Difference between revisions of "User:James.hoctor"
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James graduated from Rensselaer Polytechnic Institute in 2016 with a Bachelor of Science in Mathematics. He earned a Master of Science in Computer Science from Duke University in 2019. He interned at General Electric Global Research in 2019, where he worked on surrogate modeling of physical systems using machine learning methods including Gaussian processes and Bayesian optimization. | James graduated from Rensselaer Polytechnic Institute in 2016 with a Bachelor of Science in Mathematics. He earned a Master of Science in Computer Science from Duke University in 2019. He interned at General Electric Global Research in 2019, where he worked on surrogate modeling of physical systems using machine learning methods including Gaussian processes and Bayesian optimization. | ||
| − | Working with fellow Duke CS student Alina Jade Barnett, James earned second place in the inaugural Duke AI for Art Contest. This work repurposed a novel interpretable image recognition method (see https://arxiv.org/abs/1806.10574) to | + | Working with fellow Duke CS student Alina Jade Barnett, James earned second place in the inaugural Duke AI for Art Contest. This work repurposed a novel interpretable image recognition method (see https://arxiv.org/abs/1806.10574) to produce digital collage. The Duke AI for Art Contest was judged on artistic merit by a panel of Duke Arts faculty. The work was also displayed at the Preservation Society of Chapel Hill. |
Latest revision as of 16:30, 28 January 2020
James Hoctor joined Kitware in January 2020 as a Research and Development Engineer in the Medical Computing team, where he works on 3D Slicer and SlicerCMF. In 2017 and 2018, he interned with Kitware's Computer Vision team, and completed projects in deep learning for analysis of Wide-Area Motion Imagery.
James graduated from Rensselaer Polytechnic Institute in 2016 with a Bachelor of Science in Mathematics. He earned a Master of Science in Computer Science from Duke University in 2019. He interned at General Electric Global Research in 2019, where he worked on surrogate modeling of physical systems using machine learning methods including Gaussian processes and Bayesian optimization.
Working with fellow Duke CS student Alina Jade Barnett, James earned second place in the inaugural Duke AI for Art Contest. This work repurposed a novel interpretable image recognition method (see https://arxiv.org/abs/1806.10574) to produce digital collage. The Duke AI for Art Contest was judged on artistic merit by a panel of Duke Arts faculty. The work was also displayed at the Preservation Society of Chapel Hill.
