The Holcombe Department of Electrical and Computer Engineering is pleased to announce our newest faculty hires, Dr. Xiaolong Ma and Dr. Lianfeng Zhao.
Dr. Xiaolong Ma received his Ph.D. in Computer Engineering from Northeastern University in May 2022, his M.S. in Electrical & Computer Engineering from Syracuse University in May 2016, and his B.E. in Communication Engineering from Yanshan University in China in June 2014. Prior to Clemson, he was a Ph. D. candidate/Research Associate in the Department of Electrical and Computer Engineering at Northeastern University.
He was the winner of the Contributed Article of CACM in 2021, for his contribution on the research of model compression for real-time inference on mobile devices. His highly efficient dynamic sparse training framework won the Best Paper Award in ICLR workshop of Hardware Aware Efficient Training (HAET), and also received the Spotlight Paper Award in NeurIPS in 2021. His work on efficient machine learning with stochastic number generator was nominated for the Best Paper Award in ISQED 2017.
Dr. Ma has broad research interests spanning from the theory to the application aspects of machine learning (ML). Most recently, he studies deep neural networks (DNNs) model compression with sparsity and low-bit quantization, efficient deep learning algorithms for training/inference acceleration, mobile/edge device computation, optimization, as well as their applications in various computer vision tasks.
Dr. Lianfeng Zhao received his Ph.D. in Electrical Engineering from Princeton University in 2019, M.S. from Tsinghua University in 2014, and B.E. from Xidian University in 2012. Prior to Clemson, he was a Postdoctoral Research Associate at Princeton University. His research focuses on optoelectronic thin-film materials, devices, and applications. His work has been recognized by a number of awards including the Princeton Wallace Memorial Honorific Fellowship and the Princeton School of Engineering and Applied Science Award for Excellence.
Dr. Zhao’s research group aims to exploit emerging thin-film materials and device concepts to develop the next-generation electronic and photonic devices and systems with the capacity to offer game-changing technologies in lighting, display, communication, computing, sensing, and renewable energy. Current focus areas include the use of metal halide perovskites, organic semiconductors, as well as nanostructured quantized matter for emerging applications in solar cells, LEDs, lasers, transistors, memory devices, and neuromorphic computing.