I'm a Data Scientist with a passion for NLP, Time Series, GNN. I hold a Master's degree in Statistics and Data Science, with a thesis focused on the optimization of dynamic transportation networks through the application of genetic algorithms. I have experience building end-to-end machine learning pipelines and scalable AI solutions. I'm open to collaborations and opportunities in the field of Data Science. Feel free to reach out to me!

Check out my Medium articles.

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• Frameworks & Tools:

  

• Project Management System with RAG: An AI-driven project management system using Retrieval-Augmented Generation (RAG) for enhanced efficiency in task execution, team collaboration, and decision-making. Read the article on Medium.
• VisualInsight: An app leveraging Google Generative AI (Gemini) to analyze images. This Streamlit-based web application allows users to upload images for analysis, storing both the original images and results in Amazon S3. Read the article on Medium.
• APDTFlow: A Modular Forecasting Framework for Time Series Data: APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily extend the framework for new methods. With over 24,000 downloads, APDTFlow is actively used by the time series forecasting community. Read the article on Medium.
• Bot Chat DeepSeek:end-to-end pipeline of fine-tuning a Large Language Model (LLM) on AWS. This repository demonstrates how to prepare a custom dataset, fine-tune a language model, deploy it on AWS SageMaker, and interact with it via a Flask-based API.
• Football Scout RAG: An AI-powered football scouting agent designed to scrape data from Transfermarkt, providing advanced player statistics and comparisons.
• LLM-Code-Review 🤖: An automated code review tool powered by Large Language Models (LLMs), integrated as a GitHub Action. It provides real-time code analysis on every pull request, offering detailed feedback on security risks, performance bottlenecks, and code quality—right in your pull requests. No more waiting for reviews or missing critical issues.