Rate My Professor Michal Aibin

Aibin's research centers on artificial intelligence, machine learning, deep learning, optimization, computer vision, and their applications to unmanned aerial vehicles (UAV/RPAS), wildfire detection, remote sensing, eHealth, optical networks, traffic prediction, and disaster-resilient communication networks. His projects include real-time image detection for search and rescue using drones, wildfire monitoring with AI, brain tumor detection from MRI images, and contributions to international efforts like the Canadian Hyper-K neutrino experiment. Notable publications encompass "Guide to disaster-resilient communication networks" (2020, Springer Nature), "A survey on traffic prediction techniques using artificial intelligence for communication networks" (2021, Telecom), "An overview of security challenges in communication networks" (2016, RNDM), "Adaptive modulation and regenerator-aware dynamic routing algorithm in elastic optical networks" (2015, IEEE ICC), "Traffic prediction based on machine learning for elastic optical networks" (2018, Optical Switching and Networking), and "Traffic Prediction in Optical Networks using Graph Convolutional Generative Adversarial Networks" (2020, ICTON). With over 1,000 citations across his work, Aibin has advanced resilient network design and AI applications in real-world scenarios. He earned the BCIT Employee Excellence Award in June 2018 for applied research on brain tumor detection software and leads a special issue in the journal Remote Sensing.