Hi,
I'm Kacem,
Data Scientist and ML Engineer,
PhD in computer engineering (AI)
from Polytechnique Montreal.
Hi,
I'm Kacem,
Data Scientist and ML Engineer,
PhD in computer engineering (AI)
from Polytechnique Montreal.
Embedded Files
I'm Kacem,
Data Scientist and ML Engineer,
PhD in computer engineering (AI)
from Polytechnique Montreal.
I am a PhD graduate in computer engineering at Polytechnique Montreal (Canada). My doctoral dissertation is about the privacy and the security of deep learning algorithms, and focuses on improving the robustness of Deep Neural Networks and protecting them from malicious attacks.
In July 2019, I obtained an engineering degree with honors in industrial computing and automation from the National Institute of Applied Sciences and Technology (INSAT) (Tunisia). Because of my past engineering internships in Berlin (Germany), I have hands-on experience in embedded software development with a good knowledge in electronics and hardware design.
Aside from work, I enjoy going for long walks, cycling and going to gym. I also like cooking and spending quality time with my friends. I must say that I am passionate about technology and I love building robots as well as smart embedded systems.
News
News
2024/08: I am thrilled to announce that I have completed my PhD in Computer engineering at Polytechnique Montréal. I am excited that my thesis on the robustness and security of Deep Learning models has been nominated by the jury for the Best Thesis Award. https://www.linkedin.com/feed/update/urn:li:activity:7229511794162094081/
Our paper "Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks" has been accepted at the 2023 22nd IEEE International Conference on Machine Learning and Applications (ICMLA 2023).
K. Khaled, M. Dhaouadi, F. G. de Magalhães and G. Nicolescu, "Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks," 2023 International Conference on Machine Learning and Applications (ICMLA), Jacksonville, FL, USA, 2023, pp. 45-52, doi: 10.1109/ICMLA58977.2023.00015. Available online: https://ieeexplore.ieee.org/document/10459949
Our paper "FedSHIBU: Federated Similarity-based Head Independent Body Update" has been accepted at international Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 (FL-NeurIPS'22)
A. Sreemathy Raj, I. Tenison, K. Khaled, F. G. De Magalhães and G. Nicolescu, "FedSHIBU: Federated Similarity-based Head Independent Body Update," Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022), 2022, Available online: https://openreview.net/forum?id=o2Y_2FgEVg2z
Our paper "Careful What You Wish For: on the Extraction of Adversarially Trained Models" has been accepted at the 19th Annual International Conference on Privacy, Security & Trust (PST2022).
K. Khaled, G. Nicolescu and F. G. De Magalhães, "Careful What You Wish For: on the Extraction of Adversarially Trained Models," 2022 19th Annual International Conference on Privacy, Security & Trust (PST), Fredericton, NB, Canada, 2022, pp. 1-10, doi: 10.1109/PST55820.2022.9851981. Available online: https://ieeexplore.ieee.org/document/10459949
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