Graduate Research Assistant/Postdoctoral Scholar – Data Labeling for Clinical ML
Developed automated labeling and classification pipelines for physiological time-series data, including EEG and EMG, for supervised machine learning models. Created clinically meaningful labels for sleep stages and epileptic activity using an open-source ML platform (SIESTA) and validated models across diverse datasets. Built and curated high-quality, analysis-ready datasets from heterogeneous sources for AI/ML training in health data analytics. • Labeled large-scale multimodal physiological recordings (EEG/EMG) for sleep stages and seizure events • Engineered and documented feature and label creation workflows for cohort studies • Conducted rigorous quality control and cross-validation for annotated data • Mentored junior researchers in structured data labeling and harmonization