Artificial intelligence models are expensive to run and drain huge amounts of energy, which creates challenges for small businesses, universities, and others than want to benefit from them. UCF’s Jun Wang is tackling barriers to efficiency and speed, information bottlenecks, and scheduling inefficiencies of AI protocols in hopes of helping to speed start times and communication with the GPUs that help AI models run.

“Think of a running AI model like a factory,” says Wang. “You have different teams, machines, workers and logistics, and all of them have to be coordinated. So in AI, those teams are the software algorithm and the hardware components like the GPUs and the operating systems. It just means coordinating across all of them and designing the algorithm and the system together so they work in harmony from the start.”

• Jun Wang