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.
Marcelline Harris, Ph.D., R.N., is Associate Professor of Systems, Populations and Leadership in the School of Nursing at the University of Michigan, Ann Arbor.
Dr. Harris’s research interests focus on what is being labeled the “continuous use” of clinical data (the use of clinical data for one or more purposes), computable knowledge representation strategies, and the use of electronic clinical data for practice and research. Her research has been funded by NIH, AHRQ, RWJF, and PCORI. Harris also has extensive enterprise level experience, having served in both scientific and operational positions that address the development and governance of systems that support the capture, storage, indexing, and retrieval of clinical data. At Michigan, she retains this translational perspective, emphasizing clinical data for patient-centered research, clinical surveillance and predictive analytics.
Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.
Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data. They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.
Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.
Peter Adriaens, PhD, is Professor of Civil and Environmental Engineering, College of Engineering, Professor of Environment and Sustainability, School for Environment and Sustainability and Professor of Entrepreneurship, Stephen M Ross School of Business, at the University of Michigan, Ann Arbor.
Prof. Adriaens’ research focuses on the use of data science to uncover trends and features in a range of financial (‘fintech’) applications relevant to economic development and investments aimed at catalyzing sustainable growth, including:
1. Network mapping to query relations in financial networks using visualization techniques
2. Trend and features prediction of value capture and investment grade in startup business models, using machine learning, natural language processing, and decision tools
3. Asset risk pricing of stocks exposed to water risk in their supply chains, using statistical methods, and portfolio theory predictions
4. Financial risk modeling of multi-asset investment funds to drive low carbon economies, leveraging network mapping, and machine learning.
Kai S. Cortina, PhD, is Professor of Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.
Prof. Cortina’s major research revolves around the understanding of children’s and adolescents’ pathways into adulthood and the role of the educational system in this process. The academic and psycho-social development is analyzed from a life-span perspective exclusively analyzing longitudinal data over longer periods of time (e.g., from middle school to young adulthood). The hierarchical structure of the school system (student/classroom/school/district/state/nations) requires the use of statistical tools that can handle these kind of nested data.
Dr. Suzuki is a behavioral scientist and has major research interests in examining and intervening mediational social determinants factors of health behaviors and health outcomes across lifespan. She analyzes the National Health Interview Survey, Medical Expenditure Panel Survey, National Health and Nutrition Examination Survey as well as the Flint regional medical records to understand the factors associating with poor health outcomes among people with disabilities including children and aging.
Using GIS, visual analytics, and spatiotemporal modeling, Dr. Rybarczyk examines the utility of Big Data for gaining insight into the causal mechanisms that influence travel patterns and urban dynamics. In particular, his research sets out to provide a fuller understanding of â€œwhatâ€ and â€œwhereâ€ micro-scale conditions affect human sentiment and hence wayfinding ability, movement patterns, and travel mode-choices.
Recent works: Rybarczyk, G. and S. Banerjee. (2015) Visualizing active travel sentiment in an urban context, Journal of Transport and Health, 2(2): 30
Perry Samson, PhD, is the Arthur F Thurnau Professor of Climate and Space Sciences and Engineering, College of Engineering and Professor of Information, School of Information, at the University of Michigan, Ann Arbor.
Prof. Samson has developed LectureTools with NSF support in response to a need to increase opportunities for student participation in larger lecture courses. It was subsequently spun off campus using NSF SBIR funding and was acquired by Echo360 which has incorporated it into its Active Learning Platform (ALP). ALP collects data on how students behave before, during and after class including how many slides they view, how many notes they type, how many questions they answer and how many gradable questions they get correct as well as what question they pose and how often do they indicate confusion.
These unique data are used to understand how student participation is related to exam grades and to build models to forecast which students will have trouble in class far earlier in the semester. His goal is to combine data from ALP with other data sets to ascertain which, if any, participation data allows the best prediction of student success.
Keshav Pokhrel, PhD, is Assistant Professor of Statistics at the University of Michigan, Dearborn.
Prof. Pokhrel’s research interests include the epidemiology of cancer, time series forecasting, quantile regression and functional data analysis. The skewed and non-normal data are increasingly more frequent than ever before. The data in the extreme ends are of their own importance. Hence the importance of quantile regression. The availability of the information is increasingly functional. My current work is gearing towards functional data analysis techniques such as principal differential analysis which can estimate a system of differential equations to reveal the dynamics of real data.
The basis of my work is to make the often invisible traces created by interactions students have with learning technologies available to instructors, technology solutions, and students themselves. This often requires the creation of new novel educational technologies which are designed from the beginning with detailed tracking of user activities. Coupled with machine learning and data mining techniques (e.g. classification, regression, and clustering methods), clickstream data from these technologies is used to build predictive models of student success and to better understand how technology affords benefits in teaching and learning. I’m interested in broadly scaled teaching and learning through Massive Open Online Courses (MOOCs), how predictive models can be used to understand student success, and the analysis of educational discourse and student writing.