Brian Hu, Ph.D.

Staff R&D Engineer

Computer Vision

Kitware DC
Arlington, VA

Ph.D. in Biomedical Engineering
Johns Hopkins University

B.S. in Biomedical Engineering
University of Pittsburgh

Brian Hu, Ph.D., is a staff 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.


  1. B. Hu, P. Tunison, B. RichardWebster, and A. Hoogs, "Xaitk-Saliency: An Open Source Explainable AI Toolkit for Saliency," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 13, pp. 15760-15766, Jun. 2023. [URL]
  2. J. Lee, Y. Choe, S. Ardid, R. Abbasi-Asl, M. McCarthy, and B. Hu, "Editorial: Functional microcircuits in the brain and in artificial intelligent systems," Frontiers in Computational Neuroscience, vol. 17, pp. 1135507, Jan. 2023. [URL]
  3. D. Voina, S. Recanatesi, B. Hu, E. Shea-Brown, and S. Mihalas, "Single Circuit in V1 Capable of Switching Contexts During Movement Using an Inhibitory Population as a Switch," Neural Computation, vol. 34, no. 3, pp. 541-594, Feb. 2022. [URL]
  4. B. RichardWebster, B. Hu, K. Fieldhouse, and A. Hoogs, "Doppelganger Saliency: Towards More Ethical Person Re-Identification," in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022. [URL]
  5. B. Hu, B. Vasu, and A. Hoogs, "X-MIR: EXplainable Medical Image Retrieval," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022. [URL]
  6. 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," PLOS Computational Biology, Sep. 2021. [URL]
  7. B. Hu, P. Tunison, B. Vasu, N. Menon, R. Collins, and A. Hoogs, "XAITK: The explainable AI toolkit," Applied AI Letters, Oct. 2021. [URL]
  8. B. Vasu, B. Hu, B. Dong, R. Collins, and A. Hoogs, "Explainable, interactive content‐based image retrieval," Applied AI Letters, Nov. 2021. [URL]
  9. N. Wagatsuma, B. Hu, R. von der Heydt, and E. Niebur, "Analysis of spiking synchrony in visual cortex reveals distinct types of top-down modulation signals for spatial and object-based attention," PLOS Computational Biology, vol. 17, no. 3, pp. e1008829, Mar. 2021. [URL]
  10. C. Schneider-Mizell, A. Bodor, F. Collman, D. Brittain, A. Bleckert, S. Dorkenwald, N. Turner, T. Macrina, K. Lee, R. Lu, J. Wu, J. Zhuang, A. Nandi, B. Hu, J. Buchanan, M. Takeno, R. Torres, G. Mahalingam, D. Bumbarger, Y. Li, T. Chartrand, N. Kemnitz, W. Silversmith, D. Ih, J. Zung, A. Zlateski, I. Tartavull, S. Popovych, W. Wong, M. Castro, C. Jordan, E. Froudarakis, L. Becker, S. Suckow, J. Reimer, A. Tolias, C. Anastassiou, H. Seung, R. Reid, and N. Costa, "Structure and function of axo-axonic inhibition," eLife, vol. 10, pp. e73783, Dec. 2021. [URL]
  11. 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," Nature, Apr. 2021. [URL]
  12. R. Iyer, B. Hu, and S. Mihalas, "Contextual Integration in Cortical and Convolutional Neural Networks," Frontiers in Computational Neuroscience, vol. 14, pp. 31, Apr. 2020. [URL]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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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