Stefanus Jasin

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My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.

 

Fabian Pfeffer

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My research investigates social inequality and its maintenance across time and generations. Current projects focus on wealth inequality and its consequences for the next generation, the institutional context of social mobility processes and educational inequality in the United States and other industrialized countries. I also help expand the social science data infrastructure and quantitative methods needed to address questions on inequality and mobility. I serve as Principal Investigator of the Wealth and Mobility (WAM) study as well as Co-Investigator of the Panel Study of Income Dynamics (PSID). As such, my research draws on and helps construct nationally representative survey data as well as full-population administrative data. My methodological work has been focused on causal inference, multiple imputation, and measurement error.

Meha Jain

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‚ÄčI am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.

Conducting wheat crop cuts to measure yield in India, which we use to train algorithms that map yield using satellite data

J.J. Prescott

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Broadly, I study legal decision making, including decisions related to crime and employment. I typically use large social science data bases, but also collect my own data using technology or surveys.

Edgar Franco-Vivanco

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Edgar Franco-Vivanco is an Assistant Professor of Political Science and a faculty associate at the Center for Political Studies. His research interests include Latin American politics, historical political economy, criminal violence, and indigenous politics.

Prof. Franco-Vivanco is interested in implementing machine learning tools to improve the analysis of historical data, in particular handwritten documents. He is also working in the application of text analysis to study indigenous languages. In a parallel research agenda, he explores how marginalized communities interact with criminal organizations and abusive policing in Latin America. As part of this research, he is using NLP tools to identify different types of criminal behavior.

Examples of the digitization process of handwritten documents from colonial Mexico.

Kevin Stange

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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.

Mithun Chakraborty

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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 Hausman

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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

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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.

Ranjan Pal

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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.