Lead software engineer for HealthGPS, a high-performance microsimulation platform modeling long-term public health and economic outcomes. Collaborated with health economists to simulate disease pathways (e.g., diabetes, cardiovascular disease) and assess policy cost-effectiveness using IHME and NHS datasets. Developed scalable HPC workflows, improving runtime efficiency by over 200%.
Conceptualized and developed an automated spike-sorting software pipeline using Python and MATLAB. Improved spike classification accuracy by 50% and reduced processing time by 25%, enhancing neuroinformatics data workflows.
Developed an embedded Wavelet Neural Network (WNN) system for real-time ECG signal monitoring and anomaly detection with 93% accuracy. Integrated IoT pipelines for health data processing and collaborated with industry experts on biomedical device prototypes.
Conducted experimental and computational studies on Lissajous figures, analyzing harmonic oscillations and resonance patterns using advanced signal processing techniques.
Focused on machine learning, computational neuroscience, and intelligent robotics. Thesis: Multi-Class Neuronal Spike Classification with Deep Learning.
Developed expertise in software engineering, machine learning, and cloud infrastructure. Thesis: Embedded Wavelet Neural Network for Real-time ECG Analysis.