Presented at NeurIPS on collaborative research into modeling neural signals and their use in understanding neuropsychiatric disorders.
Research Experience
Collaborating with a variety of collaborators to develop and use interpretable probabilistic models and deep learning to gain insights from various signals. For instance, involved in learning treatment and diagnostic biomarkers from electrophysiological data collected during an Autism Spectrum Disorder clinical trial; previously revealed neural biomarkers of stress susceptibility in an animal model of depression. Additionally collaborates on other applied problems such as air quality estimation and computational toxicology.
Background
Focused on developing novel machine learning methodologies to facilitate and advance a diverse set of applications. Specifically, building tools for data-driven science where information automatically derived from large, complex observations of 'big data' are used to facilitate experimental design and hypothesis generation.