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.
Sriram Chandrasekaran, PhD, is Assistant Professor of Biomedical Engineering in the College of Engineering at the University of Michigan, Ann Arbor.
Dr. Chandrasekaran’s Systems Biology lab develops computer models of biological processes to understand them holistically. Sriram is interested in deciphering how thousands of proteins work together at the microscopic level to orchestrate complex processes like embryonic development or cognition, and how this complex network breaks down in diseases like cancer. Systems biology software and algorithms developed by his lab are highlighted below and are available at http://www.sriramlab.org/software/.
– INDIGO (INferring Drug Interactions using chemoGenomics and Orthology) algorithm predicts how antibiotics prescribed in combinations will inhibit bacterial growth. INDIGO leverages genomics and drug-interaction data in the model organism – E. coli, to facilitate the discovery of effective combination therapies in less-studied pathogens, such as M. tuberculosis. (Ref: Chandrasekaran et al. Molecular Systems Biology 2016)
– GEMINI (Gene Expression and Metabolism Integrated for Network Inference) is a network curation tool. It allows rapid assessment of regulatory interactions predicted by high-throughput approaches by integrating them with a metabolic network (Ref: Chandrasekaran and Price, PloS Computational Biology 2013)
– ASTRIX (Analyzing Subsets of Transcriptional Regulators Influencing eXpression) uses gene expression data to identify regulatory interactions between transcription factors and their target genes. (Ref: Chandrasekaran et al. PNAS 2011)
– PROM (Probabilistic Regulation of Metabolism) enables the quantitative integration of regulatory and metabolic networks to build genome-scale integrated metabolic–regulatory models (Ref: Chandrasekaran and Price, PNAS 2010)
Jon Zelner, PhD, is Assistant Professor in the department of Epidemiology in the University of Michigan School of Public Health. Dr. Zelner holds a second appointment in the Center for Social Epidemiology and Population Health.
Dr. Zelner’s research is focused on using spatial analysis, social network analyisis and dynamic modeling to prevent infectious diseases, with a focus on tuberculosis and diarrheal disease. Jon is also interested in understanding how the social and biological causes of illness interact to generate observable patterns of disease in space and in social networks, across outcomes ranging from infection to mental illness.
The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.
People’s behavior is often contingent on what other people are doing or have done. In dating and job markets, for example, each person’s choices limit what opportunities are available to others. A classic problem in sociology is explaining the relationship between individuals’ actions and larger-scale social patterns. My strategy is to use computer models of how people’s choices co-evolve with aspects of their environment—known as agent-based models (ABMs)—to determine what behavioral or demographic features are important for understanding social processes. I then use statistical models to assess to what degree these features exist in the real world. Substantively, most of my work examines the drivers of neighborhood segregation. More recently, I embarked on a study of how mate choice strategies shape (and are shaped by) dating, marriage, and affair markets.
With Fred Feinberg (UM Marketing and Statistics), I am also exploring how new data sources can be combined with choice models. The vast amounts of activity data from sources such as cell phones and the Internet make it possible to study human behavior with an unparalleled richness of detail. Such “big data” are interesting in large part because they are behavioral data that allow us to observe how people explore their environment, engage in novel or habitual behaviors, interact with others, and learn from past experiences. In ongoing work, we show how decision processes regarding mate choice can be extracted from online dating activity data.
Andrzej Galecki, MD, PhD, is Research Professor in the department of Biostatistics, School of Public Health, and Research Professor in the Institute of Gerontology at the University of Michigan, Ann Arbor.
Hyun-soo Ahn is an Associate Professor of Operations and Management Science at the Michigan Business School. He joined Michigan in 2003 from the department of Industrial Engineering and Operations Research at UC Berkeley. In his research, Hyun-soo develops and analyzes mathematical models related to supply chain management, dynamic pricing and revenue management, workforce agility, and resource allocation. He is also interested in modeling the customer’s behavior (such as subscription) and how it affects the firm’s profit. He has worked with more than 20 companies and his research has been funded by several organizations including National Science Foundation. His papers appear in leading journals in the field, including Operations Research, M&SOM, and Journal of Applied Probability.
At Ross, he teaches supply chain analytics and business statistics to MBAs, Exec. MBAs, MSCM, and BBAs. He has won a number of teaching awards voted by students, including 2012 Exec MBA teaching excellence award, 2012 Global MBA teaching excellence award, and 2006 BBA teaching excellence award.
Necmiye Ozay, PhD, is Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.
Prof. Ozay and her team develop the scientific foundations and associated algorithmic tools for compactly representing and analyzing heterogeneous data streams from sensor/information-rich networked dynamical systems. They take a unified dynamics-based and data-driven approach for the design of passive and active monitors for anomaly detection in such systems. Dynamical models naturally capture temporal (i.e., causal) relations within data streams. Moreover, one can use hybrid and networked dynamical models to capture, respectively, logical relations and interactions between different data sources. They study structural properties of networks and dynamics to understand fundamental limitations of anomaly detection from data. By recasting information extraction problem as a networked hybrid system identification problem, they bring to bear tools from computer science, system and control theory and convex optimization to efficiently and rigorously analyze and organize information. The applications include diagnostics, anomaly and change detection in critical infrastructure such as building management systems, transportation and energy networks.
My current research focus is on modeling and simulating the value and benefits of various data sharing and policy trade offs. Typically these utilize system dynamics methodologies and tools.
I also have considerable experience across multiple industries with developing processes to enable industry and faculty to identify and solve data science problems using SAS tools.
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.