Brian Hu, Ph.D.

Senior R&D Engineer

Brian Hu, Ph.D., is a senior R&D engineer on Kitware’s Computer Vision (CV) Team located in Arlington, Virginia. He provides research direction and guidance in the areas of explainable AI (XAI), human-machine teaming, image retrieval, and object detection. In support of the CV Team, Brian is actively involved in developing new models and algorithms, running experiments, and writing papers.

On the Defense Advanced Research Projects Agency (DARPA) XAI program, Brian developed novel saliency algorithms for image retrieval and anomaly detection and applied them to the medical imaging domain. He was also involved in the United States Special Operations Command (SOCOM) XQHM project where he helped design user studies to improve Kitware’s interactive query refinement platforms. For the Air Force Research Laboratory (AFRL) DIYAI effort, Brian is working on improving small object detection by incorporating spatial context and motion cues.

Prior to joining Kitware, Brian was a research scientist at the Allen Institute for Brain Science where he worked on data analysis and computational modeling of neural and behavioral data, biologically-plausible neural networks, and short-term memory mechanisms. His work in the neuroscience domain has been featured in several journals and at conferences such as the IEEE Conference on Information Sciences and Systems (CISS).

Brian received his Ph.D. in biomedical engineering from Johns Hopkins University. He received his bachelor’s degree in biomedical engineering from the University of Pittsburgh.


Ph.D. in biomedical engineering from Johns Hopkins University

B.S. in biomedical engineering from University of Pittsburgh

Get to Know Brian

What is your favorite thing about working at Kitware? Coming from academia, I enjoy the independence and flexibility in research that exists among the different projects at Kitware. While I was initially worried that this would be harder to find in the industry setting, I have been pleasantly surprised by opportunities to be creative in my approaches to solve different problems.

What do you love most about what you do? Kitware says its people are its product, which I believe is true. I enjoy working collaboratively in teams built around people with different expertise to solve challenging yet essential problems together, with the ability to learn and grow daily.

Share something interesting about yourself that is not on your resume. I’m a proud father of two young children, Katherine and Jacob, who keep me busy outside of work. I became interested in studying vision (both biological and computer vision) due to my younger sister Joy, who is blind and continues to inspire me.



Brian’s publication list is below. To see all of Kitware’s computer vision publications, please visit the Computer Vision Publications page.

  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]

Bibliography generated 2020-04-04-04:02:58 (3617)