfield worked for over 27 years as an applied statistician in various manufacturing and chemical processing facilities, including several years as Chief Statistician for ConocoPhillips. His area of expertise includes design of experiments, empirical model building and statistical process control. Since obtaining his Ph.D. in Statistics, he is an Associate Professor in the Mathematics and Statistics Department at SDSU with research interests in spatial statistics, simulation methods and the analysis of large data sets. Dr. Hatfield is a member of the graduate faculty and advises graduate and undergraduate students. He teaches courses in inference, spatial statistics and statistical computing and simulation.
Dr. Cedric Neumann obtained his Ph.D. in 2008 by developing, on behalf of the United States Secret Service, a statistical algorithm for searching chemical analytical data in large databases. During that time, Dr. Neumann was also the Research Manager of a team of statisticians working for the government of the United Kingdom. Dr. Neumann’s team was tasked with conducting statistical analysis of forensic evidence with the goal of providing faster, better and cheaper forensic leads to police investigators and to courts. Dr. Neumann and his colleagues have obtained numerous grants from both public and private sectors to support their research projects. Recently, they obtained a large award for a 3‐years project aimed at measuring the accuracy of statistical models used in forensic science.
Dr. Christopher Saunders has been the lead statistician on a number of applied research programs in bioinformatics, ranging from developing algorithms to support the quantification of the horse genome to identifying biomarkers for lung cancer cells, and has provided statistical support to the Getchell Lab at the Sanders‐Brown Center on Aging. Since completing his PhD, Dr. Saunders has focused on providing statistical support to the FBI and the intelligence community as an Intelligence Community Research Fellow, Assistant and Associate Research Professor at George Mason University and as an ORISE visiting scientist to the FBI labs. During this time he led a group focusing on the interpretation and presentation of forensic evidence related to handwriting identification. He also supported efforts in pattern recognition related to the quantification of various impression and pattern evidence forms. Since 2010 he has served as a Lead Signal Processing Engineer for the FFRDC MITRE. In this role he has provided statistical support to projects ranging from identifying anomalies in chemometric data to developing an early warning system for the disease epidemics for the Veterans Administration. He has also provided statistical support to various research programs at SDSU such as analyzing data related to medical decisions of breast cancer patients to predicting the location of Asian Carp under iced-over South Dakota lakes. He is currently serving as vice chair of NIST's OSEC subcommittee on handwriting as the vice chair. Dr. Saunders' applied areas of expertise are statistical pattern recognition, prediction/forecasting, design and analysis of experiments and large scale multiple testing corrections.
Dr. Jixiang Wu received his Ph.D. focusing on quantitative genetics in 2003. While he is housed in the Plant Science Department at South Dakota State University, he has joint appointment with the Department of Mathematics and Statistics. His areas of research expertise include linear mixed model approaches and their applications in quantitative genetics and design of experiments, computational statistics/genetics and computer programming. His current research interests focus on augmented experimental designs, high‐dimensional genome‐wide association mapping with epistasis, categorical variable selection algorithms and computer tool development. Dr. Wu has published over 60 journal refereed papers and released four R packages. He regularly teaches two graduate‐level courses: quantitative genetics and design of experiments. He also teaches two special topics courses: computational genetics and data analysis with R.