My current research is in the area of rational approximation in the complex domain. For example, I investigate the convergence of rational function series on the extended complex plane.
My research spans security, privacy, and optimization of data collection particularly as applied to the Smart Grid, an augmented and enhanced paradigm for the conventional power grid. I am particularly interested in optimization approaches that take a notion of security and/or privacy into the modeling explicitly. At the intersection of the Intelligent Transportation Systems, Smart Grid, and Smart Cities, I am interested in data privacy and energy usage in smart parking lots. Protection of data and availability, especially under assault through a Denial-of-Service attacks, represents another dimension of my area of research interests. I am working on developing data privacy-aware bidding applications for the Smart Grid Demand Response systems without relying on trusted third parties. Finally, I am interested in educational and pedagogical research about teaching computer science, Smart Grid, cyber security, and data privacy.
James R. Creps, DScPT, is an Assistant Professor of Orthopedics and the Associate Director of Post-Professional Education and Clinical Practice in the Department of Physical Therapy at the University of Michigan, Flint.
Utilization of regression and decision tree analysis to determine specific scheduling rules to improve utilization and financial viability of outpatient physical therapy services. Cultivation of key management metrics to improve decision making as it relates to scheduling and staffing for outpatient services.
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
My current research interest is focused on improving efficiency and utilization of outpatient clinics, using data mining techniques such as decision tree analysis, Bayesian networks, neural networks, and similar techniques. While our previous and continuing research have been focused on using some of these techniques to develop more sophisticated methods of patients scheduling within physical therapy clinics, we can see the applicability of the techniques to other types of health services providers. There is also applicability to university administration in developing predictive models using data mining techniques for assessing student success.
Dr. Li’s research focuses on new search method to solve the traveling salesman problem (TSP). More specially, this new method uses multi-start local search to construct the solution attractor, search all solutions in the attractor, and then identify the globally optimal solution. He has used this method to tackle classic TSP, multi-objective TSP, dynamic TSP, and probabilistic TSP.
Sy Banerjee studies the impact of mobile devices on consumer behavior and on the processing of signals emerging from location-based Social Media posts. He teaches a MBA class on digital marketing and Big Data and collaborates with researchers from Business, GIS and Computer Science. Some of his recent works include:
Assessing Prime-Time for Geotargeting With Mobile Big Data, Sy Banerjee, Vijay Viswanathan, Kalyan Raman, Hao Ying, Journal of Marketing Analytics, 2013, Vol. 1(3), pp 174-183.
“Visualizing active travel sentiment in an urban context” with Greg Rybarczyk, International Conference on Transport & Health MINETA Transportation Institute, San Jose, California, July 2016.
“Assigning Geo-Relevance of Sentiments Mined from Location-Based Social Media Posts” with R. Sanborn and M. Farmer, in Advances in Intelligent Data Analysis XIV, LNCS
“Understanding In-Store Consumer Experiences via User Generated Content from Social Media”, working paper with Karthik Sridhar and Ashwin Aravindakshan
“Tweeted Customer Emotions as Currency for Competitive Performance: A Framework of Location-Based Social Media Listening”, working paper with Amit Poddar, Karthik Sridhar, Nanda Kumar
Vicki Johnson-Lawrence, PhD, is Assistant Professor in the department of Public Health and Health Sciences at the University of Michigan, Flint.
Dr. Johnson-Lawrence is a social epidemiologist interested in the application of epidemiologic methods that capture the dynamic nature of psychosocial factors over the life course, and how these factors contribute to chronic disease risk. Further, she is interested in racial/ethnic patterns of comorbid mental and physical health outcomes, and how these patterns vary throughout the life course.