Accepted at the top biomedical engineering conference as a 9th grader
Suriya built a machine learning framework that analyzes eye-tracking measures including fixation duration, saccade amplitude and blink rate to separate Alzheimer's patients from healthy controls. He tested Random Forest, XGBoost, a feed-forward neural network and ensemble methods with ten-fold cross-validation under signal degradation, reaching a ROC-AUC of 0.75 with horizontal eye-position measures as the strongest indicators. He also built a Streamlit prototype for real-time eye-tracking visualization.
AI & Neuroscience
Accepted at IEEE EMBC 2026 in Toronto
IEEE EMBC 2026, 2026
Suriya Dev Saravanakumar, YRI Fellow
A 9th grader at Doha College comfortable with coding but with no prior research, mentorship or science fair experience.
Accepted at IEEE EMBC, the world's top biomedical engineering conference, as a 9th grader from Qatar.
Accepted at the 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Toronto, July 26 to 30, 2026
ROC-AUC of 0.75 across multiple ML classifiers with ten-fold cross-validation
Identified horizontal eye-position measures as the strongest diagnostic indicators
Built a Streamlit prototype for real-time eye-tracking data visualization
Srihaan Edla, Accepted at IEEE CIBCB 2026 in Greece