Jay Sonagra
Papers
1
Total Citations
3
H-Index
1
About
Jay Sonagra is a researcher at the intersection of machine learning, sensor data analysis, and surgical education. His work focuses on developing objective, interpretable tools to quantify surgical expertise, moving beyond traditional subjective assessments. In his notable 2025 paper, "Explainable machine learning using EMG and accelerometer sensor data quantifies surgical skill and identifies biomarkers of expertise," Sonagra demonstrates how wearable sensor data—electromyography and accelerometers—can be used to train explainable machine learning models that not only assess skill levels but also identify specific physiological biomarkers of expertise. This work addresses a critical gap in robotic and simulation-based training, offering a scalable, data-driven approach to surgical education. While his citation count is still growing, his research has already garnered attention for its potential to transform how surgical proficiency is measured and taught. Sonagra’s contributions are paving the way for more precise, objective feedback systems in medicine, making him a rising voice in the fields of explainable AI and surgical analytics.
Research Focus
Key Achievements
Top Papers
- 1