Brian Hu

Brian Hu

Senior R&D Engineer

Brian Hu is a researcher on the computer vision team at Kitware. Prior to joining Kitware, he was a research scientist at the Allen Institute for Brain Science. There, he was responsible for developing computational models of visual perception with applications to computer vision. Brian obtained his Ph.D. in biomedical engineering from Johns Hopkins University under the guidance of Ernst Niebur. His focus was on modeling the neural circuits that group visual features into 2D and 3D object representations. As a computational neuroscientist, Brian has worked on models of perceptual organization, selective attention, and short-term memory. 

For more information, please visit his personal web page: https://brianhhu.github.io.

  1. B. Hu, M. Garrett, P. Groblewski, D. Ollerenshaw, J. Shang, K. Roll, S. Manavi, C. Koch, S. Olsen, and S. Mihalas, "Adaptation supports short-term memory in a visual change detection task," Neuroscience, Mar. 2020. [URL]
  2. J. Siegle, X. Jia, S. Durand, S. Gale, C. Bennett, N. Graddis, G. Heller, T. Ramirez, H. Choi, J. Luviano, P. Groblewski, R. Ahmed, A. Arkhipov, A. Bernard, Y. Billeh, D. Brown, M. Buice, N. Cain, S. Caldejon, L. Casal, A. Cho, M. Chvilicek, T. Cox, K. Dai, D. Denman, S. de Vries, R. Dietzman, L. Esposito, C. Farrell, D. Feng, J. Galbraith, M. Garrett, E. Gelfand, N. Hancock, J. Harris, R. Howard, B. Hu, R. Hytnen, R. Iyer, E. Jessett, K. Johnson, I. Kato, J. Kiggins, S. Lambert, J. Lecoq, P. Ledochowitsch, J. Lee, A. Leon, Y. Li, E. Liang, F. Long, K. Mace, J. Melchior, D. Millman, T. Mollenkopf, C. Nayan, L. Ng, K. Ngo, T. Nguyen, P. Nicovich, K. North, G. Ocker, D. Ollerenshaw, M. Oliver, M. Pachitariu, J. Perkins, M. Reding, D. Reid, M. Robertson, K. Ronellenfitch, S. Seid, C. Slaughterbeck, M. Stoecklin, D. Sullivan, B. Sutton, J. Swapp, C. Thompson, K. Turner, W. Wakeman, J. Whitesell, D. Williams, A. Williford, R. Young, H. Zeng, S. Naylor, J. Phillips, R. Reid, S. Mihalas, S. Olsen, and C. Koch, "A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas," Neuroscience, Oct. 2019. [URL]
  3. B. Hu, R. von der Heydt, and E. Niebur, "Figure-Ground Organization in Natural Scenes: Performance of a Recurrent Neural Model Compared with Neurons of Area V2," eneuro, vol. 6, no. 3, pp. ENEURO.0479-18.2019, May 2019. [URL]
  4. B. Hu, S. Khan, E. Niebur, and B. Tripp, "Figure-ground representation in deep neural networks," in 2019 53rd Annual Conference on Information Sciences and Systems (CISS), 2019. [URL]
  5. B. Hu, R. Iyer, and S. Mihalas, "Convolutional neural networks with extra-classical receptive fields," in Thirty-third Conference on Neural Information Processing Systems, 2019. [URL]
  6. B. Hu, J. Shang, R. Iyer, J. Siegle, and S. Mihalas, "Does the neuronal noise in cortex help generalization?," in Thirty-third Conference on Neural Information Processing Systems, 2019. [URL]
  7. B. Hu, I. Johnson-Bey, M. Sharma, and E. Niebur, "Head movements are correlated with other measures of visual attention at smaller spatial scales," in 2018 52nd Annual Conference on Information Sciences and Systems (CISS), 2018. [URL]
  8. B. Hu, I. Johnson-Bey, M. Sharma, and E. Niebur, "Head movements during visual exploration of natural images in virtual reality," in 2017 51st Annual Conference on Information Sciences and Systems (CISS), 2017. [URL]
  9. B. Hu and E. Niebur, "A recurrent neural model for proto-object based contour integration and figure-ground segregation," Journal of Computational Neuroscience, vol. 43, no. 3, pp. 227-242, Dec. 2017. [URL]
  10. B. Hu, R. Kane-Jackson, and E. Niebur, "A proto-object based saliency model in three-dimensional space," Vision Research, vol. 119, pp. 42-49, Feb. 2016. [URL]
  11. B. Hu, R. von der Heydt, and E. Niebur, "A neural model for perceptual organization of 3D surfaces," in 2015 49th Annual Conference on Information Sciences and Systems (CISS), 2015. [URL]

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