Behnam Kazemivash

Georgia State University, Computer Science Ph.D. Candidate

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1806C, Trends center

55 Park Pl NE

Atlanta, GA 30303

I am a graduate student at the Computer Science department of Georgia State University. Currently, I am working as a Graduate Research Assistant at the prestigious Center for Translational Research in Neuroimaging and Data Science (TReNDS), a collaborative research hub involving Georgia Tech, Georgia State, and Emory University. Under the supervision of Dr. Vince Calhoun.

My academic journey is centered around the interesting realm of Computer Vision, with a specific focus on its application in the intricate field of neuroimaging. Precisely, my intellectual curiosity is drawn to the intricate domains of dense prediction, spatiotemporal dynamics, and multi-task training to make meaningful contributions to both the scientific community and society as a whole.

latest news

Aug 19 2024 Our work titled as “Spatiotemporal Vision Transformer for Weakly Supervised Dense Prediction of Dynamic Brain Maps” is accepted by British Machine Vision Conference (BMVC 2024).
Jul 19 2024 Our work titled as “Scepter: Weakly Supervised Framework for Spatiotemporal Dense Prediction of 4D Dynamic Brain Networks” is accepted by EMBC 2024.
Feb 1 2023 Our work on characterization of dynamic brain maps is accepted by Frontiers in Neuroimaging journal.

Experience

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Georgia State University

Aug 2019 - present
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TReNDS

Aug 2019 - present

selected publications

2023

  1. BaseModel.jpg
    A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia
    Behnam Kazemivash, Theo GM Erp, Peter Kochunov, and 1 more author
    Frontiers in Neuroimaging, 2023

2022

  1. EMBC
    A 5D approach to study spatio-temporal dynamism of resting-state brain networks in schizophrenia
    B Kazemivash, and VD Calhoun
    In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022
  2. Basediagram.jpg
    A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning
    Behnam Kazemivash, and Vince D Calhoun
    Journal of neuroscience methods, 2022

2020

  1. BIBE
    BPARC: A novel spatio-temporal (4D) data-driven brain parcellation scheme based on deep residual networks
    Behnam Kazemivash, and Vince D Calhoun
    In 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), 2020

2018

  1. SC
    A predictive model-based image watermarking scheme using regression tree and firefly algorithm
    Behnam Kazemivash, and Mohsen Ebrahimi Moghaddam
    Soft Computing, 2018

2017

  1. MTA
    A robust digital image watermarking technique using lifting wavelet transform and firefly algorithm
    Behnam Kazemivash, and Mohsen Ebrahimi Moghaddam
    Multimedia Tools and Applications, 2017