Jeremy Watts

Jeremy Watts is a Postdoctoral Associate specializing in the application of machine learning and biomedical technologies to neurological disorders, particularly Parkinson’s disease. His research focuses on optimizing treatment planning, analyzing gait dynamics via sensor technology, and improving clinical outcomes through predictive modeling. He has contributed to advancements in deep brain stimulation (DBS) state classification, gene expression prediction, and water level forecasting using deep learning techniques.

Key research areas include machine learning for clinical decision-making, sensor-based diagnostics, and algorithm development for healthcare applications. His work bridges computational methods with neurology, aiming to enhance precision medicine and treatment personalization.

Jeremy’s publications highlight interdisciplinary approaches, addressing challenges in Parkinson’s disease management, anomaly detection frameworks, and environmental modeling. He collaborates on projects involving wearable sensors, reinforcement learning, and Bayesian statistical methods.