Philadelphia, Pennsylvania, United States
ML & signal processing expert with 10+ years designing real-time, scalable pipelines for biosignal-driven healthtech. Proven track record across academia and startups, delivering FDA-aligned solutions and publishing in top journals (Nature Medicine, Nature Neuroscience). Specialized in EEG, ECG, IMU, and PPG analytics, with experience deploying models on edge and cloud-based platforms. Passionate about transforming complex physiological data into interpretable, clinically meaningful predictions.
Built multimodal pipelines (ECG, EEG, PPG, IMU, thermal) for real-time health monitoring, improving runtime 5× through optimized and parallel processing. Developed algorithms, statistics and reports for arrhythmia, sleep staging, and heat-stress detection in high-activity and noisy environments for both military, clinical and healthcare settings. Collaborated with firmware teams for IoT data acquisition, software teams for Docker and API integration, and product team for FDA pre-certification.
Applied classical ML (logistic regression, SVM, random forest, clustering) and deep learning (CNN/RNN/LSTM) to decode EEG and predict cognitive outcomes using reinforcement learning. Published large-scale brain stimulation (tACS) studies linking oscillatory states to memory and behavior. Mentored graduate and undergraduate students in ML workflows from data preparation through model evaluation.
• Created EEG analysis data pipeline for data collection, preprocessing, source localization, and statistical modeling. • Standardized psychometric experiments programs with a framework ensuring consistent and predictable data acquisition across studies.
● Applied classical ML (logistic regression, SVM, random forest, clustering) and deep learning (CNN/RNN/LSTM) to decode EEG and predict cognitive outcomes using reinforcement learning. ● Published large-scale brain stimulation (tACS) studies linking oscillatory states to memory and behavior. ● Mentored graduate and undergraduate students in ML workflows from data preparation through model evaluation.
● Created EEG analysis data pipeline for data collection, preprocessing, source localization, and statistical modeling. ● Standardized psychometric experiments programs with a framework ensuring consistent and predictable data acquisition across studies.