I am a third-year Ph.D. Student in IIK at NTNU advised by Frank Alexander 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, South Korea and a B.S. in Electrical Engineering from Qassim University, Saudi Arabia.

PUBLICATIONS:

2021:

• Murad, A.; Kraemer, F.A.; Bach, K.; Taylor, G.; Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting. Sensors 21(23), November 2021.
• Klemsdal, E.; Herland, S.; Murad, A.; "Learning Task Agnostic Skills with Data-driven Guidance". ICML 2021 workshop on Unsupervised Reinforcement Learning, July 18--24, 2021, Vienna, Austria.

• Murad, A.; Kraemer, F.A.; Bach, K.; Taylor, G.; Information-Driven Adaptive Sensing based on Deep Reinforcement Learning. Proceeding of the 10th International Conference on the Internet of Things, October 4--9, 2020, Malmö, Sweden.

2019:

• Murad, A.; Kraemer, F.A.; Bach, K.; Taylor, G.; Autonomous Management of Energy-Harvesting IoT Nodes using Deep Reinforcement Learning. Proceeding of the IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), June 16--20, 2019, Umeå, Sweden.
• Murad, A.; Kraemer, F.A.; Bach, K.; Taylor, G.; IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning. Proceeding of the 9th International Conference on the Internet of Things, October 22--25, 2019, Bilbao, Spain.

2017:

• Murad, A.; Pyun, J.; Deep Recurrent Neural Networks for Human Activity Recognition. Sensors 17(11), November 2017.