Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.
Clement Vachet

Clement Vachet -

Director
Biomedical Imaging and Data Analytics Core (BIDAC)
WEB 2863
phone (801) 585-0367
fax (801) 585-6513
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My Publications

Google Scholar Citations

Background

Clement Vachet received an M.S. degree in Electrical Engineering from CPE-Lyon (France) in 2006, and a research M.S. degree in Image Processing from INSA-Lyon (France). Prior to joining the SCI Institute, Clement was a Faculty Research Instructor at the University of North Carolina at Chapel Hill. As a data scientist in the Neuro Image Research and Analysis Laboratories for 5 years, he developed and applied end-to-end imaging data workflows for various clinical studies. With additional interest in business and management, he received an MBA from the University of Utah in 2016.

Current Responsibilities

Clement directs the Biomedical Imaging and Data Analytics Core (BIDAC), a core facility providing application-oriented consulting services to the health science community. By leveraging the expertise of the SCI Institute, BIDAC offers advanced solutions in the areas of deep learning, medical computing, visualization, data science and data engineering.

In his prior role as a Technical Program Manager, Clement managed several NIH-funded projects w.r.t. technological, methodological, scientific testing and validation aspects. In that regard, he has been collaborating regularly with researchers and clinicians in multi-disciplinary nation-wide teams, such as the Autism Center of Excellence network (ACE-IBIS) and the National Alliance for Medical Image Computing (NA-MIC).

Research Interests

His areas of interest entail biomedical computing and data science applied to multi-disciplinary health science collaborations. He has been developing expertise in deep learning, enabling automated image data classification, regression and segmentation. Driving clinical applications have included brain development in autism spectrum disorder, Down syndrome, Huntington's disease, and obsessive-compulsive disorder.