Nesma Mohamed Ibrahim Rezk

Assistant Professor at Computer and Systems Engineering

Career Information

Academic Information

Brief

Nesma M. Rezk received bachelor’s and master’s degrees in computer and systems engineering from the Faculty of Engineering, Ain Shams University, Egypt, in 2010 and 2015, respectively. She received her Ph.D. in computer science and engineering from the School of Information Technology, Halmstad University, Sweden, in 2022. Her Ph.D. thesis entitled “Deep Learning on the Edge: A Flexible multi-level Optimization Approach” connects two scientific areas: edge computing and deep learning. She is lecturing at the Department of Computer and Systems at the Faculty of Engineering, Ain Shams University, Egypt. Her research interests include embedded systems, deep learning applications, the design of domain-specific architectures, and multi-objective optimization.

Publications

-Nesma M. Rezk, Madhura Purnaprajna, Tomas Nordström, and Zain Ul-Abdin, "Recurrent Neural Networks: An Embedded Computing Perspective," IEEE Access, vol. 8, pp. 57967-57996, 2020.
https://ieeexplore.ieee.org/iel7/6287639/8948470/09044359.pdf

-Nesma M. Rezk, Madhura Purnaprajna, and Zain Ul-Abdin, "Streaming Tiles: Flexible Implementation of Convolution Neural Networks Inference on Manycore Architectures," 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018, pp. 867-876.
https://www.diva-portal.org/smash/record.jsf?pid=diva2:1212121

-Nesma M. Rezk, Tomas Nordström, Dimitrios Stathis, Zain Ul-Abdin, Eren Erdal Aksoy, and Ahmed Hemani, "MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks," Journal of Systems Architecture, 2022. https://www.mdpi.com/2078-2489/13/4/176

-Nesma M. Rezk, Tomas Nordström, and Zain Ul-Abdin, "Shrink and Eliminate: A Study of Post-training Quantization and Repeated Operations Elimination in RNN Models", Information, 2022, vol. 13. https://www.sciencedirect.com/science/article/pii/S1383762122002636

-Nesma M. Rezk, Y. Alkabani, H. Bedor, S. Hammad. 2014. A distributed genetic algorithm for swarm robots obstacle avoidance. 2019 IEEE 9th International Conference on Computer Engineering & Systems (ICCES).
https://ieeexplore.ieee.org/abstract/document/7030951/