Explore ARCExplore ARC

Veera Baladandayuthapani

By |

Dr. Veera Baladandayuthapani is currently a Professor in the Department of Biostatistics at University of Michigan (UM), where he is also the Associate Director of the Center for Cancer Biostatistics. He joined UM in Fall 2018 after spending 13 years in the Department of Biostatistics at University of Texas MD Anderson Cancer Center, Houston, Texas, where was a Professor and Institute Faculty Scholar and held adjunct appointments at Rice University, Texas A&M University and UT School of Public Health. His research interests are mainly in high-dimensional data modeling and Bayesian inference. This includes functional data analyses, Bayesian graphical models, Bayesian semi-/non-parametric models and Bayesian machine learning. These methods are motivated by large and complex datasets (a.k.a. Big Data) such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. His work has been published in top statistical/biostatistical/bioinformatics and biomedical/oncology journals. He has also co-authored a book on Bayesian analysis of gene expression data. He currently holds multiple PI-level grants from NIH and NSF to develop innovative and advanced biostatistical and bioinformatics methods for big datasets in oncology. He has also served as the Director of the Biostatistics and Bioinformatics Cores for the Specialized Programs of Research Excellence (SPOREs) in Multiple Myeloma and Lung Cancer and Biostatistics&Bioinformatics platform leader for the Myeloma and Melanoma Moonshot Programs at MD Anderson. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. He currently serves as an Associate Editor for Journal of American Statistical Association, Biometrics and Sankhya.

 

An example of horizontal (across cancers) and vertical (across multiple molecular platforms) data integration. Image from Ha et al (Nature Scientific Reports, 2018; https://www.nature.com/articles/s41598-018-32682-x)

Xiang Zhou

By |

My research is focused on developing efficient and effective statistical and computational methods for genetic and genomic studies. These studies often involve large-scale and high-dimensional data; examples include genome-wide association studies, epigenome-wide association studies, and various functional genomic sequencing studies such as bulk and single cell RNAseq, bisulfite sequencing, ChIPseq, ATACseq etc. Our method development is often application oriented and specifically targeted for practical applications of these large-scale genetic and genomic studies, thus is not restricted in a particular methodology area. Our previous and current methods include, but are not limited to, Bayesian methods, mixed effects models, factor analysis models, sparse regression models, deep learning algorithms, clustering algorithms, integrative methods, spatial statistics, and efficient computational algorithms. By developing novel analytic methods, I seek to extract important information from these data and to advance our understanding of the genetic basis of phenotypic variation for various human diseases and disease related quantitative traits.

A statistical method recently developed in our group aims to identify tissues that are relevant to diseases or disease related complex traits, through integrating tissue specific omics studies (e.g. ROADMAP project) with genome-wide association studies (GWASs). Heatmap displays the rank of 105 tissues (y-axis) in terms of their relevance for each of the 43 GWAS traits (x-axis) evaluated by our method. Traits are organized by hierarchical clustering. Tissues are organized into ten tissue groups.

Mousumi Banerjee

By |

My research is primarily focused around 1) machine learning methods for understanding healthcare delivery and outcomes in the population, 2) analyses of correlated data (e.g. longitudinal and clustered data), and 3) survival analysis and competing risks analyses. We have developed tree-based and ensemble regression methods for censored and multilevel data, combination classifiers using different types of learning methods, and methodology to identify representative trees from an ensemble. These methods have been applied to important areas of biomedicine, specifically in patient prognostication, in developing clinical decision-making tools, and in identifying complex interactions between patient, provider, and health systems for understanding variations in healthcare utilization and delivery. My substantive areas of research are cancer and pediatric cardiovascular disease.

Samuel K Handelman

By |

Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.

Brenda Gillespie

By |

Brenda Gillespie, PhD, is Associate Director in Consulting for Statistics, Computing and Analytics Research (CSCAR) with a secondary appointment as Associate Research Professor in the department of Biostatistics in the School of Public Health at the University of Michigan, Ann Arbor. She provides statistical collaboration and support for numerous research projects at the University of Michigan. She teaches Biostatistics courses as well as CSCAR short courses in survival analysis, regression analysis, sample size calculation, generalized linear models, meta-analysis, and statistical ethics. Her major areas of expertise are clinical trials and survival analysis.

Prof. Gillespie’s research interests are in the area of censored data and clinical trials. One research interest concerns the application of categorical regression models to the case of censored survival data. This technique is useful in modeling the hazard function (instead of treating it as a nuisance parameter, as in Cox proportional hazards regression), or in the situation where time-related interactions (i.e., non-proportional hazards) are present. An investigation comparing various categorical modeling strategies is currently in progress.

Another area of interest is the analysis of cross-over trials with censored data. Brenda has developed (with M. Feingold) a set of nonparametric methods for testing and estimation in this setting. Our methods out-perform previous methods in most cases.

