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S. Sriram

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S. Sriram, PhD, is Associate Professor of Marketing in the University of Michigan Ross School of Business, Ann Arbor.

Prof. Sriram’s research interests are in the areas of brand and product portfolio management, multi-sided platforms, healthcare policy, and online education. His research uses state of the art econometric methods to answer important managerial and policy-relevant questions. He has studied topics such as measuring and tracking brand equity and optimal allocation of resources to maintain long-term brand profitability, cannibalization, consumer adoption of technology products, and strategies for multi-sided platforms. Substantively, his research has spanned several industries including consumer packaged goods, technology products and services, retailing, news media, the interface of healthcare and marketing, and MOOCs.

Erhan Bayraktar

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Erhan Bayraktar, PhD, the holder of the Susan Smith Chair, is a full professor of Mathematics at the University of Michigan, where he has been since 2004. Professor Bayraktar’s research is in stochastic analysis, control, applied probability and mathematical finance. He has over 120 publications in top journals in these areas.

Professor Bayraktar is recognized as a leader in his areas of research: he is a corresponding editor in the SIAM Journal on Control and Optimization and also serves in the editorial boards of Applied Mathematics and Optimization, Mathematics of Operations Research, Mathematical Finance. His research has been also been continually funded by the National Science Foundation; in particular, he received a CAREER grant.

Professor Bayraktar has also been devoting his time to teaching and synergistic activities: Professor Bayraktar has been the director of the Risk Management and Quantitative Finance Masters program since its inception in 2015. As one of the two organizers of the Financial/Actuarial Math seminar which brings about 10-15 speakers every academic year and he has also organized several international workshops in stochastic analysis for finance and insurance in Ann Arbor.

Areas of interest: Mathematical finance, applied probability, stochastic analysis, stochastic control, optimal stopping.

Antonios M. Koumpias

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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.

Evolution of Annual Changes in All-cause Childless Adult Mortality in New York State following 2001 State Medicaid Expansion

Brian P. McCall

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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.

Matthew Shapiro

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Prof. Shapiro is the Lawrence R. Klein Collegiate Professor of Economics, College of Literature, Science, and the Arts and Research Professor, Survey Research Center, Institute for Social Research, at the University of Michigan, Ann Arbor.

Prof. Shapiro’s general area of research is macroeconomics. He has studied investment and capital utilization, business-cycle fluctuations, consumption and saving, financial markets, monetary policy, fiscal policy, and time-series econometrics. Among his current research interests are consumption, saving, retirement, and portfolio choices of households, the effects of tax policy on investment, using surveys in macroeconomics, and improving the quality of national economic statistics.

Derek Harmon

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My research focuses on the intended and unintended consequences of language in financial markets. I examine this relationship across a number of contexts, such as the Federal Reserve, initial public offerings, and mergers and acquisitions. More broadly, my work aims to develop new theoretical and methodological approaches to understand the role of language in society.

Peter Adriaens

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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

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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.

The marginal effects of health IT on mortality by diagnosis and deciles of severity. We study the affect of hospitals' electronic health record (EHR) systems on patient outcomes. While we observe no benefits for the average patient, mortality falls significantly for high-risk patients in all EHR-sensitive conditions. These patterns, combined findings from other analyses, suggest that EHR systems may be more effective at supporting care coordination and information management than at rules-based clinical decision support. McCullough, Parente, and Town, "Health information technology and patient outcomes: the role of information and labor coordination." RAND Journal of Economics, Vol. 47, no. 1 (Spring 2016).

The marginal effects of health IT on mortality by diagnosis and deciles of severity. We study the affect of hospitals’ electronic health record (EHR) systems on patient outcomes. While we observe no benefits for the average patient, mortality falls significantly for high-risk patients in all EHR-sensitive conditions. These patterns, combined findings from other analyses, suggest that EHR systems may be more effective at supporting care coordination and information management than at rules-based clinical decision support. McCullough, Parente, and Town, “Health information technology and patient outcomes: the role of information and labor coordination.” RAND Journal of Economics, Vol. 47, no. 1 (Spring 2016).

Michael Cafarella

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Michael Cafarella, PhD, is Associate Professor of Electrical Engineering and Computer Science, College of Engineering and Faculty Associate, Survey Research Center, Institute for Social Research, at the University of Michigan, Ann Arbor.

Prof. Cafarella’s research focuses on data management problems that arise from extreme diversity in large data collections. Big data is not just big in terms of bytes, but also type (e.g., a single hard disk likely contains relations, text, images, and spreadsheets) and structure (e.g., a large corpus of relational databases may have millions of unique schemas). As a result, certain long-held assumptions — e.g., that the database schema is always known before writing a query — are no longer useful guides for building data management systems. As a result, my work focuses heavily on information extraction and data mining methods that can either improve the quality of existing information or work in spite of lower-quality information.

A peek inside a Michigan data center! My students and I visit whenever I am teaching EECS485, which teaches many modern data-intensive methods and their application to the Web.

A peek inside a Michigan data center! My students and I visit whenever I am teaching EECS485, which teaches many modern data-intensive methods and their application to the Web.