Neural Engineering Data Consortium
Vinit Shah is an experienced data scientist and engineer specializing in machine learning and data analysis, particularly in the fields of neuroscience and biomedical applications. Currently, Vinit serves as an EEG Annotation & Quality Control Engineer for the Neural Engineering Data Consortium. Prior roles include Data Scientist positions at Axbio Inc. and Alto Neuroscience, where Vinit focused on deep learning experiments and the analysis of physiological data for neuro-biomarker identification. Vinit has also contributed as a Research Assistant at Temple University, developing real-time seizure detection software, and held various technical roles in machine learning and data science across several companies. Vinit holds a PhD and an MS in Electrical Engineering from Temple University, as well as additional degrees from Cleveland State University and Sardar Patel University.
This person is not in any teams
Neural Engineering Data Consortium
The Neural Engineering Data Consortium (NEDC) launched to focus the research community on high-impact, common-interest neural engineering research questions. Critically, the NEDC will also generate and curate massive data sets to support statistically significant data-driven solutions to those problems. Competition-based evaluations on common data will drive progress by incentivizing innovation, especially from unfunded groups that are unable to generate their own data. NEDC's first dataset is the Temple University Hospital EEG Corpus (TUH-EEG), which is the world's largest publicly available database of clinical EEG data.