Abdulmajid Murad
Background and activities
I am a first-year Ph.D. Student in IIK at NTNU advised by Frank Kraemer, Kerstin Bach, and Gavin Taylor. I am currently working as part of ROBIOT project (Reinforcement Learning for Intelligent Autonomous IoT). My main research goal is to develop and apply reinforcement learning algorithms that enable IoT devices to operate autonomously in the real world.
Previously, I graduated in 2018 with M.Eng in Information and Communication Engineering from Chosun University, and a B.S. in Electrical Engineering from Qassim University
PUBLICATIONS:
- A. Murad, F. A. Kraemer, K. Bach and G. Taylor, "Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning," 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Umea, Sweden, 2019, pp. 43-51.
- Murad, A., Bach, K., Kraemer, F. A., & Taylor, G. (2019, October). IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning. In Proceedings of the 9th International Conference on the Internet of Things (p. 37). ACM.
- Murad, A.,Pyun, J. "Deep recurrent neural networks for human activity recognition." Sensors 17.11 (2017): 2556.
- Irfan, M., Esmaeil, K., Murad, A. , & Almufadia, F. "Benchmarking Study for Energy Footprint of Water Bottling in Saudi Arabia. Energy Engineering." 115(3) (2018): 23-34.
Scientific, academic and artistic work
Part of book/report
- (2019) Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning. 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO).
- (2019) IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning. 9th International Conference on the Internet of Things (IoT 2019), October 22--25, 2019, Bilbao, Spain.
Others
- (2019) IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning (Poster). IoT 20019 ; 2019-10-22 - 2019-10-25.