Samuel Gerber

Samuel Gerber

Senior R&D Engineer

Sam studied computer science at the University of Applied Sciences and Arts Northwestern Switzerland, before he completed his Ph.D. at the University of Utah. Prior to joining Kitware, he served as a research scientist at the College of Arts & Sciences Information Technology (CASIT) at the University of Oregon. There, he focused on data analysis methodology. Sam also worked as a visiting assistant professor at Duke University and as a research assistant at the University of Utah. He has won several awards for his efforts, including Best Paper in the “Journal of Medical Image Analysis.”

  1. S. Gerber and M. Maggioni, "Multiscale strategies for computing optimal transport," Journal of Machine Learning Research, p. accepted with minor revision, 2017.
  2. S. Gerber et al., "Morse--smale regression," Journal of Computational and Graphical Statistics, vol. 22, no. 1, pp. 193--214, 2013.
  3. S. Gerber and R. Whitaker, "Regularization-free principal curve estimation," The Journal of Machine Learning Research, vol. 14, no. 1, pp. 1285--1302, 2013.
  4. M. Maggioni and S. Gerber, "Multiscale dictionaries, transforms, and learning in high-dimensions," in SPIE Optical Engineering+ Applications, 2013, pp. 88581T--88581T.
  5. K. Potter, S. Gerber, and E. W. Anderson, "Visualization of uncertainty without a mean," IEEE computer graphics and applications, vol. 33, no. 1, pp. 75--79, 2013.
  6. S. Gerber and K. Potter, "Data analysis with the morse-smale complex: the msr package for r," Journal of Statistical Software, 2012.
  7. P. Zhu, S. Awate, S. Gerber, and R. Whitaker, "Fast shape-based nearest-neighbor search for brain mris using hierarchical feature matching," in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 (Lecture Notes in Computer Science), G. Fichtinger, A. Martel, and T. Peters, Eds., Springer Berlin / Heidelberg, 2011, pp. 484-491.
  8. cems: Conditional expectation manifolds, 2011.
  9. msr: Morse-Smale approximation, regression and visualization, 2011.
  10. A. Agarwal, H. Daume, and S. Gerber, "Learning multiple tasks using manifold regularization," in Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS) , 2010.
  11. S. Gerber et al., "Visual exploration of high dimensional scalar functions (IEEE Transactions on Visualization and Computer Graphics)," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1271--1280, 2010.
  12. S. Gerber et al., "Manifold modeling for brain population analysis," Medical Image Analysis, vol. 14, no. 5, pp. 643 - 653, 2010.
  13. S. Gerber et al., "On the manifold structure of the space of brain images," in Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I (MICCAI '09), Berlin, Heidelberg, 2009, pp. 305--312.
  14. S. Gerber et al., "Dimensionality reduction and principal surfaces via kernel map manifolds," in Computer Vision, 2009 IEEE 12th International Conference on, 2009, pp. 529-536.
  15. R. Tao, P. T. Fletcher, S. Gerber, and R. Whitaker, "A variational image-based approach to the correction of susceptibility artifacts in the alignment of diffusion weighted and structural mri," in Information Processing in Medical Imaging (Lecture Notes in Computer Science), J. Prince, D. Pham, and K. Myers, Eds., Springer Berlin / Heidelberg, 2009, pp. 664-675.
  16. M. Fuchs and S. Gerber, "Variational shape detection in microscope images based on joint shape and image feature statistics," Computer Vision and Pattern Recognition Workshop, pp. 1-8, 2008.
  17. S. Gerber et al., "Robust non-linear dimensionality reduction using successive 1-dimensional laplacian eigenmaps," in Proceedings of the 2007 International Conference on Machine Learning (ICML), 2007, pp. 281--288.