Rate My Professor Melissa Smith

Melissa C. Smith is a Professor of Electrical and Computer Engineering at Clemson University and serves as Associate Dean for Graduate Studies in the College of Engineering, Computing and Applied Sciences. She holds a Ph.D. in Electrical and Computer Engineering from the University of Tennessee, an M.S. in Electrical Engineering from Florida State University, and a B.S. in Electrical Engineering from Florida State University. Before joining Clemson University in 2006, she worked for 12 years as a research associate at Oak Ridge National Laboratory, where her research spanned high-energy and nuclear physics instrumentation including the Spallation Neutron Source and PHENIX experiment at Brookhaven National Laboratory, sub-micron CMOS circuit design, fault-tolerant sensor networks, software-defined radio, machine learning, and high-performance and reconfigurable computing for real-time systems and scientific computation. At Clemson, she leads the Future Computing Technologies Lab, a research group dedicated to high-performance computing and computer architecture.

Smith's research specializations include machine learning, deep learning, artificial intelligence, reconfigurable computing, GPUs, high-performance computing, embedded computing, and system performance modeling and analysis. Her current focus is on performance analysis and optimization using emerging heterogeneous computing architectures such as GPGPU- and FPGA-based systems for applications in machine learning, high-performance or real-time embedded systems, image processing, autonomous off-road perception, and data compression techniques. With over 25 years of experience developing and implementing scientific workloads and machine learning applications across multiple domains, she maintains collaborations with researchers at Oak Ridge National Laboratory and institutions nationwide in areas like heterogeneous high-performance computing, machine learning and AI, system performance modeling, and high-speed data acquisition systems. Key publications include 'PHENIX detector overview' (2003), 'PHENIX calorimeter' (2003), 'PHENIX central arm tracking detectors' (2003), 'FPGA implementation of Izhikevich spiking neural networks for character recognition' (2009), 'Using FPGA devices to accelerate biomolecular simulations' (2007), 'A generalized deep learning approach for local structure identification in molecular simulations' (2019), 'Real-Time Inference for Unmanned Ground Vehicles Using Lossy Compression and Deep Learning' (2025), and 'Speech enhancement using multi-stage self-attentive temporal convolutional networks' (2021). Her interdisciplinary work provides solutions at the application/architecture interface, advancing capabilities in computing-intensive fields.