Mahdi Kazemi Moghaddam

Mahdi Kazemi Moghaddam

Higher Degree by Research Candidate

HDR Student

School of Computer Science

Faculty of Sciences, Engineering and Technology


I am a PhD researcher at the Australian Institute for Machine Learning (AIML). I work at the intersection of Reinforcement Learning (RL) and robotics. Please see my personal page for more information: https://mahdi-kazemi-m.github.io/

My research is at the intersection of computer vision and machine learning. I'm, particularly, interested in reinforcement learning and the ways it can be utilised to perform goal-oriented decision making tasks.

  • Awards and Achievements

    Date Type Title Institution Name Country Amount
    2019 Award Valedictorian of class 2019 The University of Adelaide Australia
    2019 Award Govehack 2019 Award Govhack Australia
    2019 Award Golden Prize at SAIC VW Logistics Innovation Day Volkswagen China
    2019 Scholarship Adelaide Graduate Research Scholarship The University of Adelaide Australia
    2018 Scholarship Adelaide Summer Research Scholarship The University of Adelaide Australia
    2018 Scholarship Higher Education Scholarship (Honours degree) The University of Adelaide Australia
  • Language Competencies

    Language Competency
    English Can read, write, speak, understand spoken and peer review
    Persian Can read, write, speak, understand spoken and peer review
  • Education

    Date Institution name Country Title
    2018 - 2019 The University of Adelaide Australia Honours Degree of Computer Science (First Class Honours)
    2015 Amirkabir University of Technology Iran, Islamic Republic of Bachelor of Electrical Engineering, Electronics
  • Research Interests

  • Journals

    Year Citation
    2022 Ghiasi, A., Moghaddam, M. K., Ng, C. T., Sheikh, A. H., & Shi, J. Q. (2022). Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network. Engineering Structures, 264, 114474.
    DOI
    Moghaddam, M. K., Abbasnejad, E., Wu, Q., Shi, J., & Hengel, A. V. D. (n.d.). Learning for Visual Navigation by Imagining the Success.
  • Conference Papers

    Year Citation
    2022 Kazemi Moghaddam, M., Abbasnejad, E., Wu, Q., Qinfeng Shi, J., & Van Den Hengel, A. (2022). ForeSI: Success-Aware Visual Navigation Agent. In Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 (pp. 3401-3410). IEEE.
    DOI
    2021 Kazemi Moghaddam, M., Wu, Q., Abbasnejad, E., & Shi, J. (2021). Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation. In 2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021 (pp. 3732-3741). online: IEEE.
    DOI Scopus3 WoS2
    Moghaddam, M. M. K., Abbasnejad, E., & Shi, J. (n.d.). Follow the Attention: Combining Partial Pose and Object Motion for
    Fine-Grained Action Detection.

Master of Data Science Research Project, Semester 1, 2021

Mining Big Data, Semester 1, 2021

Master of Data Science Research Project, Semester 2, 2020

Deep Learning Fundamentals, Semester 2, 2020

Applied Machine Learning, Semester 2, 2020

  • Memberships

    Date Role Membership Country
    2018 - 2019 Member Australian Computer Society Australia
  • Position: HDR Student
  • Email: mahdi.kazemimoghaddam@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor 1
  • Org Unit: Australian Institute for Machine Learning - Operations

Connect With Me
External Profiles