معرفی
In Kee Kim is an Associate Professor in the School of Computing at the University of Georgia, holding a Ph.D. in Computer Science from the University of Virginia (2018). His research focuses on performance and resource management problems in diverse computing systems, including cloud, high-performance computing (HPC), edge, and IoT environments. Funded by agencies like NSF, DoD, and Army Research Labs, his work bridges theoretical innovation with practical system optimization.
- Education: Ph.D. in Computer Science (University of Virginia, 2018)
- University: University of Georgia
- School: School of Computing
- Department: Department of Computer Science
Current research directions include:
- Edge AI: System-level optimization for AI inference, training, and compression at the edge
- Serverless Workflow Management: Cost-performance optimization in edge-cloud collaboration
- Reproducible Benchmarking: Workload characterization across cloud, HPC, edge, and IoT systems
Recent publications highlight advancements in:
- Edge device scheduling for heterogeneous AI accelerators
- Model compression techniques for constrained environments
- Energy scheduling for environmental sensors
- Serverless computing benchmarking
Scientific Awards:
- Best Paper Award at IEEE EDGE 2024 for 'Characterizing Deep Learning Model Compression...'
His Adaptive Computing & Edge Intelligence (ACE) Lab at UGA supports student research through facilities in Boyd Graduate Studies Research Center (Rooms 817 and 819). Current and former advisees have contributed to publications at top venues including IEEE EDGE, CLOUD, and ACM Transactions.
