AI Engineer and Master’s student in Artificial Intelligence & Digital Computing at FST Beni Mellal. I build document AI pipelines, quantization research, and full-stack systems that connect models to real products — from OCR and layout detection to RAG chatbots and interactive demos.
I was always into math and computer science and I couldn’t leave one for another — but I was lucky to find out AI is the perfect mix between both, so here we are :)
Master’s in Artificial Intelligence & Digital Computing at Faculté des Sciences et Techniques de Beni Mellal (Oct 2024 – July 2026).
Bachelor’s in Computer Software Engineering from FST Errachidia (2021–2024).
President of Code Crafters, the FST Beni Mellal CS/AI student club.
Member of Great Debaters at FST Errachidia, Morocco (2022–2024).
Master’s — Artificial Intelligence & Digital Computing
Faculté des Sciences et Techniques de Beni Mellal
Oct 2024 – July 2026
Bachelor’s — Computer Software Engineering
FST Errachidia, Morocco
2021 – 2024
President — Code Crafters
FST Beni Mellal CS/AI student club · Jan 2024 – 2025
Member — Great Debaters
FST Errachidia, Morocco · 2022 – 2024
02 — Projects
Selected work
Filter by Deep Learning, Machine Learning, NLP, xAI, Tools, or Fun.
Compress long industrial maintenance histories into episodic memory, then reconstruct what matters for failure understanding. On NASA C-MAPSS FD001, ~20% memory keeps event recall near full history with stronger temporal structure than retrieval-only.
Uncertainty-aware active perception so a VLM robot can observe again instead of making unsafe crop interventions — scores information gain and chooses PICK / OBSERVE / ABSTAIN.
Does JEPA-style latent prediction encode naive physics better than pixel reconstruction? Trains JEPA vs pixel baselines on procedural 2D pymunk worlds with linear probes and violation-of-expectation surprise curves.
Language-conditioned visuomotor policy with BC → RL fine-tuning, active perception, and latent world-model planning on LIBERO. Dashboard exports 5 LIBERO Spatial rollouts — research in progress.
Decision-support dermoscopy classifier on HAM10000 with measured benchmarks, Grad-CAM++ interpretability, and an interactive referral-threshold showcase. EfficientNet-B0 matches ResNet-50 at 6× lower CPU latency; melanoma AUC 0.888.
Hamiltonian NN vs MLP vs Neural ODE on a 1D bouncing ball where contact breaks smooth-Hamiltonian assumptions. Continuous-time structure beats the MLP by ~300× rollout MSE; HNN wins only in the elastic case.
Frequency-decomposed Chebyshev KAN for long-term forecasting — RevIN, per-band M-KAN, cross-band attention; benchmarks vs PatchTST, iTransformer, and more. Part of the KAN Portfolio showcases.
Simulate, search, and learn policies for classic 2048 — Monte Carlo bakeoffs, expectimax, and Double DQN ablations, plus a playable Angular companion. Heuristic and shallow expectimax reach ~4970 mean score vs ~894 random; short CPU Double DQN demos beat random and approach heuristic with longer training. Charts load from committed metrics via a Render API. Streamlit lab: https://twenty48-lab.onrender.com
Causal ML demo measuring how advanced weather information changes solar forecasting error across regimes. Estimates ATEs with S/T/X-Learner and Causal Forest — headline ATE ≈ −10.3 pp absolute error, strongest under extreme conditions.
Function-approximation arena — fight models, run tournaments, and play Graphwar in the browser. Client-side play works offline; fights and tournaments hit a FastAPI backend. Also published as the Python package graphwar on PyPI.
Provably secure route planning for high-value transport — cargo + decoy routes that preserve Type-B opacity at checkpoints. LangGraph pipeline on OSM/NetworkX graphs with live God/Observer map playback.
DINOv2 embeddings → QUBO max-diversity lunar site selection with greedy, simulated annealing, and QAOA solvers. Greedy scores 8.460 diversity, beating Neal SA and Aer-simulated QAOA on a 12-qubit pool.
Emotion-conditioned agentic RAG that picks a retrieval/generation strategy and explains why. A classifier tags affect; an agent maps emotion → concise / scaffolded / standard strategy with a rationale trace.
Nine RAG architectures built in order — each motivated by a measured failure of the one before it. v8 Agentic hits 0.876; v9 Modular trades accuracy for perfect adversarial resistance; v6 RAPTOR peaks faithfulness at 0.954.
Interpretable image classification with Kolmogorov–Arnold Networks — Pure KAN and Conv-KAN vs MLP/CNN baselines, plus edge-function visualization and symbolic fits. Part of the KAN Portfolio showcases.