Dothang Truong, Ph.D.

Dr. Dothang Truong is Associate Dean for School of Graduate Studies and Professor of Aviation Data Science at Embry Riddle Aeronautical University, Daytona Beach, FL. He leads and oversees all academic, administrative, and strategic aspects of the graduate programs within the School of Graduate Studies, ensuring the quality and effectiveness of the programs, curriculum, and faculty. He received his Ph.D. in Manufacturing Management and Engineering from the University of Toledo in 2004. He is an ASCM Certified Supply Chain Professional (CSCP) and a member of the American Institute of Aeronautics and Astronautics (AIAA)’s Air Transportation Systems Technical Committee. In addition, he serves on National Science Foundation (NSF) Review Panel in the Advanced Manufacturing Division and Transportation Research Board’s Economics and Forecasting Committee. He has strong expertise in aviation safety, artificial intelligence and machine learning in aviation, natural language processing, data mining, big data analytics, air transportation, operations research, optimization, and simulation. He has extensive skills and experience in data analytics software, including SAS Enterprise Miner, SAS Text Miner, SPSS, SPSS Modeler, AMOS, STATISTICA, NVivo, and LINGO. He teaches data mining, operations research and decision-making, advanced statistics, structural equation modeling, logistics and supply chain management, and transportation management. His research interests include Natural Language Processing (NLP) and Machine Learning (ML) in aviation safety, leveraging Artificial Intelligence (AI) to build predictive models for aviation safety and air transportation efficiency, a new safety culture framework for aviation maintenance, and air transport resilience system.
He has worked on multiple research grants funded by the Federal Aviation Administration (FAA), FAA's Center of Excellence for UAS Research (ASSURE), National Science Foundation (NSF), Airlines, Faculty Innovative Research in Science and Technology (FIRST), and Boeing Center for Aviation and Aerospace Safety (BCAAS). Those projects involved developing and testing the Safety Management System (SMS) effectiveness model, developing a new safety culture framework for aviation maintenance, performing comprehensive data analysis for non-segregated UAS operations into the National Airspace System (NAS), mining the airline injury data, and applications of machine learning in aviation safety. In addition, he received several internal research grants to develop predictive models for flight delays in the NAS and to use machine learning algorithms to predict small UAS violation incidents. He has used and is familiar with major aviation safety databases, including ASIAS, AIDS, FOQA, ASAP, LOSA, ASRS, NTSB, ASPM, and UAS Sighting Reports.
His research accomplishment has been highly recognized in the aviation research community. Notably, he received the Frank Sorenson’s Research Award for the outstanding achievement of excellence in aviation research and scholarship in 2022. Furthermore, he received the Blackboard Exemplary Course Award in 2014 and the Best Research Paper Award from the International Academy of Business and Public Administration Disciplines for two consecutive years, 2006 and 2007.
Regarding scholarship, he has published 64 peer-reviewed articles in high-impact journals, one book, three book chapters, and 36 conference proceedings. The significance of his publications is proven with more than 2,000 citations, an h-index of 26, and an i10-index of 37. His most recent book "Data Science and Machine Learning for Non-programmers: Using SAS Enterprise Miner" is well recognized and reflects his dedication to making complex topics accessible to broader audiences.