MSc Artificial Intelligence candidate at the University of Verona, building practical projects in Python, data pipelines, software development, and applied AI. Currently strengthening my backend and data engineering skills through hands-on project development.
- Python software development and data pipelines
- Backend development fundamentals and HTTP
- SQL and relational databases
- Data processing, analytics, and visualization
- Git/GitHub and version-control workflows
- Docker and reproducible development environments
- Machine Learning and Deep Learning
Programming
Python
Backend & Data Fundamentals
HTTP · Requests · SQL · Relational Databases · Web Scraping · Beautiful Soup
Data & Machine Learning
Pandas · NumPy · scikit-learn · TensorFlow · PyTorch · Matplotlib · Streamlit
Engineering Tools
Git · GitHub · Docker · Apache Kafka
Modular Python pipeline for collecting, parsing, normalizing, and storing financial-market data from public web sources.
Built with Python, Requests, Beautiful Soup, SQLite, pathlib, and pytest. Includes timestamped raw-data snapshots, locale-aware normalization, JSON/CSV exports, idempotent database inserts, and indexed historical queries.
Repository: Gaszajoo
End-to-end streaming data pipeline for computing and visualizing the Effective Drought Index (EDI) from daily precipitation data.
Built with Python, Kafka, Parquet, Pandas, Docker, and Streamlit.
Includes streaming ingestion, windowed processing, curated analytics-ready datasets, dashboard filtering, trend visualization, class summaries, and basic latency monitoring.
Repository: streaming-data-pipeline-dashboard
Python codebase for pricing American-style path-dependent contracts using Longstaff-Schwartz Monte Carlo (LSMC).
Includes Monte Carlo simulation, payoff functions for Asian and Lookback contracts, feature engineering, regression-based continuation value estimation, and signature features.
- American-Style Option Pricing Engine — MSc thesis project implementing Longstaff–Schwartz Monte Carlo for path-dependent American options, with polynomial, ridge, and signature-based continuation-value models.
LSTM-based volatility forecasting and SARSA reinforcement-learning trading agent for intraday financial decision-making experiments.
Repository: ai-finance-trading-agent
Streamlit app for simulating random-weight portfolios, computing expected return, volatility, and Sharpe Ratio, and visualizing the efficient frontier and optimal allocation.
Repository: portfolio-optimizer
I am currently focused on strengthening my profile for junior technical roles, especially in:
- Python backend / backend-data roles
- Data and analytics engineering
- AI-enabled software
- Technical consulting and implementation roles
- LinkedIn: linkedin.com/in/saman-dadkhah
- GitHub: github.com/SamanDadkhah