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.

Vahid Lotfi

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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.

Weiqi Li

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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.

The concepts of search trajectories and solution attractor in a multi-start local search system.

The concepts of search trajectories and solution attractor in a multi-start local search system.

Syagnik Banerjee

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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

9.9.2020 MIDAS Faculty Research Pitch Video.

Vicki Johnson-Lawrence

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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.