I’m a PhD graduate in Theoretical Physics with a strong background in String Theory. Currently, I’m transitioning to the field of Quantitative Finance and building a strong foundation in Data Science and Machine Learning.
🔍 Projects:
- LLM-powered arXiv Summarizer: A Streamlit project designed to extract and summarize academic papers from arXiv, with integrated LaTeX source extraction.
- Startup Success Analysis: A project focused on analyzing and predicting startup success using a Random Forest classifier in Scikit-learn and BigQuery for data processing and analysis.
- Physics-informed GNN Simulator: Physics-informed neural networks using GNN architecture (TF-GNN) to simulate dynamics of systems with a variable number of particles
💡 Skills:
- Python, Mathematical Modeling, Financial Analytics, Machine Learning, and Deep Learning.
🌱 Currently Learning:
- Expanding my knowledge in Quantitative Finance and Data Analysis to apply cutting-edge techniques to financial markets.
Feel free to explore my repositories, contribute, or connect!