I design intelligent, high-performance systems bridging data, algorithms, and real-world impact. Currently focused on Deep Learning, Computer Vision, and AI-driven Medical Innovations.
I build end-to-end architectures, not just standalone models. I translate complex AI concepts into scalable, reliable software infrastructure that thrives in production.
I break down real-world problems into clear mathematical formulations. I always validate simple, robust baselines before moving to deep learning networks.
Driven by curiosity, I actively read state-of-the-art research in optimization and perception, adapting the latest breakthroughs into applied, impactful solutions.
Pushing the boundaries of intelligent systems
"I aim to pioneer Neuro-Symbolic architectures that bridge statistical learning with logic-based reasoning. By combining deep learning with strict logical constraints, I want to build highly interpretable and fail-safe AI systems. This is especially crucial for life-saving domains like automated clinical diagnostics and precision healthcare."
Algérie Telecom
EIT Digital
Oracle
University of Michigan
Docker, Inc.
NVIDIA
Applied research and engineered systems.
Problem: Cryptocurrency price volatility unpredictability.
Importance: Crucial for robust risk management and high-frequency algorithmic trading.
Approach: Built advanced time-series analysis and forecasting models utilizing historical orderbook data.
Innovation: Hybrid feature engineering capturing both macro trends and micro market volatility.
Results: Established highly accurate short-term prediction corridors.
Future Work: Real-time sentiment analysis integration via NLP pipelines.
Problem: Extracting actionable business intelligence from massive, fragmented e-commerce data.
Importance: Drives data-backed revenue growth and optimizes modern inventory management.
Approach: Engineered an end-to-end ETL data pipeline leading into advanced statistical modeling.
Innovation: Deployed automated anomaly detection algorithms for immediate sales velocity tracking.
Results: Reduced executive reporting latency by 70% and isolated high-value customer cohorts.
Future Work: Integration of predictive dynamic Customer Lifetime Value (CLV) scoring.
Problem: Inefficient and error-prone text entry for Arabic users due to complex morphological syntax.
Importance: Enhances digital accessibility and typing speeds for millions globally.
Approach: Fused traditional N-gram models with deep learning sequence models for robust prediction.
Innovation: Highly specialized context-aware algorithms optimized specifically for Arabic grammatical matrices.
Results: Demonstrated a 40% reduction in keystrokes for standard conversational Arabic text.
Future Work: Adapting models to handle unstructured regional dialects and slang.
Problem: Disconnected, legacy supply chain communication among Algerian manufacturers, wholesalers, and stores.
Importance: Modernization of the B2B distribution network to radically reduce friction and logistical costs.
Approach: Developed a full-stack PWA platform acting as a centralized digital B2B marketplace.
Innovation: Designed an ultra-lightweight, offline-capable architecture for zones with unstable internet connectivity.
Results: Successfully connected disparate local vendors with a fast, scalable MVP deployment.
Future Work: Integrating mobile payment gateways and predictive inventory restocking nodes.
Problem: The need for highly reliable, non-intrusive personal biometric authentication from writing.
Importance: Critical for high-security access applications and advanced digital forensic analysis.
Approach: Engineered a full biometric pipeline spanning preprocessing, feature extraction, and classification.
Innovation: Conducted comparative ensemble analysis merging robust classical SVMs with lightweight CNNs (SqueezeNet) for edge-deployment compatibility.
Results: Achieved high-accuracy identification metrics matching state-of-the-art academic benchmarks.
Future Work: Expansion into real-time dynamic signature verification systems.
Problem: Delayed and inaccurate manual diagnosis of critical chest conditions from medical imaging.
Importance: Rapid, accurate detection of pneumonia, tuberculosis, and lung cancer saves lives.
Approach: Implemented advanced deep learning classification and fine-grained segmentation on X-rays and CT scans.
Innovation: Developed a multi-disease classification model enriched with Grad-CAM attention maps to provide necessary clinical interpretability.
Results: Exceeded baseline human radiologist screening accuracy for specific early-stage pathogenic markers.
Future Work: Securing the model for deployment via an encrypted API for direct hospital infrastructure integration.
Problem: Time-consuming, error-prone manual MRI analysis for delineating brain tumors and lesions.
Importance: Rapid, highly accurate, and objective diagnosis is absolutely critical for patient survival and surgical planning.
Approach: Designed specialized Convolutional Neural Networks for the semantic segmentation of intricate tumor regions.
Innovation: Utilized 3D spatial context logic for significantly more precise tumor boundary delineation than standard 2D slices.
Results: Achieved a high Dice similarity coefficient, strongly validating the segmentation architecture's accuracy.
Future Work: Multi-modal MRI (T1, T2, FLAIR) volume fusion for comprehensively enhanced detection.
Problem: Understanding and categorizing massive volumes of unstructured text feedback at scale.
Importance: Indispensable for automated brand monitoring, dynamic ad-targeting, and rapid customer service triage.
Approach: Deployed a state-of-the-art Transformer-based text classification system exposed via a high-performance REST API.
Innovation: Enabled highly robust handling of specific domain jargon, sarcasm, and mixed sentiment signals.
Results: Reached 90%+ nuanced classification accuracy across out-of-distribution validation datasets.
Future Work: Expanding core models to support multilingual contexts and real-time social media streaming analysis.