Assistant Professor, Electrical and Computer Engineering
Mohammed Alawad is an Assistant Professor of Electrical and Computer Engineering at Wayne State University. He received his Ph.D. degree in Computer Engineering from the University of Central Florida (UCF) in 2016. His doctoral research in the Research Laboratory of Emerging Computing Paradigms at UCF was focused on design and development of novel probabilistic-based deep learning hardware accelerators. Prior to joining Wayne State University, Dr. Alawad was a Research Scientist at the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL). He was the technical lead of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a collaboration program between the DOE and the National Cancer Institute, at ORNL. His research and development in the area of AI for cancer research has been highlighted by the U.S. DOE, Nvidia Developer, and HPCwire. In addition, Dr. Alawad worked as an Adjunct Professor in the College of Innovation at Florida Polytechnic University, and a Joint Faculty Assistant Professor in the Bredesen Center at the University of Tennessee, Knoxville.
Dr. Alawad’s research background and interests are in the areas of Artificial Intelligence (AI) algorithms and systems, energy-efficient and high-performance computing, and privacy-aware and secure AI. Dr. Alawad is a member of editorial board and technical committee for a number of journals and conferences. He has organized and led workshops for international conferences. For his accomplishments and scientific contributions that have made a significant impact within and outside ORNL, Dr. Alawad received the ORNL Supplemental Performance Award – Outstanding Research Scientist, and the Early Career Researcher Award from the Computational Sciences and Engineering Division at ORNL.
- Ph.D. in Computer Engineering, University of Central Florida, 2016
- Emerging Computing Architectures Beyond Von-Neumann, such as Neuromorphic and Probabilistic Computing
- AI Algorithms and Systems
- AI Hardware Accelerators
- Machine Learning/ Deep Learning for Natural Language Processing
- Privacy-Aware and Secure AI
- Energy-Efficient and High-Performance Computing
- Deep Learning Compression and Quantization Algorithms
- Reconfigurable Architectures