Bhramar Mukherjee

By |

Bhramar Mukherjee is  a Professor in the Department of Biostatistics, joining the department in Fall, 2006. Bhramar is also a Professor in the Department of Epidemiology. Bhramar completed her Ph.D. in 2001 from Purdue University. Bhramar’s principal research interests lie in Bayesian methods in epidemiology and studies of gene-environment interaction. She is also interested in modeling missingness in exposure, categorical data models, Bayesian nonparametrics, and the general area of statistical inference under outcome/exposure dependent sampling schemes. Bhramar’s methodological research is funded by NSF and NIH.   Bhramar is involved as a co-investigator in several R01s led by faculty in Internal Medicine, Epidemiology and Environment Health sciences at UM. Her collaborative interests focus on genetic and environmental epidemiology, ranging from investigating the genetic architecture of colorectal cancer in relation to environmental exposures to studies of air pollution on pediatric Asthma events in Detroit. She is actively engaged in Global Health Research.

Kevin Dombkowski

By |

Kevin J. Dombkowski, DrPH., MS, is Research Professor with the Child Health Evaluation and Research (CHEAR) Center within the University of Michigan Department of Pediatrics.   He is a health services researcher working extensively with public health information systems and large administrative claims databases.  

Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions.  Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems.  Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services.  He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.  Kevin is currently collaborating with Michigan State University on the design and development of the Flint Lead Exposure Registry (FLExR) information architecture.

Kevin’s research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems.  He has led numerous collaborations with the Michigan Department of Health and Human Services (MDHHS), including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR).  Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic.  Kevin is currently collaborating with MDHHS to evaluate MCIR data quality as immunization providers across Michigan adopt real-time, bi-directional messaging between electronic health records and MCIR.   He is conducting a similar statewide evaluation as new messaging protocols are adopted by electronic laboratory systems for reporting blood lead testing results to MDHHS.

Jon Zelner

By |

Jon Zelner, PhD, is Assistant Professor in the department of Epidemiology in the University of Michigan School of Public Health. Dr. Zelner holds a second appointment in the Center for Social Epidemiology and Population Health.

Dr. Zelner’s research is focused on using spatial analysis, social network analyisis and dynamic modeling to prevent infectious diseases, with a focus on tuberculosis and diarrheal disease. Jon is also interested in understanding how the social and biological causes of illness interact to generate observable patterns of disease in space and in social networks, across outcomes ranging from infection to mental illness.

 

A large spatial cluster of multi-drug resistant tuberculosis (MDR-TB) cases in Lima, Peru is highlighted in red. A key challenge in my work is understanding why these cases cluster in space: can social, spatial, and genetic data tell us where transmission is occurring and how to interrupt it?

A large spatial cluster of multi-drug resistant tuberculosis (MDR-TB) cases in Lima, Peru is highlighted in red. A key challenge in my work is understanding why these cases cluster in space: can social, spatial, and genetic data tell us where transmission is occurring and how to interrupt it?

 

 

Steven J. Katz

By |

Dr. Katz’s research addresses cancer treatment communication, decision-making, and quality of care. His work aims to examine the dynamics of how precision medicine presents itself in the exam room via provider and patient communication and shared decision-making. Dr. Katz leads the Cancer Surveillance and Outcomes Research Team (CanSORT), an interdisciplinary research program centered at the University of Michigan and focused on population and intervention studies of the quality of care and outcomes of cancer detection and treatment in diverse populations.  Dr. Katz and CanSORT have been collaborating with Surveillance, Epidemiology, and End Results (SEER) cancer registries since 2002 to study breast cancer treatment decision making at the population level. We obtain patient clinical and demographic information from SEER and combine this with surveys of patients and physicians to create comprehensive data sets that enable us to study testing and treatment trends and the challenges of individualizing treatments for breast cancer patients. In 2015 we added a new dimension to our research by partnering with evaluative testing firms to obtain tumor genomic and germline genetic test results for over 30,000 breast and ovarian cancer patients in the states of California and Georgia. We are also pursuing insurance claims data to assist with our analysis of physician network effects.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Trivellore E. Raghunathan

By |

Dr. Raghunathan’s primary research interest is in developing methods for dealing with missing data in sample surveys and in epidemiological studies. The methods are motivated from a Bayesian perspective but with desirable frequency or repeated sampling properties. The analysis of incomplete data from practical sample surveys poses additional problems due to extensive stratification, clustering of units and unequal probabilities of selection. The model-based approach provides a framework to incorporate all the relevant sampling design features in dealing with unit and item nonresponse in sample surveys. There are important computational challenges in implementing these methods in practical surveys. He has developed SAS based software, IVEware, for performing multiple imputation analysis and the analysis of complex survey data. Raghunathan’s other research interests include Bayesian methods, methods for small area estimation, combining information from multiple surveys, measurement error models, longitudinal data analysis, privacy, confidentiality and disclosure limitations and statistical methods for epidemiological studies. His applied interests include cardiovascular epidemiology, social epidemiology, health disparity, health care utilization, and social and economic sciences. Raghunathan is also involved in the Survey Methodology Program at the Institute for Social Research, a multidisciplinary team of sociologists, statisticians and psychologists, provides an opportunity to address methodological issues in: nonresponse, interviewer behavior and its impact on the results, response or measurement bias and errors, noncoverage, respondent cognition, privacy and confidentiality issues and data archiving. The Survey Methodology Program has a graduate program offering masters and doctoral degrees in survey methodology.