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aghasalim/README.md

Aghasalim Mustafazada

claims checked

AI student at Howest, Kortrijk. Anomaly detection and interpretability. Every number below is measured, reproducible from its repo, and re-checked weekly by CI β€” including the ones that make me look bad.

Open to AI and backend internships.

πŸ”¬ Built to prove something, then refuted its own premise

SpuriousAD β€” image AUROC stays 1.000 at every confound correlation; the faithfulness collapse was an artefact

GraphCiteFaith β€” 3,965 of 3,965 cited node ids real, while two models name the structure at chance

DriftHarm β€” harm-precision 0.512–0.593 against a 0.517 base rate, so it is not a ranking

RingFaith β€” explainer overlap correlates r=+0.945 with its own random baseline

LeakGraph β€” only 5 of 10 GNN cells resolve above noise; the metric mostly tracks graph size

AI Act fairness audit β€” Annex III exempts fraud detection, so the audit was never legally required

Forecast backtesting β€” the split costs 4.1% MASE where the horizon costs 42.1%

M4 prediction intervals β€” nothing reaches nominal 95% coverage; the best is 86.6%

πŸ§ͺ Measured end to end

Explainable defect detector β€” PatchCore reproduced across all 15 MVTec categories, 0.9874 mean AUROC Β· demo

EU AI Act RAG β€” 90.2% faithfulness over 45 questions, graded by a separate model family Β· demo

Fraud decision trail β€” 0.1044 AUC separates the flattering split from the defensible one Β· demo

MLOps pipeline β€” prediction PSI correlates βˆ’0.709 with the AUC loss it should predict

RL reward shaping β€” six reward functions, two the agent exploited; 43.2% against a 73.5% oracle Β· demo

Hallucination-aware captioning β€” BLIP hallucinates on 4.5% of probes but misses 29% of objects present Β· demo

A/B testing & causal inference β€” mSPRT proved more conservative than specified: 0.9% error against a nominal 5%

LoRA fine-tuning β€” an adapter of 0.28% of parameters still degraded held-out behaviour

Offline vs online metrics β€” an AUC of 0.5386 next to what the policy actually earns

ARC-AGI-2 β€” 3.9%, published because a negative result honestly reported is still a result

Smart IVC cage platform β€” full stack, firmware to frontend; 173 of 204 commits mine on a 3-person client project

πŸ…

Gold medal, International STEM Olympiad β€” mathematics, France 2022. Bronze, German, 2023. 0.9086 private leaderboard on IEEE-CIS, scored by Kaggle against labels I never saw. Taught ~200 students over four years. ORCID 0009-0001-8746-4582.

πŸ› 

Python Β· PyTorch Β· LightGBM Β· Transformers Β· SHAP Β· FastAPI Β· Streamlit Β· React Β· Docker Β· GitHub Actions Β· Raspberry Pi

πŸ“«

salim.mustafazada@student.howest.be Β· LinkedIn

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  1. arc-prize-2026 arc-prize-2026 Public

    ARC-AGI-2 attempt: object-centric DSL with a verifier-backed program search. 3.9% on public training, 0% on public evaluation -- and a characterisation of why.

    Python 1

  2. eu-ai-act-rag eu-ai-act-rag Public

    Grounded QA over the EU AI Act where the evaluation harness is the deliverable: retrieval, faithfulness and hallucination metrics over 45 hand-written questions

    Python 1

  3. explainable-defect-detector explainable-defect-detector Public

    Anomaly detection for visual defect inspection: trained on normal images only, with localisation. PyTorch + MVTec AD.

    Python 1

  4. lora-forgetting lora-forgetting Public

    LoRA fine-tuning a small LLM for structured extraction, with the catastrophic-forgetting check most projects skip. Both numbers reported.

    Python 1

  5. mlops-fraud-pipeline mlops-fraud-pipeline Public

    Fraud model behind FastAPI, CI gating and drift monitoring -- where the monitoring is measured against a healthy null, not just demoed against an injected failure.

    Python 1

  6. ieee-fraud-ml ieee-fraud-ml Public

    Working IEEE-CIS Fraud Detection end to end, where the documented decision trail is the deliverable, not the leaderboard score.

    Python 1