El Sayed Mahmoud
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Biography
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Teaching & Research Interests
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Research, Innovation and Entrepreneurship
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Grants
El Sayed has been with Sheridan as a professor of Applied Computing since 2013. During that period, he taught introductory and upper-level undergraduate courses in computer science. El Sayed also supervised students in completing their theses and led Artificial-Intelligence(AI)-based research projects as an active principal investigator at the Centre of Mobile Innovation (CMI). He has also been involved in curriculum development and led the collaborative effort to develop the program proposal for a new Honours Bachelor of Computer Science degree with specializations in Data Analytics, Cloud Computing, Game Engineering, Network Engineering and simulation & visualization. The program was approved in Jan 2020 and launched in Sep 2021. Furthermore, El Sayed served in many committees, including the Sheridan Senate, the Local Academic Council of Faculty of Science & Technology, Scholarship, Research, and Creative Activities Committee.
Before joining Sheridan, El Sayed successfully completed his graduate studies at the University of Guelph in Computer Science and received his MSc. and Ph.D. degrees in 2008 and 2013. Before his graduate studies, El Sayed occupied several professional positions in Applied Computing and post-secondary education industries for 15 years. After completing his undergraduate degree in Computer Engineering in 1991, El Sayed worked as a software developer at ECS (a software company in Egypt). Three years later, he became a computer instructor at AICT, a multi-location computer institute that carries certified partnerships with Microsoft and Edexcel in the Middle East. There, El Sayed was promoted to Branch Manager to manage a two-year computing diploma program, and he taught various computing courses. El Sayed achieved Microsoft-certified trainer status and was promoted to Regional Manager. He successfully managed the two-year diploma program and taught computing courses on several campuses of the AICT.
El Sayed has researched Artificial Intelligence since 2006. His research focuses on developing robust AI-based systems that provide innovative solutions for health and business applications, including Big data, data analytics, machine learning, mobile cloud computing and social networks. His Engineering, Computer Science, Artificial Intelligence, and Business experiences significantly support his multidisciplinary research projects' breadth and depths. El Sayed published many peer-reviewed papers in various international journals and conferences. For more information, visit his web page: http://mahmouel.dev.fast.sheridanc.on.ca/
Teaching Interests
- Enhancing the students’ uptake of learning Applied Computing by using various approaches to encourage their engagement including student-centred and active learning.
- Inspiring student's desire to learn and make the class environment meaningful, encouraging, supportive, and respectful.
- Using learning-by-doing focusing on scenarios from real life or industry.
Research Interests
- Developing robust AI-based systems that provide innovative solutions for health and business applications, including Big data, data analytics, mobile computing, cloud computing and social networks.
- Investigating how to combine Engineering, Computer Science, Artificial Intelligence, healthcare and Business research effectively for Multidisciplinary research.
Book Chapters
Mahmoud, E., Calvert, D. (2013). A Robust System for Distributed Data Mining and Preserving-Privacy. In SmartData(pp. 129--138). Springer, New York, NY.
Conference Proceedings
Mahmoud, E., Calvert, D. (2009). Auto-calibration of support vector machines for detecting disease outbreaks. Science and Technology for Humanity (TIC-STH), 2009 IEEE Toronto International Conference.
Mahmoud, E., Calvert, D. (2008). Comparing Performance of Back Propagation Networks and Support Vector Machines in Detecting Disease Outbreaks. Intelligent Engineering Systems through Artificial Neural Networks Volume 18.
Mahmoud, E., Calvert, D. (2009). Regression of Representative Keys for Classification: A Simple Learning Approach. Intelligent Engineering Systems through Artificial Neural Networks.
Mahmoud, E., Dancy, E. (2016). Practice and Refactoring Log: A Reflection Based Learning Strategy to Improve the Fluency of Computing Students in Writing Computer Programs. Proceedings of EduTeach International Conference, Toronto.
Mahmoud, E., Stacey, D. (2007). Identifying Syndromic Fingerprints in Reason Fields in Emergency Department or Telehealth Records using N-grams for Similarity Analysis. Advances in Disease Surveillance.
Mahmoud, E., Akulick, S. (2017). Intent detection through text mining and analysis. Future Technologies Conference (FTC), Vancouver, BC.
Mahmoud, E., Calvert, D. (2007). Comparing Syndromic Surveillances using Two Aspects: Emergency and Telehealth Data Sources. Advances in Disease Surveillance.
Journal Articles
Mahmoud, E. (2017). An Engagement Strategy for Teaching Computing Concepts.International Journal of Digital Society, 8(2), 1288-1295.
Mahmoud, E., Ross, M., Abdel-Aal, E. (2018). Exploring Identifiers of Research Articles Related to Food and Disease using Artificial Intelligence.International Journal of Advanced Computer Science and Applications (IJACSA), 9(11).
Mahmoud, E., ERAM, B., PATEL, J., SATVEDI, A., SNEYD, R. (2018). Video-Call Platforms for Online Healthcare.International Journal of Advances in Science Engineering and Technology, 56--64.
Mahmoud, E., Grishchenko, I. (2019). An Investigation of a Convolution Neural Network Architecture for Detecting Distracted Pedestrians.International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 630--638.
Mahmoud, E., Niu, Y., Hui, L., Hechao, D., Ruiqiang, M., Guihua, W., Tim A., M., Kim, S. (2021). Efficacy of Individual Bacteriophages Does Not Predict Efficacy of Bacteriophage Cocktails for Control of Escherichia coli O157.Frontiers in Microbiology, 12, 140.
Mahmoud, E., Martinez-Soto, C., Cuci'c, S., Lin, J., Kirst, S., Khursigara, C., Anany, H. (2021). PHIDA: A High Throughput Turbidimetric Data Analytic Tool to Compare Host Range Profiles of Bacteriophages Isolated Using Different Enrichment Methods.Viruses, 13(11), 2120.
Grants
"CTL Conference Scholarship". Sheridan College. Centre for Teaching & Learning (CTL). $1,500. January 2016.
El Sayed Mahmoud (Principle Investigator)."Smart Duct Assembler". NSERC. Applied Research and Development grants. $225,000. 2021-.
El Sayed Mahmoud (Co-Investigator), Andy Alubaidy (Principle Investigator) Weijing Ma .
