I manage research activities for the College and Beyond II study at ICPSR, including survey development and data infrastructure planning. My research broadly focuses on issues of postsecondary access and success for undergraduate and graduate students and uses quantitative methodologies.
Prof. Stange’s research uses population administrative education and labor market data to understand, evaluate and improve education, employment, and economic policy. Much of the work involves analyzing millions of course-taking and transcript records for college students, whether they be at a single institution, a handful of institutions, or all institutions in several states. This data is used to richly characterize the experiences of college students and relate these experiences to outcomes such as educational attainment, employment, earnings, and career trajectories. Several projects also involve working with the text contained in the universe of all job ads posted online in the US for the past decade. This data is used to characterize the demand for different skills and education credentials in the US labor market. Classification is a task that is arising frequently in this work: How to classify courses into groups based on their title and content? How to identify students with similar educational experiences based on their course-taking patterns? How to classify job ads as being more appropriate for one type of college major or another? This data science work is often paired with traditional causal inference tools of economics, including quasi-experimental methods.
My broad research interests are in multi-agent systems, computational economics and finance, and artificial intelligence. I apply techniques from algorithmic game theory, statistical machine learning, decision theory, etc. to a variety of problems at the intersection of the computational and social sciences. A major focus of my research has been the design and analysis of market-making algorithms for financial markets and, in particular, prediction markets — incentive-based mechanisms for aggregating data in the form of private beliefs about uncertain events (e.g. the outcome of an election) distributed among strategic agents. I use both analytical and simulation-based methods to investigate the impact of factors such as wealth, risk attitude, manipulative behavior, etc. on information aggregation in market ecosystems. Another line of work I am pursuing involves algorithms for allocating resources based on preference data collected from potential recipients, satisfying efficiency, fairness, and diversity criteria; my joint work on ethnicity quotas in Singapore public housing allocation deserves special mention in this vein. More recently, I have got involved in research on empirical game-theoretic analysis, a family of methods for building tractable models of complex, procedurally defined games from empirical/simulated payoff data and using them to reason about game outcomes.
Catherine H. Hausman is an Associate Professor in the School of Public Policy and a Research Associate at the National Bureau of Economic Research. She uses causal inference, related statistical methods, and microeconomic modeling to answer questions at the intersection of energy markets, environmental quality, climate change, and public policy.
Recent projects have looked at inequality and environmental quality, the natural gas sector’s role in methane leaks, the impact of climate change on the electricity grid, and the effects of nuclear power plant closures. Her research has appeared in the American Economic Journal: Applied Economics, the American Economic Journal: Economic Policy, the Brookings Papers on Economic Activity, and the Proceedings of the National Academy of Sciences.
Rahul Ladhania is an Assistant Professor of Health Informatics in the Department of Health Management & Policy at the University of Michigan School of Public Health. He also has a secondary (courtesy) appointment with the Department of Biostatistics at SPH. Rahul’s research is in the area of causal inference and machine learning in public and behavioral health. A large body of his work focuses on estimating personalized treatment rules and heterogeneous effects of policy, digital and behavioral interventions on human behavior and health outcomes in complex experimental and observational settings using statistical machine learning methods.
Rahul co-leads the Machine Learning team at the Behavior Change For Good Initiative (Penn), where he is working on two `mega-studies’ (very large multi-arm randomized trials): one in partnership with a national fitness chain, to estimate the effects of behavioral interventions on promoting gym visit habit formation; and the other in partnership with two large Mid-Atlantic health systems and a national pharmacy chain, to estimate the effects of text-based interventions on increasing flu shot vaccination rates. His other projects involve partnerships with step-counting apps and mobile-based games to learn user behavior patterns, and design and evaluate interventions and their heterogeneous effects on user behavior.
Cyber-security is a complex and multi-dimensional research field. My research style comprises an inter-disciplinary (primarily rooted in economics, econometrics, data science (AI/ML/Bayesian and Frequentist Statistics), game theory, and network science) investigation of major socially pressing issues impacting the quality of cyber-risk management in modern networked and distributed engineering systems such as IoT-driven critical infrastructures, cloud-based service networks, and app-based systems (e.g., mobile commerce, smart homes) to name a few. I take delight in proposing data-driven, rigorous, and interdisciplinary solutions to both, existing fundamental challenges that pose a practical bottleneck to (cost) effective cyber-risk management, and futuristic cyber-security and privacy issues that might plague modern (networked) engineering systems. I strongly strive for originality, practical significance, and mathematical rigor in my solutions. One of my primary end goals is to conceptually get arms around complex, multi-dimensional information security and privacy problems in a way that helps, informs, and empowers practitioners and policy makers to take the right steps in making the cyber-space more secure.
Anthony Vanky develops and applies data science and computational methods to design, plan, evaluate cities, emphasizing their applications to urban planning and design. Broadly, his work focuses on the domains of transportation and human mobility; social behaviors and urban space; policy evaluation; quantitative social sciences; and the evaluation of urban form. Through this work, he has extensively collaborated with public and private partners. In addition, he considers creative approaches toward data visualization, public engagement and advocacy, and research methods.
Anthony Vanky’s Cityways project analyzed 2.2 million trips from 135,000 people over one year to understand the factors that influence outdoor pedestrian path choice. Factors considered included weather, urban morphology, businesses, topography, traffic, the presence of green spaces, among others.
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.
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
I am a social epidemiologist with expertise in data collection, analysis, and translation. My research is focused on quantifying health inequities at the individual, community, and national level and examining how policy and social factors impact these inequities. My experience has spanned academic, clinical, and community settings, providing me with a unique perspective on the value and need for epidemiologic research and dissemination in multiple contexts. My current work focuses on the health equity impact of tobacco product use as part of the University of Michigan Tobacco Center of Regulatory Science, the Center for the Assessment of Tobacco Regulations (CAsToR). I am examining sociodemographic inequities in polytobacco use (the use of multiple tobacco products) across multiple nationally representative datasets. I am also an active member of CAsToR’s Data Analysis and Dissemination (DAD) Core. Additionally, I am collaborating with colleagues in Chicago to disseminate findings from a community-level probability survey of 10 Chicago communities, of which I served as Co-PI while working at a hospital system in Chicago. We continue to publish on the unique survey process, sharing our community-driven approach to conducting research and disseminating findings in partnership with surveyed communities.
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.