Applied astronomy techniques to detect Alzheimer's at 88.9% accuracy
Ayaan built a cross-domain approach that adapts astrobiology signal processing, including pulsar timing techniques used to pull faint signals out of noisy astronomical data, to detect Alzheimer's patterns in brain scans. His machine learning models trained on those cross-domain features reached 88.9% accuracy. The work was published in IEEE and won 3rd place at his science fair.
AI & Neuroscience
IEEE published and 3rd place science fair
IEEE, 2026
Ayaan Rustagi, YRI Fellow
A 10th grader at Rouse High School interested in AI and healthcare, with no research or publication experience.
IEEE-published author and 3rd place science fair winner with cross-domain AI research.
“I applied astronomy techniques like pulsar timing to detect Alzheimer's from brain scans, achieving 88.9% accuracy. YRI helped me bridge astrobiology and neuroscience, and I won 3rd place at my science fair.”
IEEE conference paper published
3rd place award at his science fair
88.9% accuracy detecting Alzheimer's patterns from brain scans
First application of astrobiology signal processing to neurodegenerative disease detection
Shaswat Senthilkumar, 1st Place, Mercer Science & Engineering Fair at Princeton University