About Me

I am a postdoctoral researcher at the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT), where I collaborate with Dr. Xavier Boix and Prof. Pawan Sinha. My research efforts are currently directed towards comprehending the computational principles that underlie human behavior, and devising machine vision algorithms for practical applications by translating this knowledge. Kindly refer to my Publications and CV for further details.

Education and Academic Appointments

  • Mar. 2008 - Aug. 2014, B.Sc. in Comptuer Science
  • Korea University, South Korea, Advisor: Prof. Hee-Jo Lee
  • Sep. 2014 - Aug. 2016, M.E. in Brain and Cognitive Engineering
  • Korea University, South Korea, Advisor: Prof. Jong-Hwan Lee
  • Sep. 2016 - Aug. 2021, Ph.D. in Psychology
  • Vanderbilt University, United States, Advisor: Prof. Frank Tong
  • Sep. 2021 - Aug. 2022, Postdoctoral Researcher in Psychology
  • Vanderbilt University, United States, Advisor: Prof. Frank Tong
  • Sep. 2022 - Present, Postdoctoral Researcher in Brain and Cognitive Sciences
  • MIT, United States, Advisors: Prof. Pawan Sinha & Dr. Xavier Boix


2022. 08. 15. Moved to Boston and started a new position at the MIT Department of Brain and Cognitive Sciences!

2023. 05. 19-24. Attended Vision Sciences Society 2023 in Florida, United States!

2023. 07. 31. Our paper "Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks" is now available on bioRxiv here!

2023. 08. 04. Our paper "Robustness to Transformations Across Categories: Is Robustness Driven by Invariant Neural Representations?" has been accepted for publication in Neural Computation!

  • All
  • Life
  • Work

Research Interests

At the center of my academic journey has been a deep curiosity to puzzle out the intricate algorithms that govern human visual perception. Back in 1999, the movie “The Matrix,” fueled me with a spark of inspiration, and this became my professional and intellectual aspiration to study Computational Neuroscience. My research objectives are twofold: firstly, to investigate the computational principles and neural mechanisms in the brain that facilitate stable representations of the visual environment; and secondly, to leverage insights from Psychology and Neuroscience literature to develop a more advanced machine vision model, with the ultimate aim of delivering practical value in real-world applications.

Presently, my research agenda is focused on the comparative robustness of human versus machine behaviors, emphasizing three distinct facets: 1) recurrent processing, which aims to elucidate the neural recurrent dynamics beyond traditional feedforward mechanisms that allow for robust computational processes, 3) biologically plausible machine models, which aims to explore machine models that not only align with biological systems but also offer practical benefits for real-world applications, and 3) visual understanding, which aims to explore the complexities of higher-order cognitive and reasoning abilities, extending beyond basic perception and recognition processes.


    Jang, H., & Boix, X. (in prep). Robust visual recognition in varied viewing conditions with neural networks: The role of configural features.
    Arslan, S.*, Fux, M.*, Jang, H.*, Cooper A., Groth M., & Sinha, P. (in prep). Comparing humans and deep neural networks on face recognition under various distance and rotation viewing conditions. (*Joint First Author).
    Jang, H., & Tong, F. (in prep). Comparing the learning abilities of humans and neural networks at recognizing degraded objects at the threshold of visibility.
    Jang, H., & Tong, F. (in prep). Convolutional neural networks optimized for face recognition reveal a computational basis for holistic face processing.
    Jang, H., & Tong, F. (2023). Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks. bioRxiv, 2023-07.
    Jang, H.*, Zaidi, S. S. A.*, Boix, X.*, Prasad, N.*, Gilad-Gutnick, S., Ben-Ami, S., & Sinha, P. (in press). Robustness to Transformations Across Categories: Is Robustness To Transformations Driven by Invariant Neural Representations?. Neural Computation. (*Joint First Author).
    Jang, H., & Tong, F. (2021). Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing. Journal of vision, 21(12), 6-6.
    Jang, H., McCormack, D., & Tong, F. (2021). Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images. PLoS biology, 19(12), e3001418.
    Kim, H. C.*, Jang, H.*, & Lee, J. H. (2020). Test–retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network. Journal of Neuroscience Methods, 330, 108451. (*Joint First Author).
    Rane, S., Jolly, E., Park, A., Jang, H., & Craddock, C. (2017). Developing predictive imaging biomarkers using whole-brain classifiers: Application to the ABIDE I dataset. Research Ideas and Outcomes, 3, e12733.
    Jang, H., Plis, S. M., Calhoun, V. D., & Lee, J. H. (2017). Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks. Neuroimage, 145, 314-328.


If you have any questions or inquires, please email me at the address below.


Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139