{"id":30,"date":"2013-10-09T13:48:59","date_gmt":"2013-10-09T17:48:59","guid":{"rendered":"https:\/\/engineering.jhu.edu\/nsa\/?page_id=30"},"modified":"2021-06-29T16:18:02","modified_gmt":"2021-06-29T20:18:02","slug":"research","status":"publish","type":"page","link":"https:\/\/engineering.jhu.edu\/nsa\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

Clinical neuroscience is a field with all the difficulties that come from high dimensional datasets, and none of the advantages that fuel modern-day breakthroughs in domains such as computer vision and natural language processing. It is a field of small sample sizes, complex phenomena, massive patient variability, and most importantly, an arguable lack of ground truth information. The NSA Lab tackles these challenges via a two-pronged approach. On the application front, we identify key knowledge gaps that can advance either our understanding or management of a disorder. On the methodological side, we combine hypothesis-driven insights about the modalities, often from our collaborators, with data-driven learning techniques to address these gaps.<\/p>\n

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Our current projects span the gamut from foundational computational neuroscience to translational applications of functional data to exploratory directions for probing neural circuitry. This multi-pronged approach allow us to generalize the algorithms across datasets and applications. Additionally, our unique modeling strategies can inspire solutions for complex engineering problems outside of the medical realm.<\/p>\n

Click on the links below to learn more about our ongoing research:<\/strong><\/h2>\n