Staff R&D Engineer
John Moeller joined Kitware in October of 2016. He received a BS in Mathematics in 1999 from the University of Utah and a PhD in Computing from the University of Utah in 2016.
Prior to his PhD and after his baccalaureate degree, John worked as a software engineer for different companies, notably Enterasys Networks (in the Routing Features group) and Onyx Graphics (working on their RIP products). During his PhD, he did two Summer internships for Google.
John’s research interests include general machine learning, support vector machines (SVMs), kernel methods, multiple kernel learning, and deep learning.
- Kernels and Geometry of Machine Learning," Ph.D. thesis, The University of Utah, 2017. , "
- "A unified view of localized kernel learning," in 2016 SIAM International Conference on Data Mining (SDM), 2016. ,
- "Continuous kernel learning," in 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, 2016. ,
- "Certifying and removing disparate impact," in 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2015. ,
- "A geometric algorithm for scalable multiple kernel learning," in 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014. ,
- "Approximate bregman near neighbors in sublinear time: beyond the triangle inequality," in Proceedings of the 2012 Symposuim on Computational Geometry (SoCG), New York, NY, USA, 2012, pp. 31--40. ,
- "Fast multiple kernel learning with multiplicative weight updates," in 5th NIPS Workshop on Optimization for Machine Learning, 2012. ,
- "Horoball hulls and extents in positive definite space," in Algorithms and Data Structures (ADSS) (Lecture Notes in Computer Science), , Ed., Springer Berlin Heidelberg, 2011, pp. 386-398. ,