1. Identifying bias in models that detect vocal fold paralysis from audio recordings using explainable machine learning and clinician ratings (PLOS Digital Health, 2024)
2. Integrated platform for multiscale molecular imaging and phenotyping of the human brain (Science, 2024)
3. Automated assessment of psychiatric disorders using speech: A systematic review (Laryngoscope Investig Otolaryngol, 2020)
4. Mapping the human subcortical auditory system using histology, post mortem MRI and in vivo MRI at 7T (Elife, 2019)
5. Functional gradients of the cerebellum (Elife, 2018)
6. Distributed Weight Consolidation: A Brain Segmentation Case Study (Neural Information Processing Systems, 2018)
7. Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging (JAMA, 2013)
Research Experience
1. Research interests span computer science and neuroimaging, specifically in applied machine learning, signal processing, and translational medicine.
2. Current research portfolio brings together speech, brain imaging, and neuroinformatics to address gaps in scientific knowledge in three areas:
a) Speech and clinical applications
b) Machine learning for personalized medicine
c) Neuroinformatics and reproducible research
3. The research group develops robust, predictive models around human health and novel analytic platforms, and establishes collaborations across disciplines.
Education
No detailed educational background provided
Background
Director of Open Data in Neuroscience Initiative and a Senior Research Scientist at the McGovern Institute for Brain Research at MIT, an Assistant Professor of Otolaryngology - Head and Neck Surgery at Harvard Medical School, and a faculty member in the Speech and Hearing Biosciences and Technology program in the Harvard Division of Medical Sciences. Trained as a computer scientist and computational neuroscientist.
Miscellany
Personal interests and hobbies not explicitly mentioned in the provided information