Name:Rijul Saini

Bio:

I am currently working as a Data & Applied Scientist at NAV CANADA, where I design and implement advanced forecasting models for strategic planning and develop systems for predictive Air Traffic Management (ATM) performance analytics. My work focuses on leveraging Machine Learning, Predictive Modeling, and Data Science to optimize operational efficiency and support data-driven decision-making in aviation.

I hold a Ph.D. in Computer Science from McGill University, where I transitioned from a Master’s program in 2017 to a fast-tracked Ph.D. in 2019 under the mentorship of Professor Gunter Mussbacher. My doctoral research centered on building a recommendation system for requirements engineering, enabling practitioners to rapidly create domain models from informal natural language requirements. This system utilized Natural Language Processing (NLP) and Machine Learning to extract domain knowledge and construct queryable trace models as knowledge graphs, enhancing explainability and user interaction.

Prior to joining NAV CANADA, I gained extensive industry experience at National Research Council Canada, Bombardier Aerospace, and Accenture, where I applied my expertise in Data Science, Machine Learning, and Software Engineering to design predictive analytics systems and enterprise applications.

Country:Canada

Affiliation:NAV CANADA

Research interests:Aviation Analytics, Model-Driven Requirements Engineering, Machine Learning, Natural Language Processing, and Data Science