Academic Profile : Faculty
Assoc Prof Mohamed M. Sabry
Associate Professor, College of Computing & Data Science
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Mohamed M. Sabry obtained his Ph.D. from EPFL in 2013 and was a postdoc in Stanford University from 2014 till 2017. His primary research interests are system-level design and optimization and resource management of computing systems by leveraging the benefits of emerging technologies (for computing, storage, interconnect, and cooling) to boost computing performance and energy efficiency beyond what is achievable today from large-scale cloud to small-scale embedded domains. This includes device-, circuit-, and architecture modeling, design-space exploration and hardware-software co-optimization.
His work on Nanoengineered Computing System Technology (N3XT), under the supervision of Prof. Subhasish Mitra and Prof. H.-S. Philip Wong has received worldwide recognition in various news press. It shows the prospects of monolithic 3D computing systems enabled by emerging nanotechnologies, such as carbon nanotubes and resistive memories.
He has been awarded the Swiss National Science Foundation Early Post-Doctoral Mobility Fellowship in 2013.
His work on Nanoengineered Computing System Technology (N3XT), under the supervision of Prof. Subhasish Mitra and Prof. H.-S. Philip Wong has received worldwide recognition in various news press. It shows the prospects of monolithic 3D computing systems enabled by emerging nanotechnologies, such as carbon nanotubes and resistive memories.
He has been awarded the Swiss National Science Foundation Early Post-Doctoral Mobility Fellowship in 2013.
- Computer architecture
- AI-based systems design and optimization
- Emerging nanotechnologies
- System-level design and optimization
- Power and thermal management
- Simulation of computing systems
- IoT design and optimization
- AI-based systems design and optimization
- Emerging nanotechnologies
- System-level design and optimization
- Power and thermal management
- Simulation of computing systems
- IoT design and optimization
- Artificial Neural Networks for Frequency-Voltage Prediction Using On-Die Measurements
- Memristive Halide Perovskites for Next Generation Embedded Neuromorphic Computing
- Spin Orbit Coupling based IntelligencE TechnologY (SOCIETY)