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OUR TEAM

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OSAMA A. OSMAN, PH.D.

Lab Director

Dr. Osman is the Assistant Professor of Intelligent Transportation Systems and Data Analytics in the Department of Civil and Chemical Engineering (CCE) and the Mobility Thrust Lead in the Center for Urban Informatics and Progress at the University of Tennessee at Chattanooga (UTC).  Prior to joining UTC, he was a research faculty at the Center for Sustainable Mobility at Virginia Tech Transportation Institute (VTTI).  He holds a PhD degree from Louisiana State University on the User and System Characteristics of Connected Vehicle Technology. He received his B.S and M.S. degrees in Civil Engineering with focus on Transportation Engineering and Traffic Flow Modeling in 2006 and 2010, respectively.  He is the Chair of the Communications Subcommittee and Research Coordinator of the Transportation Research Board Standing Committee on Artificial Intelligence and Advanced Computing Applications.  He is also an active member of the SAE standards committee on Cooperative Driving Automation.

Team: Our Team
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AHMED AREDAH

Graduate Research Assistant - Ph.D.

Ahmed Aredah graduated with a bachelor's degree in the top 10 of his class from the German University in Cairo in 2016.  He also finished his master’s degree from the same institution on the effect of Building Information Modeling (BIM) on project cost and time optimization.  Ahmed worked as a Teaching Assistant then as a Lecturer Assistant from 2016 to 2021 at the German University in Cairo.  He is enthusiastic about desktop application development using many programming languages such as C#, VB.Net, Python, and SQL. His research areas are quite broad, from cost and time optimization to Transportation and traffic planning, Statistics, Machine Learning and deep learning, and Autonomous Vehicles.  He is currently pursuing his PhD in Computer Science at the University of Tennessee at Chattanooga; he is pursuing his research in the broad area of Cooperative Driving Automation.  As part of this, he is currently working on building a multi-modal human-in-the-loop simulation platform.

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FAIZA KHAN

Graduate Assistant - MS

FAIZA KHAN completed her Bachelors in Electrical Engineering from the Mirpur University of Science and Technology, AJK, Pakistan, in October 2018. From 2019 to 2020 she worked with the National Engineering Services Pakistan (NESPAK) as a Junior Engineer in the operations management and business development division.  She is currently pursuing her M.S. degree at the University of Tennessee at Chattanooga, USA, where her research focuses on Connected and Autonomous Vehicles (CAVs) vehicle to vehicle communication, and Cooperative driving Automation (CDA).  She is currently working on the development of an L4 1/10th scale CAV.

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JEWEL PALIT

Graduate Research Assistant - MS

Jewel Palit completed his bachelor’s degree in civil engineering from Chittagong University of Engineering & Technology (CUET), Bangladesh. He has always felt an intense drive toward Transportation Engineering which was his major in undergraduate studies.  Jewel's research focuses on the broad area of ITS, Traffic Simulation, application of data analytics for traffic congestion, and travel behavior analysis. Jewel extensively uses microscopic simulation software for modeling large-scale networks.  He is also working on VR-based simulations using driving and walking simulators.

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JIBRIL BABATUNDE

Graduate Research Assistant - MS

Jibril is a pursuing his master degree in the Civil and Chemical Engineering Department at the University of Tennessee at Chattanooga.  His research spans application of modeling and machine learning techniques for traffic control and congestion management. He received his bachelors degree from the University of Ilorin, Nigeria. Before joining UTC, he worked for four years as a Project Engineer with Reckitt Benckiser Ltd, where he worked on Engineering projects including pavement design and construction, structural development, software and various equipment installation. Jibril's ultimate goal is to develop a novel energy-conscious, cost-effective machine learning model for real-time optimization of city-wide traffic grids; thereby reducing or eliminating congestions and creating more sustainable cities globally.

Team: Our Team
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