Kiwan Maeng

Kiwan Maeng is an Assistant Professor in Computer Science and Engineering, focusing on the intersection of machine learning systems, privacy-preserving techniques, and low-latency computing architectures. His research emphasizes algorithm-system co-design for scalable and secure AI implementations.

• Research Trends: His recent work explores retrieval-augmented generation systems, low-latency diffusion models, privacy-preserving federated learning, and energy-harvesting intermittent computing frameworks. Publications highlight collaborations across machine learning, cryptography, and hardware-software co-design.
• Key Projects: He leads a 3-year NSF SaTC grant (2024-2027) addressing privacy-preserving data embedding for untrusted ML services, and contributes to serverless video analytics frameworks (SVDE) and VR streaming optimization (PIRATE).
• Technical Contributions: His scholarship spans 17 conference contributions and 3 journal articles since 2007, with notable work on memory encryption for edge devices, secure MPC-based inference, and sustainable AI systems. Current research focuses on balancing privacy guarantees with model utility while optimizing for environmental efficiency.