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.
My interests are in the areas of labor economics, program evaluation, and the economics of education. Currently my research focuses on college student debt accumulation and the subsequent risk of default, the effect of tuition subsidies on college attendance, the influence of family wealth on college attendance and completion, the effect of financial aid packages on college attendance, completion and subsequent labor market earnings, the influence of education on job displacement and subsequent earnings, the impact of unemployment insurance rules on unemployment durations and re-employment wages, and the determinants and consequences of repeat use of the unemployment insurance system.
Prof. Titiunik’s research interests lie primarily in quantitative methodology for the social sciences, with emphasis on quasi-experimental methods for causal inference and political methodology. She is particularly interested in the application and development of non-experimental methods for the study of political institutions, a methodological agenda that is motivated by her substantive interests on democratic accountability and the role of party systems in developing democracies. Some of her current projects include the application of web scraping and text analysis tools to measure political phenomena.
Professor Saigal has held faculty positions at the Haas School of Business, Berkeley and the department of Industrial Engineering and Management Sciences at Northwestern University, has been a researcher at the Bell Telephone Laboratories and numerous short term visiting positions. He currently teaches courses in Financial Engineering. In the recent past he taught courses in optimization, and Management Science. His current research involves data based studies of operational problems in the areas of Finance, Transportation, Renewable Energy and Healthcare, with an emphasis on the management and pricing of risks. This involves the use of data analytics, optimization, stochastic processes and financial engineering tools. His earlier research involved theoretical investigation into interior point methods, large scale optimization and software development for mathematical programming. He is an author of two books on optimization and large set of publications in top refereed journals. He has been an associate editor of Management Science and is a member of SIAM, AMS and AAAS. He has served as the Director of the interdisciplinary Financial Engineering Program and as the Director of Interdisciplinary Professional Programs (now Integrative Design + Systems) at the College of Engineering.
Professor Seiford’s research interests are primarily in the areas of quality engineering, productivity analysis, process improvement, multiple-criteria decision making, and performance measurement. In addition, he is recognized as one of the world’s experts in the methodology of Data Envelopment Analysis. His current research involves the development of benchmarking models for identifying best-practice in manufacturing and service systems. He has written and co-authored four books and over one hundred articles in the areas of quality, productivity, operations management, process improvement, decision analysis, and decision support systems.
Matias D. Cattaneo, Ph.D., is Professor of Economics and Statistics in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.
Prof. Cattaneo’s research interests include econometric theory, mathematical statistics, and applied econometrics, with focus on causal inference, program evaluation, high-dimensional problems and applied microeconomics. Most of his recent research relates to the development of new, improved semiparametric, nonparametric and high-dimensional inference procedures exhibiting demonstrable superior robustness properties with respect to tuning parameter and other implementation choices. His work is motivated by concrete empirical problems in social, biomedical and statistical sciences, covering a wide array of topics in settings related to treatment effects and policy evaluation, high-dimensional models, average derivatives and structural response functions, applied finance and applied decision theory, among others.
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.
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.
Jessica K. Camp, PhD, is Assistant Professor of social work in the Department of Health and Health Services at the University of Michigan, Dearborn.
Her research focuses on using large nationally representative data from the United States and internationally (SIPP, ACS, GSOEP) to explore trends in poverty and inequality. Specifically, I examine ways that marginalized and hyper-marginalized groups experience economic disparity and labor market exclusion. My most recent completed study showed how welfare reform can have a powerful impact on the well-being of working women, especially women with vulnerabilities. My area of expertise as a data analyst is in complex samples, regression, and longitudinal models. I am hoping my future work will inform ways that “Big Data” can be used in social work research.
Exploring properties of spatial-econometric methods for valid estimation of interdependent processes, i.e., estimation of spatially & spatiotemporally dynamic responses, primarily in political science and political economy applications. Specific applications have included international tax-competition and national tax & other economic policies, U.S. inter-state policy diffusion, the (possibly contagious) spread of intra- and inter-state conflict.