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.
Antonios M. Koumpias, Ph.D., is Assistant Professor of Economics in the department of Social Sciences at the University of Michigan, Dearborn. Prof. Koumpias is an applied microeconomist with research interests in public economics, with an emphasis on behavioral tax compliance, and health economics. In his research, he employs quasi-experimental methods to disentangle the causal impact of policy interventions that occur at the aggregate (e.g. states) or the individual (e.g. taxpayers) level in a comparative case study setting. Namely, he relies on regression discontinuity designs, regression kink designs, matching methods, and synthetic control methods to perform program evaluation that estimates the causal treatment effect of the policy in question. Examples include the use of a regression discontinuity design to estimate the impact of a tax compliance reminders on payments of overdue income tax liabilities in Greece, matching methods to measure the influence of mass media campaigns in Pakistan on income tax filing and the synthetic control method to evaluate the long-term effect of state Medicaid expansions on mortality.
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.
Ding Zhao, PhD, is Assistant Research Scientist in the department of Mechanical Engineering, College of Engineering with a secondary appointment in the Robotics Institute at The University of Michigan, Ann Arbor.
Dr. Zhao’s research interests include autonomous vehicles, intelligent/connected transportation, traffic safety, human-machine interaction, rare events analysis, dynamics and control, machine learning, and big data analysis
My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies. Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.
The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.
Kai S. Cortina, PhD, is Professor of Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.
Prof. Cortina’s major research revolves around the understanding of children’s and adolescents’ pathways into adulthood and the role of the educational system in this process. The academic and psycho-social development is analyzed from a life-span perspective exclusively analyzing longitudinal data over longer periods of time (e.g., from middle school to young adulthood). The hierarchical structure of the school system (student/classroom/school/district/state/nations) requires the use of statistical tools that can handle these kind of nested data.
Jeffrey S. McCullough, PhD, is Associate Professor in the department of Health Management and Policy in the School of Public Health at the University of Michigan, Ann Arbor.
Prof. McCullough’s research focuses on technology and innovation in health care with an emphasis on information technology (IT), pharmaceuticals, and empirical methods. Many of his studies explored the effect of electronic health record (EHR) systems on health care quality and productivity. While the short-run gains from health IT adoption may be modest, these technologies form the foundation for a health information infrastructure. As scientists are just beginning to understand how to harness and apply medical information, this problem is complicated by the sheer complexity of medical care, the heterogeneity across patients, and the importance of treatment selection. His current work draws on methods from both machine learning and econometrics to address these issues. Current pharmaceutical studies examine the roles of consumer heterogeneity and learning about the value of products as well as the effect of direct-to-consumer advertising on health.
Dr. Suzuki is a behavioral scientist and has major research interests in examining and intervening mediational social determinants factors of health behaviors and health outcomes across lifespan. She analyzes the National Health Interview Survey, Medical Expenditure Panel Survey, National Health and Nutrition Examination Survey as well as the Flint regional medical records to understand the factors associating with poor health outcomes among people with disabilities including children and aging.
My core intellectual interest is the way in which parenting behaviors, like the use of physical punishment, or parental expressions of emotional warmth, have an effect on child outcomes like aggression, antisocial behavior, anxiety and depression, and how these dynamics play out across contexts, neighborhoods, and cultures. A lot of my work is done with international samples. In my work I use statistical models, like multilevel models and some econometric models, and software like Stata, R, HLM and ArcGIS, to examine things like growth and change over time, or community, school or parent effects on children and families. I have emerging interests in text-mining and natural language processing.