Halil Bisgin

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My research is focused on a wide range of topics from computational social sciences to bioinformatics where I do pattern recognition, perform data analysis, and build prediction models. At the core of my effort, there lie machine learning methods by which I have been trying to address problems related to social networks, opinion mining, biomarker discovery, pharmacovigilance, drug repositioning, security analytics, genomics, food contamination, and concussion recovery. I’m particularly interested in and eager to collaborate on cyber security aspect of social media analytics that includes but not limited to misinformation, bots, and fake news. In addition, I’m still pursuing opportunities in bioinformatics, especially about next generation sequencing analysis that can be also leveraged for phenotype predictions by using machine learning methods.

A typical pipeline for developing and evaluating a prediction models to identify malicious Android mobile apps in the market

Victoria Morckel

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Dr. Morckel uses spatial and statistical methods to examine ways to improve quality of life for people living in shrinking, deindustrialized cities in the Midwestern United States. She is especially interested in the causes and consequences of population loss, including issues of vacancy, blight, and neighborhood change.

Suitability Analysis Results: Map of Potential Properties to Naturalize in the City of Flint, Michigan.

Amal Alhosban

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Amal Alhosban, is an Associate 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.

Murali Mani

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Murali Mani, PhD, is 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.

9.9.2020 MIDAS Faculty Research Pitch Video.

Illustrating how our SMART system effectively integrates big data processing and data visualization to enable big data visualization. The left side shows a typical data visualization scenario, where the different analysts are using their different visualization systems. These visualization systems can provide interactive visualizations but cannot handle the complexities of big data. They interact with a distributed data processing system that can handle the complexities of big data. The SMART system improves the user experience by carefully sending additional data to the visualization system in response to a request from an analyst so that future visualization requests can be answered directly by the visualization system without accessing the data processing system.

Illustrating how our SMART system effectively integrates big data processing and data visualization to enable big data visualization. The left side shows a typical data visualization scenario, where the different analysts are using their different visualization systems. These visualization systems can provide interactive visualizations but cannot handle the complexities of big data. They interact with a distributed data processing system that can handle the complexities of big data. The SMART system improves the user experience by carefully sending additional data to the visualization system in response to a request from an analyst so that future visualization requests can be answered directly by the visualization system without accessing the data processing system.

 

Mark Allison

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Mark Allison, PhD, is Associate 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.

 

Amy M. Yorke

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Amy M. Yorke, PT, PhD, NCS, is Assistant Professor of Physical Therapy at the University of Michigan, Flint.

 

Rie Suzuki

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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

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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.

Suleyman Uludag

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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.

This figure shows the data collection model I used in developing a practical and secure Machine-to-Machine data collection protocol for the Smart Grid.

This figure shows the data collection model I used in developing a practical and secure
Machine-to-Machine data collection protocol for the Smart Grid.

Greg Rybarczyk

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Dr. Greg Rybarczyk is an Associate Professor of Geography at the University of Michigan-Flint. He is also a Fellow at the Urban Design/Mental Health Institute (UK) and Director of the GIS program. He received his Ph.D. from the University of Wisconsin-Milwaukee and has over a decade of experience utilizing geospatial and empirical approaches to examine active transportation, mobility, travel sentiment, urban design, accessibility, food systems, and public health.

Recent works:

Platt, L., and G. Rybarczyk. (2020) “Skateboarder and scooter rider perceptions of the urban environment: A qualitative analysis of user generated content,” Urban Geography, DOI: 10.1080/02723638.2020.1811554.

Rybarczyk, G., A. Ozbil, E. Andresen, and Z. Hayes. (2020) “Physiological responses to urban design during bicycling: A naturalistic investigation,” Transportation Research Part F: Psychology and Behaviour, 68: 79-93; https://doi.org/10.1016/j.trf.2019.12.001

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, 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 12(1): 40-62

9.9.2020 MIDAS Faculty Research Pitch Video.