Amal Alhosban, is an Assistant Professor of Computer Science at the University of Michigan Flint campus. She received her Ph.D. in Computer Science at Wayne State University in 2013. Her research focuses on Semantic Web and Fault Management and Wireless Network.
Professor Perera is Assistant Professor of Operations and Supply Chain Management in the School of Management at the University of Michigan, Flint
Professor Perera’s research broadly focuses on Supply Chain Management, Revenue Management, the Operations-Finance interface, the Operations-Marketing interface, Healthcare Operations Management and Financial Engineering. He is particularly interested in stochastic and deterministic inventory problems under general cost structures, government (central bank) operations in the foreign exchange market, consumer behavior under social learning, optimal delivery strategies for various supply chain networks, and asymmetric information in fads models. His recent research in healthcare operations management, revenue management, stochastic inventory management and financial engineering are mainly data and algorithm oriented.
Murali Mani, PhD, is Associate Professor of Computer Science at the University of Michigan, Flint.
The significant research problems Prof. Mani is investigating include the following: big data management, big data analytics and visualization, provenance, query processing of encrypted data, event stream processing, XML stream processing. data modeling using XML schemas, and effective computer science education. In addition, he has worked in industry on clickstream analytics (2015), and on web search engines (1999-2000). Prof. Mani’s significant publications are listed on DBLP at: http://dblp.uni-trier.de/pers/hd/m/Mani:Murali.
Mark Allison, PhD, is Assistant Professor of Computer Science in the department of Computer Science, Engineering and Physics at the University of Michigan-Flint.
Dr. Allison’s research pertains to the autonomic control of complex cyberphysical systems utilizing software models as first class artifacts. Domains being explored are microgrid energy management and unmanned aerial vehicles (UAVs) in swarms.
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.
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.
Mehrdad Simkani, PhD, is Professor of Mathematics, College of Arts and Sciences, at the University of Michigan, Flint.
Prof. Simkani’s current research is in the area of rational approximation in the complex domain. For example, he investigates the convergence of rational function series on the extended complex plane.
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.
Rybarczyk, G. and S. Banerjee. (2015) Visualizing active travel sentiment in an urban context, Journal of Transport and Health, 2(2): 30
Rybarczyk, G., S. Banerjee, and M. Starking-Szymanski, and R. Shaker. (2018) “Travel and us: The impact of mode share on sentiment using geosocial media data and GIS” Journal of Location-Based Services (forthcoming)
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.