Professor Saigal has held faculty positions at the Haas School of Business, Berkeley and the department of Industrial Engineering and Management Sciences at Northwestern University, has been a researcher at the Bell Telephone Laboratories and numerous short term visiting positions. He currently teaches courses in Financial Engineering. In the recent past he taught courses in optimization, and Management Science. His current research involves data based studies of operational problems in the areas of Finance, Transportation, Renewable Energy and Healthcare, with an emphasis on the management and pricing of risks. This involves the use of data analytics, optimization, stochastic processes and financial engineering tools. His earlier research involved theoretical investigation into interior point methods, large scale optimization and software development for mathematical programming. He is an author of two books on optimization and large set of publications in top refereed journals. He has been an associate editor of Management Science and is a member of SIAM, AMS and AAAS. He has served as the Director of the interdisciplinary Financial Engineering Program and as the Director of Interdisciplinary Professional Programs (now Integrative Design + Systems) at the College of Engineering.
Brenda Gillespie, PhD, is Associate Director in Consulting for Statistics, Computing and Analytics Research (CSCAR) with a secondary appointment as Associate Research Professor in the department of Biostatistics in the School of Public Health at the University of Michigan, Ann Arbor. She provides statistical collaboration and support for numerous research projects at the University of Michigan. She teaches Biostatistics courses as well as CSCAR short courses in survival analysis, regression analysis, sample size calculation, generalized linear models, meta-analysis, and statistical ethics. Her major areas of expertise are clinical trials and survival analysis.
Prof. Gillespie’s research interests are in the area of censored data and clinical trials. One research interest concerns the application of categorical regression models to the case of censored survival data. This technique is useful in modeling the hazard function (instead of treating it as a nuisance parameter, as in Cox proportional hazards regression), or in the situation where time-related interactions (i.e., non-proportional hazards) are present. An investigation comparing various categorical modeling strategies is currently in progress.
Another area of interest is the analysis of cross-over trials with censored data. Brenda has developed (with M. Feingold) a set of nonparametric methods for testing and estimation in this setting. Our methods out-perform previous methods in most cases.
Kevin Dombkowski, DrPH, is Research Associate Professor in the department of Pediatrics, Medical School, and holds a secondary appointment in the School of Public Health at the University of Michigan, Ann Arbor.
Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions. Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems. Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.
Further, his research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems. He has led numerous collaborations with the Michigan Department of Health and Human Services, including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR). Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic.
Michael Elliott, PhD, is a Professor of Biostatistics, School of Public Health, and Research Scientist at the Institute for Social Research at the University of Michigan, Ann Arbor.
Dr. Elliott’s statistical research interests focus around the broad topic of “missing data,” including the design and analysis of sample surveys, casual and counterfactual inference, and latent variable models. He has worked closely with collaborators in injury research, pediatrics, women’s health, and the social determinants of physical and mental health. Dr. Elliott serves as an Associate Editor for the Journal of the American Statistical Association.
Pascal Van Hentenryck, Phd, is the Seth Bonder Collegiate Professor of Industrial and Operations Engineering, Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.
His research is concerned with evidence-based optimization, the idea of optimizing complex systems holistically, exploiting the unprecedented amount of available data. It is driven by an exciting convergence of ideas in big data, predictive analytics, and large-scale optimization (prescriptive analytics) that provide, for the first time, an opportunity to capture human dynamics, natural phenomena, and complex infrastructures in optimization models. He applies evidence-based optimization to challenging applications in environmental and social resilience, energy systems, marketing, social networks, and transportation. Key research topics include the integration of predictive (machine learning, simulation, stochastic approximation) and prescriptive analytics (optimization under uncertainty), as well as the integration of strategic, tactical, and operational models.
The video above is of a planned evacuation of 70,000 persons for a 1-100 year flood in the Hawkesbury-Nepean Region using both predictive and prescriptive analytics and large data sets for the terrain, the population, and the transportation network.
Muzammil M. Hussain is an Assistant Professor of Communication Studies, and Faculty Associate in the Institute for Social Research at the University of Michigan.
Dr. Hussain’s interdisciplinary research is at the intersections of global communication, comparative politics, and complexity studies. At Michigan, Professor Hussain teaches courses on research methods, digital politics, and global innovation. His published books include “Democracy’s Fourth Wave? Digital Media and the Arab Spring” (Oxford University Press, 2013), a cross-national comparative study of how digital media and information technologies have supported the opening-up of closed societies in the MENA, and “State Power 2.0: Authoritarian Entrenchment and Political Engagement Worldwide” (Ashgate Publishing, 2013), an international collection detailing how governments, both democracies and dictatorships, are working to close-down digital systems and environments around the world. He has authored numerous research articles, book chapters, and industry reports examining global ICT politics, innovation, and policy, including pieces in The Journal of Democracy, The Journal of International Affairs, The Brookings Institution’s Issues in Technology and Innovation, The InterMedia Institute’s Development Research Series, International Studies Review, International Journal of Middle East Affairs, The Communication Review, Policy and Internet, and Journalism: Theory, Practice, and Criticism.
Daniel Almirall, Ph.D., is Assistant Professor in the Survey Research Center and Faculty Associate in the Population Studies Center in the Institute for Social Research at the University of Michigan.
Prof. Almirall’s current methodological research interests lie in the broad area of causal inference. He is particularly interested in methods for causal inference using longitudinal data sets in which treatments, covariates, and outcomes are all time-varying. He is also interested in developing statistical methods that can be used to form adaptive interventions, sometimes known as dynamic treatment regimes. An adaptive intervention is a sequence of individually tailored decisions rules that specify whether, how, and when to alter the intensity, type, or delivery of treatment at critical decision points in the medical care process. Adaptive interventions are particularly well-suited for the management of chronic diseases, but can be used in any clinical setting in which sequential medical decision making is essential for the welfare of the patient. They hold the promise of enhancing clinical practice by flexibly tailoring treatments to patients when they need it most, and in the most appropriate dose, thereby improving the efficacy and effectiveness of treatment.
Study Design Interests: In addition to developing new statistical methodologies, Prof. Almirall devotes a portion of his research to the design of sequential multiple assignment randomized trials (SMARTs). SMARTs are randomized trial designs that give rise to high-quality data that can be used to develop and optimize adaptive interventions.
Substantive Interests: As an investigator and methodologist in the Institute for Social Research, Prof. Almirall takes part in research in a wide variety of areas of social science and treatment (or interventions) research. He is particularly interested in the substantive areas of mental health (depression, anxiety) and substance abuse, especially as related to children and adolescents.
My current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. I also conduct research in statistical software.