Programming
Enhancing Aeronautical Data Accuracy through Machine Learning
Cătălin Roman - Lead Software Architect @ Frequentis
Miruna Morărașu - Software engineer @ Frequentis
Europa Room
8th November, 11:00-11:30
NOTAMs (Notice to Airmen) serve as vital communication messages for personnel involved in flight operations, conveying critical information about abnormal conditions or statuses of aeronautical components. This narrative explores the practical applications of machine learning tools such as TensorFlow, Scikit-learn, and NLTK, demonstrating their role in rectifying, encoding, and categorizing NOTAMs to enhance aviation safety by mitigating potential hazards.
Cătălin Roman
Frequentis
Cătălin is a seasoned software professional specializing in Java backend development. He has designed and implemented software solutions for companies like Nokia and HERE Maps in domains such as Location-Based Services, Mobile Advertising, and e-Commerce.
Currently, at Frequentis, he holds the pivotal role of Lead Software Architect within a dynamic business unit dedicated to delivering Aeronautical Information Management solutions for airline companies and aeronautical authorities.
Miruna Morărașu
Frequentis
Miruna is a full-time Java Developer and a part-time AI Enthusiast who enjoys technical challenges and dabbling in new technologies. Her work at Frequentis is two-fold: first, as a Full-Stack developer and second, as a researcher, working to integrate Machine Learning techniques into the established Aeronautical Information Management solutions the company develops.
