Mohammad Karasneh

About

Mohamad Karasneh is a dedicated and driven professional with a strong academic background and expertise in the field of transportation engineering. Currently, he is a master's student at the University of Cincinnati, and a Graduate Research Assistant at the Center for Smart, Sustainable, and Resilient Infrastructure (CSSRI). With a keen interest in emerging technologies, Mohamad specialized in developing a real-time detection and evaluation framework driven by artificial intelligence (AI) for transportation infrastructure. His research involved leveraging cutting-edge tools and advanced software to integrate sensor data into traffic signs evaluation and other infrastructure assets, enabling proactive and data-driven decision-making. Throughout his academic journey, Mohamad has demonstrated proficiency in computer skills and research methodologies. His research experience spans various areas, including traffic modeling and simulation, asset management, autonomous vehicles (AVs) sensors, and machine learning. He is also interested in getting involved in more areas of transportation engineering such as: CAVs, Automated vehicle driving and testing, & Traffic control systems. Learn more about the CSSRI at: https://ceas.uc.edu/research/centers-labs/center-for-smart-sustainable-and-resilient-infrastructure.html

Work

Michigan State University
|

Graduate Research Assisstant

US

University of Cincinnati
|

Graduate Research Assisstant

US

Education

Michigan State University
United States of America

Ph.D. in Civil Engineering

University of Cincinnati
United States of America

Master of Science in Civil Engineering

Yarmouk University
Jordan

Bachelor degree of Science in Engineering Technology - Civil Engineering

Publications

An Artificial Intelligence–Driven Approach for Real-Time Detection of Traffic-Sign Deficiencies

Published by

Transportation Research Record: Journal of the Transportation Research Board

Summary

journal-article

Enhancing Road Safety on US Highways: Leveraging Advanced Computer Vision for Automated Guardrail Damage Detection and Evaluation

Published by

Buildings

Summary

journal-article

Enhancing Road Safety on US Highways: Leveraging Advanced Computer Vision for Automated Guardrail Damage Detection and Evaluation

Published by

Buildings

Summary

journal-article

A LiDAR-camera fusion approach for automated detection and assessment of potholes using an autonomous vehicle platform

Published by

Innovative Infrastructure Solutions

Summary

journal-article