My research interests lie in design and analysis of randomized controlled trials (RCTs), partial identification, identification and inference with multi-valued treatments and instruments, and quantile regression. In one recent paper I study the optimal stratified randomization procedure in RCTs, and found a certain kind of matched-pair design is optimal. In another paper (coauthored with Joe Romano and Azeem Shaikh), we provide asymptotically exact inference procedure for matched-pair designs. In another paper we study inference with moment inequalities whose dimension grows exponentially fast with the sample size. I also have a paper in which we study the sharp identified sets for various treatment effects with multi-valued instruments and multi-values treatments.
Albert S. Berahas is an Assistant Professor in the department of Industrial & Operations Engineering. His research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous, and arise in a plethora of areas such as engineering design, economics, transportation, robotics, machine learning and statistics. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.
My main interest is theoretical statistics as implied to complex model from semiparametric to ultra high dimensional regression analysis. In particular the negative aspects of Bayesian and causal analysis as implemented in modern statistics.
An analysis of the position of SCOTUS judges.
Greg’s research primarily investigates information flow in financial markets and the actions of agents in those markets – both consumers and producers of that information. His approach draws on theory from the social sciences (economics, psychology and sociology) combined with large data sets from diverse sources and a variety of data science approaches. Most projects combine data from across multiple sources, including commercial data bases, experimentally created data and extracting data from sources designed for other uses (commercial media, web scrapping, cellphone data etc.). In addition to a wide range of econometric and statistical methods, his work has included applying machine learning , textual analysis, mining social media, processes for missing data and combining mixed media.
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.
My applied research focuses on simulation models of the progression of multiple chronic complications and comorbidities of diabetes and its precursors. I study the effectiveness and cost-effectiveness of early interventions in the progression of diabetes. My methodological research synthesizes secondary data from complementary studies to model complex processes.
Xiaoling Xiang conducts community-based services research concerning the physical and mental health and service use of diverse older populations. She is particularly interested in psychosocial approaches to promoting mental health and enhancing the quality of life in older adults. Her other areas of research include the epidemiology of mental disorders in late life, comorbidity, quality of home and community-based services, and implementation of evidence-based interventions. She uses a variety of applied statistical methods in the analysis of data from national surveys, electronic medical records, insurance claims.
My work falls into three general application areas. I am an applied (accredited) biostatistician with a strong team science motivation and I collaborate with scientists in primarily the biomedical sciences, contributing expertise in experimental design, statistical analysis/modeling, and data visualization. I have held faculty appointments in Schools of Medicine and Nursing, and also worked as a senior scientist in the Human Research Program at the NASA Johnson Space Center. I currently direct an Applied Biostatistics Laboratory and Data Management Core within the UM School of Nursing, and maintain several collaborative research programs within the School, at NASA, and with collaborators elsewhere.