Bio-X Graduate Student Fellow

Awarded in 2019
Home Department: Neurosciences
Faculty Advisors: Amit Etkin (Psychiatry & Behavioral Sciences), Fei-Fei Li (Computer Science), and Russell Poldrack (Psychology)

Research Title: Closed-Loop Treatment Optimization for Repetitive Transcranial Magnetic Stimulation with Reinforcement Learning

Research Description: Major depression is a highly prevalent (and often drug-resistant) disorder. Repetitive Transcranial Magnetic Stimulation (rTMS) leads to remissionPhoto of graduate student Molly Lucas in a dry lab, working on a computer showing a 3D model of a brain and many lines of code. in about 30-40% of patients, which is impressive in spite of this being a first-generation treatment protocol. There is much room for improvement, particularly with regards to selecting stimulation parameters to maximize therapeutic effects. Using reinforcement learning algorithms, Molly will develop a fully-automated, individualized approach for rTMS. This adaptive protocol will modify treatment parameters in real time for each unique subject with the goal of eliciting a certain brain state quantified by electroencephalography (EEG) with higher efficacy than traditional rTMS.


Molly is a Data Scientist at Janssen Pharmaceuticals (within Johnson & Johnson). Her work focuses on using machine learning and digital health strategies to improve patient tracking and pharmacological development. Additionally, Molly is a Lecturer at Columbia University, where she teaches graduate-level AI & Ethics.