(734) 615-5290
Applications: Behavioral Science, Civic Infrastructure, Complex Systems, Computer Vision, Education, Electrical Engineering, Environmental Sciences, Nanotechnology, Sensors and Sensor Networks, Transportation Research, Urban Planning Methodologies: Algorithms, Bayesian Methods, Classification, Data Management, Data Mining, Data Visualization, Database Systems and Infrastructure, Dynamical Models, Image Data Processing and Analysis, Machine Learning, Optimization, Pattern Analysis and Classification, Predictive Modeling, Real-time Data Processing, Signal Processing, Spatio-Temporal Data Analysis, Time Series Analysis Relevant Projects:

USDOT RITA Program

National Science Foundation Cyber-Physical Systems Program

Office of Naval Research

Connections:

Michigan Department of Transportation

Union Pacific Railroad

National Center for Research in Earthquake Engineering (Taiwan)

Jerome P. Lynch

Professor, Donald Malloure Department Chair, Civil and Environmental Engineering, College of Engineering

Affiliation(s):

Electrical Engineering and Computer Science, College of Engineering

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.