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Achievement: Pair ExtraordinaireAchievement: Pull SharkAchievement: YOLOAchievement: Quickdraw

Achievements

Achievement: Pair ExtraordinaireAchievement: Pull SharkAchievement: YOLOAchievement: Quickdraw

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Aqib121201/README.md

Aqib Siddiqui

Profile Snapshot

Architecting interpretable, production‑grade ML pipelines with research‑level reproducibility and deployment‑level reliability.

Core Competence Technologies Evidence
End‑to‑End AI Pipelines PyTorch · scikit‑learn · LangChain · TensorFlow Docker hashes · CI logs
Explainability & Fairness SHAP · Grad‑CAM · Fairlearn · Captum PDF reports · Jupyter artefacts
Edge & Cloud Deployment Spring Boot · FastAPI · TensorRT · AWS CDK · GCP · Azure Functions Live demo endpoints
Verification Discipline 82%+ coverage · static analysis · regression test harnesses GitHub Actions · badges
DataOps & MLOps DVC · MLflow · Airflow · Great Expectations Auto-tracked experiments & validation

Tech Stack Mastery

Programming Languages

Python Java JavaScript SQL TypeScript

AI/ML Frameworks

PyTorch TensorFlow Keras scikit-learn XGBoost LightGBM

NLP & LLM

spaCy HuggingFace LangChain Transformers

MLOps & DevOps

Docker MLflow DVC GitHub Actions Airflow Kubernetes

Visualization & UI

Plotly Altair Streamlit

Backend & API

Spring Boot FastAPI PostgreSQL

Vision & Next Horizon

Project Nebula – an open‑source, privacy‑preserving federated‑learning framework coupling on‑device differential privacy with live SHAP explanations.

Objectives 2025 • Deploy on 10 000+ edge nodes across healthcare & civic‑IoT networks
• Cut carbon cost per inference by 40 % via model quantization & smart scheduling
• Public fairness‑drift dashboard updated in real time
• Publish a reproducible white‑paper + dataset cards for peer review

⚙️ Engineering Principles

  • Transparency First – Models must ship with SHAP summaries, feature importances, and audit trails
  • Fail-Safe by Design – Edge deployments have guardrails, prediction boundaries, and exception-resilient fallbacks
  • Versioned Science – All experiments tracked via DVC + MLflow, with exact dependencies and hyperparams
  • Tight Loop Development – Every model and service is regression-tested with >80% coverage before staging
  • Interpretable Pipelines – Every decision boundary is explainable through visual/quantitative techniques
  • Human-in-the-Loop AI – Designs integrate oversight checkpoints into the ML lifecycle

Contact Vector

[email protected]   •   linkedin.com/in/aqib-siddiqui-b954021b9   •   leetcode.com/u/aqib_siddiqui_121201/
Continuous integration of knowledge & infrastructure since 2020.

Pinned Loading

  1. Multi-Disease-Prediction-Using-Ensemble-Learning Multi-Disease-Prediction-Using-Ensemble-Learning Public

    SHAP-powered ensemble ML system (AUROC: 0.94) for multi-disease diagnosis; Streamlit UI

    Python 1 1

  2. BurgerBot BurgerBot Public

    LLM dashboard for German policy documents—translation, summarization, visualization

    Python 1

  3. FairNLP-SHAP-Based-Bias-Detection-in-Multilingual-BERT-Models FairNLP-SHAP-Based-Bias-Detection-in-Multilingual-BERT-Models Public

    Bias analysis in multilingual BERT using SHAP and fairness metrics (EN, DE, HI)

    Python 1

  4. MiniRAG-Streamlit-Q-A-Interface-with-Vector-Search-and-Local-LLMs MiniRAG-Streamlit-Q-A-Interface-with-Vector-Search-and-Local-LLMs Public

    Local PDF Q&A with vector search + LLMs; LangChain powered Streamlit interface

    Python 1

  5. XAI-for-Chest-X-rays-Grad-CAM-Interpretability-on-CNN-Based-Medical-Diagnosis XAI-for-Chest-X-rays-Grad-CAM-Interpretability-on-CNN-Based-Medical-Diagnosis Public

    Grad-CAM + SHAP explanations on ResNet for interpretable chest X-ray diagnosis

    Python 1

  6. YOLO-R-CNN-Vision-Assistant-for-Visually-Impaired-Navigation YOLO-R-CNN-Vision-Assistant-for-Visually-Impaired-Navigation Public

    Edge-deployed assistive vision system with object detection + audio for navigation support

    Python 1