Higher Degree by Research Candidate
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences
PhD researcher at the Australian Institute for Machine Learning (AIML)
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 Competency English Can read, write, speak, understand spoken and peer review Persian Can read, write, speak, understand spoken and peer review
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
Year Citation — Moghaddam, M. K., Abbasnejad, E., Wu, Q., Shi, J., & Hengel, A. V. D. (n.d.). Learning for Visual Navigation by Imagining the Success.
Year Citation — Moghaddam, M. M. K., Abbasnejad, E., & Shi, J. (n.d.). Follow the Attention: Combining Partial Pose and Object Motion for
Fine-Grained Action Detection.
— Moghaddam, M. K., Wu, Q., Abbasnejad, E., & Shi, J. Q. (n.d.). Optimistic Agent: Accurate Graph-Based Value Estimation for More
Successful Visual Navigation.
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
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