Curating
Knowledge at the
Intersection of
Theory & Practice.
I am a Data Scientist and Ph.D. Candidate specializing in Artificial Intelligence and Federated Learning Security. My work focuses on defending decentralized AI pipelines against adversarial attacks, bridging the gap between secure robust algorithms and sustainable edge models.
"The role of the contemporary data scientist is not merely to discover new insights, but to build secure and resilient models for the data ecosystems we inhabit."— RESEARCH PHILOSOPHY, 2024
Professional Journey
EST. 2024Computer Vision – Video Analytics
Blockward · Casablanca, MAR
Designed multi-object tracking pipelines for sports video analysis using PyTorch. Built custom annotated datasets to train tracking systems and generate trajectory visualizations for tactical insights.
Machine Learning Researcher
DeepEcho · Rabat, MAR
Developed and optimized energy-efficient deep learning models (ViTs, GANs) for fetal ultrasound image analysis. Improved model accuracy and reduced computational costs for battery-powered edge devices.
Current Focus
Federated Learning Security
Defending decentralized AI pipelines against adversarial attacks and maintaining data integrity in distributed learning environments.
Video
Analytics
Edge AI
Optimization
Research & Publications
ACADEMICIOTA TANGLE 2.0: AN OVERVIEWopen_in_new
EDPACS Journal
Compared digital signature schemes across IOTA 1.0, 1.5, and 2.0 for security and decentralization. Analyzed cryptographic evolution enabling IOTA 2.0's coordinator-free, smart contract–capable architecture.
Signing Algorithms Behind Blockchain Digital Transactions
MASI Conference · Nador, Morocco
Analyzed and compared blockchain signature algorithms, including EdDSA, ECDSA, and RSA. Evaluated elliptic curve–based schemes against RSA for signing and verification efficiency and security.