Amirhossein Honardoust

Data Scientist • Machine Learning Engineer • Applied AI Builder

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About me

I build data-centric, explainable, and interactive AI systems, from problem framing and data pipelines to model evaluation, dashboards, APIs, and decision-support tools.

My work focuses on projects where machine learning is not only trained, but also tested, explained, documented, and connected to a real user or business decision.

Main focus areas:

• Retrieval-Augmented Generation and graph-based AI systems
• Risk modeling, fraud detection, underwriting, and decision safety
• NLP systems with responsible evaluation and uncertainty handling
• Synthetic data generation and realism evaluation
• SQL + Python machine learning workflows
• Streamlit/FastAPI apps for interactive model use

Open to: Data Scientist, Machine Learning Engineer, and Applied AI roles, especially projects involving explainability, decision support, and production-minded ML workflows.

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Featured projects

Project	Area	Why it matters
Fake-News-Detector	NLP / Responsible AI	TF-IDF + Logistic Regression style-risk detector with Streamlit, CLI prediction, uncertainty handling, leakage analysis, tests, and CI.
Coffee-Shop-Profit-Predictor	SQL + Machine Learning	End-to-end site-selection workflow with SQL feature engineering, regression modeling, model comparison, candidate ranking, tests, and CI.
Graph-RAG-Engine	RAG / LLM Systems	Explainable Graph + Vector + RAG system with FAISS retrieval, knowledge graph reasoning paths, FastAPI backend, and Streamlit UI.
Synthetic-Data-Artist	Synthetic Data / Generative ML	Research-style comparison of Gaussian Copula and VAE methods with distribution checks, correlation analysis, PCA diagnostics, and visual reports.
Financial-Fraud-Risk-Engine	Fraud Detection / Risk ML	Cost-sensitive fraud detection system with SHAP explanations, threshold optimization, batch scoring, and an interactive dashboard.
Underwriting-Decision-Safety-Lab	Decision Safety / Risk ML	Loan approval safety lab with probability calibration, abstention policies, coverage-quality tradeoffs, triage UI, and data quality checks.

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What I build

Machine learning systems

Projects that move beyond notebooks into repeatable workflows:

• data cleaning and validation
• feature engineering
• train/test evaluation
• cross-validation and model comparison
• uncertainty and threshold handling
• saved artifacts and reproducible outputs
• tests and CI where appropriate

Interactive AI tools

I like building model interfaces that a user can actually interact with:

• Streamlit dashboards
• FastAPI backends
• CLI tools
• batch scoring workflows
• visual reports
• decision-support outputs

Responsible and explainable AI

I try to make model behavior understandable through:

• honest limitations
• model cards and documentation
• leakage analysis
• SHAP and feature importance
• calibration and abstention
• uncertainty bands
• human-review workflows

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Project map

RAG, LLMs, and hybrid AI systems

• Graph-RAG-Engine | Graph intelligence, vector search, and explainable retrieval-augmented generation.
• PR-Guardian-AI | AI-assisted pull request review workflow using GitHub App patterns and OpenAI integration.
• RAG-vs-Fine-Tuning | Practical framework for deciding between retrieval and fine-tuning.
• Designing-Hybrid-AI-Systems | Notes and patterns for hybrid AI design.

Risk, fraud, and decision safety

• Financial-Fraud-Risk-Engine | Fraud-risk workflow with cost-sensitive thresholding and SHAP explanations.
• Underwriting-Decision-Safety-Lab | Calibration, abstention, and defensible loan decision policies.
• Onchain-Security-Suite | Web3 security pipeline with static analysis and deployer risk scoring.

Synthetic data and data realism

• Synthetic-Data-Artist | Copula vs VAE synthetic tabular data comparison and diagnostics.
• Autocurator-Synthetic-Data-Benchmark | Synthetic data benchmarking across fidelity, coverage, privacy, and utility.
• Missing-Data-Doctor | Missingness profiling and imputation impact analysis.

Business ML, forecasting, and dashboards

• Coffee-Shop-Profit-Predictor | SQL + ML workflow for retail location profitability prediction.
• Forecast-Factory | Forecasting and scenario simulation app for business decision support.
• Data-Storytelling-Dashboard | E-commerce analytics dashboard with KPIs, cohorts, retention, and business narrative.
• Market-IQ | BI-style analytics and KPI exploration.

NLP and classic ML

• Fake-News-Detector | Responsible text classification with uncertainty handling and leakage analysis.
• Sentiment-Analysis-BERT | BERT fine-tuning pipeline for sentiment classification.
• ML-Playground-Autodetect | Interactive ML playground with automatic task detection.

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Tech stack

Area	Tools
Languages	Python, SQL, Solidity, MQL5
Data	pandas, NumPy, SQLite, SQLAlchemy
Machine Learning	scikit-learn, XGBoost, LightGBM, joblib
Deep Learning / NLP	PyTorch, TensorFlow/Keras, Hugging Face Transformers, BERT
RAG / AI Systems	FAISS, Sentence Transformers, FastAPI, Streamlit
Visualization	matplotlib, Plotly, Streamlit dashboards
Explainability	SHAP, feature importance, calibration, threshold analysis
Workflow Quality	tests, CI, reproducible outputs, model artifacts, documentation

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Currently improving

• Building more production-style ML projects with tests, CI, and reproducible workflows.
• Improving RAG systems with better retrieval evaluation, traceability, and source grounding.
• Expanding risk and decision-safety projects with calibration, abstention, and monitoring ideas.
• Turning analytics projects into clearer decision-support tools with stronger documentation and outputs.

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GitHub stats

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Contact

• Email: amirhosseinhonardoust@gmail.com
• LinkedIn: honardoust
• X: @amirhonardoust

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AI is not just about models; it is about systems that solve real problems for real people.