Issam Hammad, Ph.D., P.Eng.

Research Topics:

• Artificial Intelligence (AI)/Machine Learning (ML) in Engineering Applications
• AI for Nuclear Energy and Power Generation
• Approximate Computing
• Edge AI and Real-Time AI
• Computer Vision
• Cloud Computing
• Cryptography and Cybersecurity
• Video/Audio Compression.

Dr. Hammad received the B.Sc degree in computer engineering from Princess Sumaya University for Technology, Amman, Jordan in 2008, the M.A.Sc and the Ph.D. degrees in electrical and computer engineering from Dalhousie University, NS, Canada in 2010 and 2021, respectively. He is currently an Assistant Professor at the Department of Engineering Mathematics and Internetworking. Prior to joining Dalhousie, he was the Engineering AI Lead at Alithya Digital Technology Corporation, Toronto, ON, Canada. He has more than twelve years of engineering industry experience in machine learning/deep learning, big data, engineering automation, control systems, predictive maintenance, video compression and analysis, cryptography and cybersecurity, cloud computing, distributed computing, embedded systems, custom hardware (FPGA and ASIC), IoT/ IIoT, nuclear software systems, nuclear software quality and standards, full-stack web development, and database design.

Selected Publications

Hammad, Issam, et al. "Using Deep Learning to Automate the Detection of Flaws in Nuclear Fuel Channel UT Scans." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69.1 (2021): 323-329.

Hammad, Issam, et al. "CNN inference using a preprocessing precision controller and approximate multipliers with various precisions." IEEE Access 9 (2021): 7220-7232.

Hammad, Issam, and Kamal El-Sankary. "Practical considerations for accuracy evaluation in sensor-based machine learning and deep learning." Sensors 19.16 (2019): 3491.

Hammad, Issam, Kamal El-Sankary, and Holly Hornibrook. "RETSManager: Real-estate database builder and synchronizer." SoftwareX 10 (2019): 100351.

Hammad, Issam, and Kamal El-Sankary. "Impact of approximate multipliers on VGG deep learning network." IEEE Access 6 (2018): 60438-60444.