Aviation safety is crucial, as failures—whether from human error, technical problems, or mechanical issues—can lead to devastating consequences. This research seeks to improve the early detection of these failures by using machine learning (ML) models to identify abnormal events, such as engine malfunctions, before they lead to accidents. Ultimately, by validating whether simulators can provide reliable training data, this project aims to create safer and more equitable aviation protocols.

Ahmad Ghauri is a Ph.D. Student in Electrical Engineering and Computer Science at Russ College of Engineering and Technology - Ohio University. He earned his bachelors and masters degree in Computer Science from COMSATS Institute of Information Technology, Lahore, Pakistan and University of Management & Technology, Lahore, Pakistan, respectively. At Ohio University, Ahmad is actively engaged in a research project that explores the simulation-to-real transfer problem, particularly how flight simulators like X-Plane can generate data for training machine learning models to predict real-world flight issues, such as engine failure. Ahmad has contributed to several conferences and research papers, including their recent work accepted at AIAA SciTech, and their ongoing interest lies in the intersection of artificial intelligence, aviation safety, and neuromorphic computing.

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