Education, AI Lab & Methodology
Deep dive into my university curriculum in AI & Software Architecture, applied deep learning research lab, and rigorous engineering methodology.
Education & Degrees
// THEORETICAL FOUNDATIONS & AI
ESGIS Lomé
Master in Artificial Intelligence & Big Data
M1 (2025–2026) · M2 (2026–present). Specialization in machine learning, deep neural architectures, big data pipelines, and production AI integration.
ESGIS Lomé
Bachelor in Computer Science, Networks & Telecom
Specialty in Software Architecture. Engineering scalable web and mobile software architectures, relational database modeling, and clean code paradigms.
Applied AI & Big Data
// PRAGMATIC MODELS & APPLIED DEEP LEARNING
Natural Language Processing & LLMs
Building semantic analysis pipelines, entity extraction, text classification, and integrating LLMs to automate unstructured text processing.
Computer Vision & Deep Learning
Training convolutional neural networks (CNNs) for visual classification, facial expression recognition, and biomedical anomaly detection (e.g. malaria-infected cell classification).
Recommender Systems & Collaborative Filtering
Modeling intelligent recommendation engines utilizing collaborative and content-based filtering to predict user preferences and maximize platform engagement.
Big Data Pipelines & Cloud Streaming
Designing serverless cloud architectures for ingesting, processing, and storing real-time IoT and telemetry data streams at scale.
How I Build
// STANDARDS, RIGOR & VELOCITY
Scalable Architecture & Systems Engineering
// Engineered for high availability
Prior to coding, meticulous database modeling, clear separation of concerns (modular REST APIs, decoupled services, serverless pipelines), and forward-looking load planning.
Clean Code, Strict Typing & Maintainability
// Zero tolerance for technical debt
Strict adherence to enterprise standards: TypeScript on the frontend, clean PEP8 Python services on the backend, automated test routines, and continuous delivery pipelines.
Pragmatic, Useful & Production-Ready AI
// From research to real-world value
Machine learning must never stay trapped in a research notebook. Strong focus on efficient inference, production integration, and seamless user experiences solving real business problems.
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