Researcher

My Expertise

Biomedical Image Computing, Computer Vision, Pattern Recognition 

Keywords

Fields of Research (FoR)

Biomedical imaging, Computer vision

Biography

Md Mamunur Rahaman is currently pursuing his PhD in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney. His research focuses on the development of novel image processing methods, algorithms, and software systems for computer-assisted diagnostics, with a specific focus on medical imaging and microscopic images. He is interested in using sophisticated computational algorithms to extract information...view more

Md Mamunur Rahaman is currently pursuing his PhD in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney. His research focuses on the development of novel image processing methods, algorithms, and software systems for computer-assisted diagnostics, with a specific focus on medical imaging and microscopic images. He is interested in using sophisticated computational algorithms to extract information and aid in medical intervention.

In 2017, he graduated with a Bachelor of Science in Electrical and Electronics Engineering from BRAC University in Dhaka, Bangladesh. In 2021, he received a Master of Engineering in Biomedical Engineering from Northeastern University in China. At Northeastern University, Mamunur served as a research assistant (RA) from 2021 to January 2022, contributing to the Microscopic Image and Medical Image Analysis Group in the College of Medicine and Bioinformatics Engineering.

In addition to his doctoral research, Mamunur serves as a tutor for various courses at UNSW, including computer vision (COMP9517), neural networks (COMP9444), and artificial intelligence (COMP3411). 


My Grants

 


My Qualifications

 


My Awards

 


My Research Activities

 


My Engagement

  • Professional Memberships

    • Professional Member, Association for Computing Machinery (ACM), 2021 - Present
    • IEEE Graduate Student Member, Region 10 (Asia and Pacific), New South Wales Section, 2024 - Present
    • Member, IEEE Engineering in Medicine and Biology Society, 2024 - Present
    • Member, IEEE Young Professionals, 2024 - Present
  • Editorial Board Memberships

    • Executive Editor, AI Medicine Journal, Scilight Press, 2024 – Present
    • Review Editor for Machine Learning and Artificial Intelligence:
      • Frontiers in Big Data, 2022 – Present
      • Frontiers in Artificial Intelligence, 2022 – Present
    • Review Editor for Image Retrieval:
      • Frontiers in Imaging, 2022 – Present
  • Reviewer:

    • IEEE Transactions on Medical Imaging, 2022 - Present
    • Applied Artificial Intelligence, 2022 - Present
    • IEEE Journal of Biomedical and Health Informatics, 2022 - Present
    • Interdisciplinary Sciences: Computational Life Sciences, 2021 - Present
    • Biomedical Signal Processing and Control (Elsevier), 2022 - Present
    • BMC Cancer (Springer Nature), 2022 - Present
    • Informatics in Medicine Unlocked (Elsevier), 2022 - Present
    • BMC Medical Imaging (Springer Nature), 2021 - Present
    • Scientific Reports (Nature), 2021 - Present
    • Cancers (MDPI), 2022 - Present
    • Applied Artificial Intelligence (Taylor & Francis), 2021 - Present
    • Computers in Biology and Medicine (Elsevier), 2022 - Present
    • IEEE Access, 2020 – Present
    • Diagnostics (MDPI), 2022 - Present
    • Expert Systems with Applications, 2022 - Present
    • Heliyon, 2022 – Present
    • Artificial Intelligence Review, 2021 - Present
    • Journal of Personalized Medicine (MDPI), 2022 - Present
    • Journal of Big Data, 2021 - Present
    • Sensors (MDPI), 2022 - Present
    • Technology in Cancer Research & Treatment, 2021 - Present
    • World Journal of Surgical Oncology (Springer Nature), 2021 - Present


My Teaching

I have been actively involved in tutoring and mentoring students across several key courses within the School of Computer Science and Engineering. My teaching philosophy is centered on fostering a deep understanding of core concepts, encouraging practical application, and inspiring innovation among students.

Courses Tutored:

  1. Computer Vision (COMP9517)

  2. Neural Networks and Deep Learning (COMP9444)

  3. Artificial Intelligence (COMP3411)

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