AI in EE
AI IN DIVISIONS
a key thrust in EE research
AI/machine learning efforts are already a big part of ongoing research in all 6 divisions -
Computer, Communication, Signal, Wave, Circuit and Device - of KAIST EE. Examples include
neuromorphic devices, VLSI hardware architecture tailored to machine learning, image/voice recognition
via deep learning, statistical inference, coding and information theory to enhance distributed machine
learning, intelligent robots, quantum information, brain imaging, etc.
When massive data is generated in the Internet of Things (IoT), it must be sent, stored and processed. Deep learning requires huge quantities of data, which often exist across the network in a highly distributed fashion. Deep learning also frequently utilizes distributed computing resources in carrying out desired tasks. In sum, distributed data processing and machine learning are essential in the era of IoT, big data, and connected AI. In efficiently managing data and computing resources across the network, communications must be at the core of successful technology innovation. The AI and machine learning related work in Communication Division of KAIST EE covers big data analysis and processing over networks, neural networks and learning systems and communication system design based on machine learning.
See below for specific ongoing research topics related to AI and machine learning within the Communication Division of KAIST EE.