Dr. Ahmad Qaisar Ahmad Al Badawi

Dr. Ahmad Qaisar Ahmad Al Badawi

Assistant Professor, Homeland Security Program

Ahmad Al Badawi received his PhD degree in Electrical and Computer Engineering from the National University of Singapore, Singapore in 2018. He also has B.Sc. and M.Sc. degrees in Computer Engineering from Al-Balqa’ Applied University and the Jordan University of Science and Technology, respectively. He is currently with the Faculty of Resilience, Homeland Security (HLS) department, at the Rabdan Academy, Abu Dhabi, UAE, as an Assistant Professor teaching a wide spectrum of cybersecurity courses and conducting research on Post-Quantum Cryptography, Privacy-Preserving Technologies, Trustworthy Machine Learning and Cryptographic Engineering.

 

Before joining Rabdan Academy, he was a Research Scientist at the Institute for Infocomm Research, A*STAR in Singapore steering the efforts on accelerating fully homomorphic encryption and privacy-preserving machine and deep learning.

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  •  Ph.D. Electrical and Computer Engineering, National University of Singapore 2019
  • M.Sc. Computer Engineering, Jordan University of Science and Technology, 2010
  • B.Sc. Computer Engineering, Al-Balqa’ Applied University, 2007
  •  Cryptography
  • Computer Organization and Architecture
  • Algorithms
  • Cybersecurity

 

  •  Applied Cryptography.
  • Post-Quantum Cryptography.
  • Privacy-Preserving Technologies.
  • Trustworthy Machine Learning.
  • Combinatorial Optimization.
  • Evolutionary Algorithms.
  • Parallel Processing.
  • High-Performance Computing.

 

Journal Papers

 

[1] Paul, Jestine, Meenatchi Sundaram Muthu Selva Annamalai, William Ming, Ahmad Al Badawi, Bharadwaj Veeravalli, and Khin Mi Mi Aung. “Privacy-Preserving Collective Learning with Homomorphic Encryption.” IEEE Access (2021).

[2] Ahmad Al Badawi, et al.,”Multi-GPU Design and Performance Evaluation of Homomorphic Encryption on GPU Clusters” in IEEE Transactions on Parallel & Distributed Systems, vol. , no. 01, pp. 1-1, 5555. doi: 10.1109/TPDS.2020.3021238

[3] A. A. Badawi, L. Hoang, C. F. Mun, K. Laine and K. M. M. Aung, “PrivFT: Private and Fast Text Classification With Homomorphic Encryption,” in IEEE Access, vol. 8, pp. 226544-226556, 2020, doi: 10.1109/ACCESS.2020.3045465.

[4] Ahmad Al Badawi, et al. “Implementation and performance evaluation of RNS variants of the BFV homomorphic encryption scheme.” IEEE Transactions on Emerging Topics in Computing (2019), DOI: 10.1109/TETC.2019.2902799.

[5] Ahmad Al Badawi et al., “Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs,” in IEEE Transactions on Emerging Topics in Computing, DOI: 10.1109/TETC.2020.3014636.

[6] Ahmad Al Badawi, Bharadwaj Veeravalli, Khin Mi Mi Aung. Accelerating Subset Sum and Lattice based Public-Key Cryptosystems with Multi-core CPUs and GPUs. Journal of Parallel and Distributed Computing, 119 (2018): 179-190.

[7] Chung, H.; Kim, M.; Badawi, A.A.; Aung, K.M.M.; Veeravalli, B. Homomorphic Comparison for Point Numbers with User-Controllable Precision and Its Applications. MDPI, Symmetry 2020, 12, 788.

[8] Ahmad Al Badawi, and Ali Shatnawi. 2013. Static scheduling of directed acyclic data flow graphs onto multiprocessors using particle swarm optimization. Computers & Operations Research. Elsevier: Vol. 40, Issue 10, Paper: (2322-2328).

[9] Al-Hiaja, Qasem Abu, Abdullah AlShuaibi, and Ahmad Al Badawi. Frequency Analysis of 32-bit Modular Divider Based on Extended GCD Algorithm for Different FPGA chips. International Journal of Computers & Technology 17.1 (2018): 7133-7139.

 

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