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Dr. Mahmoud Darwich

Assistant Professor of Computer Science

As a professor, I am motivated by the opportunity to inspire and educate the next generation of computer scientists.

Education

PhD in Computer Engineering, University of Louisiana at Lafayette, USA 2017
MS in Computer Engineering, University of Louisiana at Lafayette, USA 2013
Bachelor in Electrical Engineering, Communications and Electronics, Beirut Arab University, Lebanon 2006

Hometown

Tripoli, Lebanon

Why Computer Science and Teaching?

My passion for computer science began with a fascination for problem-solving and the endless possibilities that technology offers to transform our world. The field of computer science is dynamic and ever-evolving, offering the opportunity to continuously learn and innovate. I chose this profession because it allows me to merge my interest in cutting-edge technologies with my desire to make a tangible impact, whether through improving video streaming systems, advancing healthcare diagnostics with machine learning, or educating the next generation of computer scientists. Teaching and research in this field enable me to contribute to the development of technologies that can enhance lives and to inspire students to pursue their own paths in this exciting and transformative discipline.

What is your favorite part of your job?

My favorite part of the job is the opportunity to engage with students and witness their growth as they develop into skilled and confident professionals. I find immense satisfaction in fostering an environment where students can explore complex concepts, ask questions, and apply their knowledge to solve real-world problems. Whether it’s guiding a student through a challenging programming assignment, seeing them grasp the intricacies of network protocols, or helping them make connections between theoretical research and practical application, these moments of discovery and achievement are incredibly rewarding. Additionally, I cherish the continuous learning aspect of my role, as my interactions with students and involvement in research keep me constantly engaged with new ideas and evolving technologies.

How is Mount Union unique?

A Mount Union education is distinguished by its commitment to providing a well-rounded, integrative learning experience that combines rigorous academics with a strong emphasis on personal and professional development. The university’s small class sizes allow for personalized attention and meaningful interactions between faculty and students, fostering a supportive environment where students are encouraged to explore their interests and pursue their passions. The computer science program at Mount Union stands out for its innovative curriculum that balances foundational knowledge with cutting-edge advancements in technology. Our program uniquely integrates hands-on, project-based learning with theoretical concepts, allowing students to immediately apply what they learn in real-world scenarios. We emphasize the development of both technical skills and soft skills, ensuring that graduates are not only proficient in coding and systems design but also effective communicators and problem-solvers.

Book Chapter

Journal Publications

  • Essel, E., Abdelhamid, A., Darwich, M., Khalifa, F., Lacy, F. and Ismail, Y., 2024. High-Fidelity Machine Learning Techniques for Driver Drowsiness Detection. International Journal of Computing and Digital Systems, 16(1), pp.1-11. DOI: http://dx.doi.org/10.12785/ijcds/XXXXXX
  • Darwich, M., Bayoumi, M. Video quality adaptation using CNN and RNN models for cost-effective and scalable video streaming Services. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04315-8
  • Darwich M, Taghreed Alghamdi, Kasem Khalil, Yasser Ismail, and Magdy Bayoumi. "Cost-optimized cloud resource management for video streaming: Arima predictive approach." Cluster Computing (2023): 1-15. https://doi.org/10.1007/s10586-023-04135-2
  • Darwich M., Ismail Y., Darwich T., and Bayoumi M, (2022). Cost Minimization of Cloud Services for On-Demand Video Streaming. The Springer Nature Computer Science. https://doi.org/10.1007/s42979-022-01140-x
  • Ismail, Y., Hammad, M., Darwich, M., & Elmedany, W. (2021). Homeland security video surveillance system utilizing the internet of video things for smart cities. IET Computers & Digital Techniques, 15(4), 302–319. https://doi.org/10.1049/cdt2.12014
  • Darwich, M., Salehi, M. A., Beyazit, E., & Bayoumi, M. (2019). Cost-Efficient Cloud-Based Video Streaming Through Measuring Hotness. The Computer Journal, 62(5), 641–656. https://doi.org/10.1093/comjnl/bxy057
  • Li X., Joshi Y., Darwich M., Landrenau B., Amini Salehi M., Bayoumi M. (2018). Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services. IEEE Transactions on Parallel and Distributed Systems. vol. 30, no. 4, pp. 910-922.http://doi.org/10.1109/TPDS.2018.2870651

