Robert Manduca

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Professor Manduca’s research focuses on urban and regional economic development, asking why some cities and regions prosper while others decline, how federal policy influences urban fortunes, and how neighborhood social and economic conditions shape life outcomes. He studies these topics using computer simulations, spatial clustering methods, network analysis, and data visualization.

In other work he explores the consequences of rising income inequality for various aspects of life in the United States, using descriptive methods and simulations applied to Census microdata. This research has shown how rising inequality has lead directly to lower rates of upward mobility and increases in the racial income gap.

9.9.2020 MIDAS Faculty Research Pitch Video.

Screenshot from “Where Are The Jobs?” visualization mapping every job in the United States based on the unemployment insurance records from the Census LODES data. http://robertmanduca.com/projects/jobs.html

Laura Power

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I am an infectious disease and preventive medicine physician and my interests include studying the epidemiology of communicable diseases and and the practice of public health. I’m a clinical assistant professor in the Department of Epidemiology at the School of Public Health and in the Division of Infectious Diseases at the Medical School. My work also focuses on connecting physicians to public health practice; I serve as the Program Director for the Preventive Medicine Residency and I lead a certificate program in population health and health equity for physicians in training. I’m also an associate editor for the American Journal of Preventive Medicine. My research focuses on the epidemiology and prevention of infectious disease, including communicable diseases in the broader public health setting.

Nancy Fleischer

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Dr. Fleischer’s research focuses on how the broader socioeconomic and policy environments impact health disparities and the health of vulnerable populations, in the U.S. and around the world. Through this research, her group employs various analytic techniques to examine data at multiple levels (country-level, state-level, and neighborhood-level), emphasizing the role of structural influences on individual health. Her group applies advanced epidemiologic, statistical, and econometric methods to this research, including survey methodology, longitudinal data analysis, hierarchical modeling, causal inference, systems science, and difference-in-difference analysis. Dr. Fleischer leads two NCI-funded projects focused on the impact of tobacco control policies on health equity in the U.S.

Sunghee Lee

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My research focuses on issues in data collection with hard-to-reach populations. In particular, she examines 1) nontraditional sampling approaches for minority or stigmatized populations and their statistical properties and 2) measurement error and comparability issues for racial, ethnic and linguistic minorities, which also have implications for cross-cultural research/survey methodology. Most recently, my research has been dedicated to respondent driven sampling that uses existing social networks to recruit participants in both face-to-face and Web data collection settings. I plan to expand my research scope in examining representation issues focusing on the racial/ethnic minority groups in the U.S. in the era of big data.

Yajuan Si

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My research lies in cutting-edge methodology development in streams of Bayesian statistics, complex survey inference, missing data imputation, causal inference, and data confidentiality protection. I have extensive collaboration experiences with health services researchers and epidemiologists to improve healthcare and public health practice, and have been providing statistical support to solve sampling and analysis issues on health and social science surveys.

Shu-Fang Shih

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Shu-Fang Shih, Ph.D., has a diverse background in public health, business administration, risk management and insurance, and actuarial science. Her research has focused on design, implementation, and evaluation of theory-based health programs for children, adolescents, pregnant women, and older adults in various settings. In addition, she used econometric methods, psychometric, and other statistical methods to examine various health issues among children, adolescent, emerging adulthood, pregnant women, and the older adults. She is particularly interested in designing effective ways to align public health, social services, and healthcare to achieve the goal of family-centered and integrated/coordinated care for the family.

John E Marcotte

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John E. Marcotte, PhD is a statistician and data security expert. His research concerns data sharing, data security, data management, disclosure, health policy, nursing staffing and patient outcomes. He has over 25 years of experience implementing computing systems and performing quantitative analysis. During his career, Marcotte has served as a quantitative researcher, biostatistician, data archivist, data security officer and computing director. Among Marcotte’s statistical fortes are linear and logistic regression, survival analysis and sampling while his computing specialties include secure systems, high performance systems and numerical methods. He has collaborated with social and natural scientists as well as nurses and physicians. Marcotte regularly presents at professional conferences and contributes to invited panels on data security and disclosure. He has formal training in Demography, Statistics and Computer Science.

Research Data Security Options

Aaron A. King

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The long temporal and large spatial scales of ecological systems make controlled experimentation difficult and the amassing of informative data challenging and expensive. The resulting sparsity and noise are major impediments to scientific progress in ecology, which therefore depends on efficient use of data. In this context, it has in recent years been recognized that the onetime playthings of theoretical ecologists, mathematical models of ecological processes, are no longer exclusively the stuff of thought experiments, but have great utility in the context of causal inference. Specifically, because they embody scientific questions about ecological processes in sharpest form—making precise, quantitative, testable predictions—the rigorous confrontation of process-based models with data accelerates the development of ecological understanding. This is the central premise of my research program and the common thread of the work that goes on in my laboratory.

Mousumi Banerjee

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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.