I am a PhD candidate in the school of Electrical and Computer Engineering at Georgia Tech advised by Dr. Alexandros Daglis, Associate Professor, School of Computer Science.

My research is focused on developing tailored memory system architectures to enhance the performance of parallel, scientific, and deep learning AI/ML workloads in resource constrained and memory bandwidth-intensive environments. My research draws on insights from computer architecture, memory system design, distributed AI/ML systems, and CXL technologies.

Prior to joining PhD, I did my Masters in Electrical Engineering from Arizona State University, Tempe and worked full-time as Sr. Applications Engineer at Cadence Design Systems, San Jose, CA. For more details please refer to my CV.

Thesis Topic: Memory system optimizations for parallel and bandwidth-intensive applications

The growing performance and bandwidth demands of modern datacenter and HPC workloads are driving innovation in memory system design. My research adopts a holistic approach to optimizing memory systems by jointly considering workload-specific characteristics and underlying hardware capabilities. These innovations demonstrate how tailored memory system designs can substantially enhance the performance of parallel, scientific, and deep learning AI applications in resource-constrained and bandwidth-intensive environments.