Payman's research centers on high-performance, low-latency, and energy-efficient design at the intersection of machine learning and systems. Currently, he is working on efficient resource management for various AI models/workloads such as DNNs, GNNs, and Transformers on resource-constrained devices. His research has been published at several top-tier conferences/journals such as MLSys, ISCA, ECCV, DAC, FPGA, IEEE Micro, and TC. His work has been recognized with several awards such as the Qualcomm Innovation Fellowship (QIF’23), Featured Paper of IEEE Transaction on Computers (TC’20), Les Palukti Best Student Poster Paper Award at GOMACTech’23, Best Paper Candidate of Design Automation and Test in Europe (DATE'21), Best Poster and Young Fellow Award in Design Automation Conference (DAC'20).
Payman Behnam is a Ph.D. student at Georgia Institute of Technology. He has obtained three master's in Electrical/Computer engineering, Computer Science, and Computer Engineering from Georgia Institute of Technology, the University of Utah, and the University of Tehran. He has a bachelor's in Computer Engineering from Shiraz University. His professional experience includes internships at prominent companies such as Western Digital, Cisco Research, and NVIDIA.