Aditya Sengupta, 2026 Davidson Fellow Laureate, $100,000 Scholarship, Age: 18, Hometown: Bellevue, WA
“Being named a Davidson Fellow is an incredible honor because it gives me the opportunity to join a community of young people who are curious, ambitious, and passionate about using their ideas to improve lives,” Sengupta said in statement provided to the Davidson Institute. Sengupta plans to pursue a university degree focusing on computing and the natural sciences, aiming to continue developing engineering applications grounded in physics and computer science, according to the Davidson Institute. Climate models indicate that rising global temperatures are intensifying high-altitude jet streams, which atmospheric scientists project will increase the frequency and severity of clear-air turbulence along major flight routes. Sengupta structured ForeCAT so it could eventually integrate directly into existing Air Traffic Control systems and flight-planning software. Giving air traffic controllers and pilots early warnings about unstable air pockets would allow flight paths and altitudes to be adjusted long before an aircraft reaches hazardous airspace. “I am excited to learn from this community, share ideas with peer Fellows, and carry that spirit of curiosity and inquiry into the next stage of my journey.
An 18-year-old student from Bellevue, Washington, has built an artificial intelligence model designed to predict dangerous clear-air turbulence, earning a $100,000 (£75,000) scholarship from the Davidson Institute. The Davidson Institute, an American educational foundation supporting high-achieving youth, named Sengupta a 2026 Davidson Fellow Laureate for his independent research combining atmospheric physics with computing to solve aviation hazards. This lack of visibility leaves commercial pilots with minimal warning before aircraft enter chaotic atmospheric air pockets, creating risks of passenger injuries and operational disruptions that cost the airline industry an estimated $500 million annually, according to findings presented at the Regeneron International Science and Engineering Fair (ISEF). In additional project data submitted to the Regeneron ISEF, the ForeCAT model achieved a 95% classification accuracy in tests, outperforming traditional industry tools such as the Graphical Turbulence Guidance algorithm. The system also evaluated historic flight data, successfully retro-predicting the severe turbulence event experienced on a Singapore Airlines flight in May 2024 with 87% confidence.
ForeCAT demonstrated significant accuracy improvements over standard turbulence forecasting methods currently used across commercial aviation, according to research documentation published by the Davidson Institute. Aditya Sengupta developed the machine-learning system, named ForeCAT, after experiencing severe and unexpected turbulence during commercial flights. Clear-air turbulence occurs without visual indicators such as cloud formations or severe storms. By embedding physical laws directly into the neural network, the system evaluates where invisible air currents are likely to form and estimates their potential severity.