Conference Papers

  • K. Khalil, T. Mohaidat, M. Darwich, A. Kumar and M. Bayoumi, "An Efficient Hardware Design of CoAP Protocol for The Internet of Things," 2024 IEEE 17th Dallas Circuits and Systems Conference (DCAS), Richardson, TX, USA, 2024, pp. 1-5, DOI: 10.1109/DCAS61159.2024.10539896
  • M. Darwich, K. Khalil and M. Bayoumi, "Smart Streaming: Deep Learning Applications in Video Streaming Optimization," SoutheastCon 2024, Atlanta, GA, USA, 2024, pp. 22-27, DOI: 10.1109/SoutheastCon52093.2024.10500209
  • M. Darwich, K. Khalil and M. Bayoumi, "Optimizing QoE in IoT-Based Video Streaming through Deep Learning Algorithms," 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Osaka, Japan, 2024, pp. 052-057, DOI:10.1109/ICAIIC60209.2024.10463417
  • Darwich, M, Kasem Khalil, Yasser Ismail, and Magdy Bayoumi. "Deep Learning-Driven Video Summarization on the Cloud: A Pathway to Efficient Storage and Quick Access." In 2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), pp. 360-365. IEEE, 2023. 10.1109/MCSoC60832.2023.00060
  • Darwich, M, Kasem Khalil, and Magdy Bayoumi. "Optimizing Video Streaming Costs in the Cloud: A CNN Approach." In 2023 International Conference on Information Technology and Computing (ICITCOM), pp. 125-130. IEEE, 2023. 10.1109/ICITCOM60176.2023.10441903
  • Darwich, M, Kasem Khalil, and Magdy Bayoumi. "Enhancing Video Storage Efficiency in the Cloud: Machine Learning-Driven Optimization Strategies." In 2023 International Conference on Information Technology and Computing (ICITCOM), pp. 119-124. IEEE, 2023. 10.1109/ICITCOM60176.2023.10442879
  • Darwich, M, and Magdy Bayoumi. "LSTM Network Assisted Content Caching at the Edge for Video on Demand." In 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), pp. 493-496. IEEE, 2023. 10.1109/ICCCMLA58983.2023.10346904
  • Darwich, M, Taghreed Alghamdi, and Magdy Bayoumi. "Deep Learning-Enabled Efficient Storage and Retrieval of Video Streams in the Cloud." In 2023 IEEE 8th International Conference on Smart Cloud (SmartCloud), pp. 99-104. IEEE, 2023. 10.1109/SmartCloud58862.2023.00026
  • Darwich, M, Taghreed Alghamdi, and Magdy Bayoumi. "Deep Learning Approach for Cost and Storage Optimization of Video Streaming in Cloud Environments." In 2023 IEEE 8th International Conference on Smart Cloud (SmartCloud), pp. 80-85. IEEE, 2023. 10.1109/SmartCloud58862.2023.00022
  • Darwich, M, Kasem Khalil, Yasser Ismail, and Magdy Bayoumi. "Edge computing for efficient storage and low-latency video streaming in cloud environments." In 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), pp. 1-5. IEEE, 2023. 10.1109/AIBThings58340.2023.10292469
  • Darwich, M, Kasem Khalil, Yasser Ismail, and Magdy Bayoumi. "Adaptive Video Streaming: An AI-Driven Approach Leveraging Cloud and Edge Computing." In 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), pp. 1-5. IEEE, 2023. 10.1109/AIBThings58340.2023.10292484
  • Darwich, M, Kasem Khalil, Yasser Ismail, and Magdy Bayoumi. "Predictive Storage Management for Cloud-Based Video Streaming Using ML ARIMA Model." In 2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 274-278. IEEE, 2023.10.1109/MWSCAS57524.2023.10405915
  • Darwich, M, Kasem Khalil, Yasser Ismail, and Magdy Bayoumi. "Enhancing cloud-based video streaming efficiency using neural networks." In 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), pp. 1-5. IEEE, 2023. 10.1109/COINS57856.2023.10189314
  • Darwich M., (2022). Machine Learning Technique Predicting Video Streaming Views to Reduce Cost of Cloud Services. 2022 IEEE 8thVirtual World Forum on Internet of Things 10.1109/WF-IoT54382.2022.10152250
  • Darwich, M., Ismail, Y., Darwich, T., & Bayoumi, M. (2021). Improving Hierarchy Storage for Video Streaming in Cloud. 2021 IEEE 7thVirtual World Forum on Internet of Things. http://doi.org/10.1109/WF-IoT51360.2021.9595722
  • Darwich, M., Ismail, Y., Darwich, T., & Bayoumi, M. (2020). Cost-Efficient Storage for On-Demand Video Streaming on Cloud. 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). https://doi.org/10.1109/WF-IoT48130.2020.9221374
  • Darwich, M., Beyazit, E., Salehi, M. A., & Bayoumi, M. (2017). Cost-Efficient Repository Management for Cloud-Based On-demand Video Streaming. 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), 39–44.https://doi.org/10.1109/MobileCloud.2017.23
  • Darwich, M., Abdelgawad, A., & Bayoumi, M. (2016). A Survey on the power and robustness of FinFET SRAM. 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), 1–4. https://doi.org/10.1109/MWSCAS.2016.7869953
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