Designing Efficient, Intelligent, and Adaptive AI Systems

My research focuses on designing resource-efficient, intelligent, and reliable systems that span the cloud-to-edge continuum and built environments. My primary methodology is to leverage the flexibility of computing workloads to design efficient and dynamic systems that adapt to the real world's dynamics. By integrating insights from systems, machine learning, theory, and optimization, my work aims to reimagine and establish foundational design principles to build efficient, agile, and reliable systems. Utilizing this methodology enabled me to make fundamental contributions to system design and resource management algorithms, optimizing the carbon emissions of AI training and inference workloads, as well as AI servicing systems for resource-constrained edge environments.