I’m Abdeljalil Bouzine, a Master's student in Artificial Intelligence & Digital Computing with a strong interest in:
- Machine Learning & Deep Learning
- LLM systems and inference optimization
- Physics-Informed Neural Networks (PINNs)
- Reinforcement Learning & multi-agent systems
- ERP systems and AI integration
- Backend engineering, automation, and deployment
I enjoy building projects that connect theory, engineering, and real-world impact.
- Developing PINNs-based solutions for PDEs and inverse problems
- Benchmarking transformer inference optimizations such as KV cache strategies
- Exploring AI + ERP workflows with Odoo and business process automation
- Building local AI agents and automation pipelines with Python, Docker, n8n, and LLMs
- Strengthening my skills in ML systems, deployment, and scalable AI applications
A practical LLM systems project focused on measuring the tradeoffs of KV cache in transformer inference.
- Compared cache strategies under constrained hardware
- Measured latency, throughput, and memory usage
- Identified strong default settings for local inference workloads
Research-oriented work around Physics-Informed Neural Networks for solving PDEs with boundary/initial conditions.
- Forward and inverse problem formulations
- Boundary condition handling in 1D / 2D / 3D
- Scientific ML workflows with PyTorch, Hydra, and PINNs-Torch
A reinforcement learning project for distributed inference and resource-aware decision making.
- Multi-agent environment design
- Latency-aware rewards and constrained allocation
- Practical experimentation with IQN / DQN-style methods
Hands-on work on Odoo functional flows and the intersection of ERP and AI systems.
- Sales, Inventory, and Invoicing workflows
- Business process understanding and automation logic
- Long-term focus on integrating AI into ERP environments
Projects combining embedded systems, APIs, forecasting, and data pipelines.
- Smart systems with edge devices
- Backend services with FastAPI
- Real-time data processing and anomaly detection
- Deepen my expertise in ML systems and AI deployment
- Build stronger foundations in distributed systems and scalable inference
- Advance in scientific machine learning and research-oriented AI
- Contribute to projects at the intersection of AI, automation, and enterprise systems
- Grow through impactful work in research, engineering, and open source
Building, learning, and pushing toward impactful AI systems.