Assoc. Prof. Dr. Mohammed Farag | Machine Learning Awards | Best Researcher Award

Assoc. Prof. Dr. Mohammed Farag | Machine Learning Awards | Best Researcher AwardΒ 

Assoc. Prof. Dr. Mohammed Farag, Alexandria University, Egypt

Dr. Mohammed M. Farag is an accomplished Associate Professor of Electrical Engineering with extensive academic experience spanning over two decades. Currently affiliated with King Faisal University, Saudi Arabia, and Alexandria University, Egypt, he specializes in the fields of machine learning, signal processing, and cybersecurity. His research is particularly focused on the development of innovative solutions for edge computing and cyber-physical systems. Dr. Farag holds a Ph.D. in Computer Engineering from Virginia Tech, where he conducted groundbreaking research on enhancing trust in cyber-physical systems. His academic journey also includes a Master’s and Bachelor’s degree in Electrical Engineering from Alexandria University, both achieved with distinction. A prolific researcher, he has an impressive publication record in high-impact journals and has secured numerous research grants. Beyond his research contributions, Dr. Farag is dedicated to advancing the field through excellence in teaching, mentorship, and quality assurance, actively contributing to program development and accreditation processes.

Professional Profile:

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Summary of Suitability for Best Researcher Award: Dr. Mohammed M. Farag

Dr. Mohammed M. Farag’s academic and professional profile reflects significant accomplishments in research, teaching, and academic leadership. Based on his qualifications and achievements, he is a strong candidate for the Best Researcher Award for the following reasons.

πŸ§‘β€πŸŽ“ Education

πŸŽ“ Ph.D. in Computer Engineering (GPA: 4.00/4.00) – Virginia Tech, USA (2009-2012)
Dissertation: “Architectural Enhancements to Increase Trust in Cyber-Physical Systems Containing Untrusted Software and Hardware”

πŸŽ“ M.Sc. in Electrical Engineering (GPA: 4.00/4.00) – Alexandria University, Egypt (2003-2007)
Thesis: “Hardware Implementation of The Advanced Encryption Standard on Field Programmable Gate Arrays”

πŸŽ“ B.Sc. in Electrical Engineering, Distinction with Honor (GPA: 3.89/4.00) – Alexandria University, Egypt (1998-2003)
Project: “VLSI Design of Cryptographic Algorithms”

πŸ“š Research Interests

πŸ” Machine Learning for Signal Processing & Edge Computing
πŸ” Cybersecurity and Hardware Security
πŸ’Ύ VLSI Design and Embedded Systems
πŸ€– AI Applications in Electrical Engineering
🌐 Cyber-Physical Systems

πŸ† Key Achievements

πŸ“ Citations: 411 | h-index: 11 | i10-index: 11 (As of October 2024)
πŸ“– Published in IEEE Access, Sensors, and top-tier journals.
πŸ’° Secured multiple research grants from King Faisal University, totaling over 100,000 SAR.

πŸ’» Technical Expertise

πŸ’‘ Programming: Python, C++, MATLAB
πŸ–₯️ Hardware Design: VHDL, Verilog
πŸ“Š Machine Learning: TensorFlow, PyTorch, Keras
πŸ”§ CAD Tools: Synopsys, Cadence, Xilinx

πŸŽ“ Teaching Experience

πŸŽ“ Electrical Circuits, Signal Processing, Digital Logic, VLSI Design, Embedded Systems, and more!
🎯 Special focus on fostering practical skills in Semiconductor Devices and Cybersecurity.

PublicationΒ Top Notes

Wearable sensors based on artificial intelligence models for human activity recognition

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge

Design and Analysis of Convolutional Neural Layers: A Signal Processing Perspective

Matched Filter Interpretation of CNN Classifiers with Application to HAR

A Self-Contained STFT CNN for ECG Classification and Arrhythmia Detection at the Edge

Aggregated CDMA Crossbar With Hybrid ARQ for NoCs

Overloaded CDMA crossbar for network-on-chip