Greg Rybarczyk

By |

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.

Jerome P. Lynch

By |

Jerome P. Lynch, PhD, is Professor and Donald Malloure Department Chair of the Civil and Environmental Engineering Department in the College of Engineering in the University of Michigan, Ann Arbor.

Prof. Lynch’s group works at the forefront of deploying large-scale sensor networks to the built environment for monitoring and control of civil infrastructure systems including bridges, roads, rail networks, and pipelines; this research portfolio falls within the broader class of cyber-physical systems (CPS). To maximize the benefit of the massive data sets, they collect from operational infrastructure systems, and undertake research in the area of relational and NoSQL database systems, cloud-based analytics, and data visualization technologies. In addition, their algorithmic work is focused on the use of statistical signal processing, pattern classification, machine learning, and model inversion/updating techniques to automate the interrogation sensor data collected. The ultimate aim of Prof. Lynch’s work is to harness the full potential of data science to provide system users with real-time, actionable information obtained from the raw sensor data collected.

A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.

A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.