My research interests span computer science and neuroimaging, specifically in the areas of applied machine learning, signal processing, and translational medicine. My current research portfolio brings together speech, brain imaging, and neuroinformatics to address gaps in scientific knowledge in three areas:
1) Speech and clinical applications: Understanding the neural basis and translational applications of human spoken communication and applying speech technology to healthcare, specifically psychiatric, neurodegenerative, and speech motor control disorders. More generally, to see the spoken signal as a window into our mind and health, more broadly.
2) Machine learning for personalized medicine: Integrating imaging, genetic, and behavioral data using machine learning algorithms for clinical diagnosis, prognosis, and treatment prediction. We link predictability with explainability and interpretability, alongside ethical and diverse data and models.
3) Neuroinformatics and reproducible research: Preserving information for reproducible research and knowledge generation. We do this by establishing standards for organizing scientific information and creating performant tools to address the data and information deluge and reproducibility.