The Natasha Chida Lab investigates methods for using education and curriculum development to improve patient outcomes worldwide, primarily by optimizing education of physicians-in-training. Most recently, our team has worked to develop and evaluate an assessment tool for evaluating internal medicine residents’ understanding of tuberculosis diagnostics. Previous research includes a retrospective cohort study on the high proportion of extrapulmonary TB in a low-prevalence setting as well as an analysis of ways to define clinical excellence in adult infectious disease practice.

Our laboratory pioneers innovations at the intersection of precision neurology, neuroengineering, artificial intelligence, and data science. We develop advanced neural-AI interfaces, autonomous wearable neurotechnologies, and immersive augmented and virtual reality platforms incorporating novel multimodal neuron-sensing technologies designed to personalize diagnostics, enhance therapeutic interventions, and optimize neurological rehabilitation. Leveraging computational neuroscience, AI, and applied data science, we generate robust digital biomarkers to monitor and treat neurologic diseases in real-time. Through interdisciplinary collaborations, we aim to transform clinical practice by providing precise, interactive, and personalized neurologic care that dramatically improves patient outcomes.

Work in the Maunank Shah Lab focuses on infectious disease modeling and health economics, and seeks to investigate new strategies for diagnosing HIV and tuberculosis (TB) in areas with limited resources, both domestically and abroad. Our primary focus is TB diagnostics, with studies examining the diagnostic test accuracy, cost-effectiveness and programmatic impact of emerging diagnostics. We have developed mobile health initiatives to incorporate video-based therapy for TB treatment, and we have a longstanding interest in interventions that help to reduce or prevent HIV transmission.