Neural, Sensor, and Time-Series Modeling

Applied statistical modeling, deep learning, and signal-processing methods to noisy biological, neural, and sensor time-series data. Work includes neural population timing, cross-area coupling, denoising, trial-level variability, neuromotor signal modeling, and robust feature extraction under subject, session, and task variability.

Focus areas: time-series ML, neural data analysis, sensor modeling, signal processing, uncertainty, statistical validation

Technologies: Python, PyTorch, SciPy, NumPy, Bayesian inference, statistical modeling, dimensionality reduction, signal processing