A Data Scientist and Ph.D. candidate in mathematical modeling with a strong background in system modeling, physics, and modern machine learning frameworks. I have a bachelor's and a Master's in Physics, and I am proficient in Python (NumPy, SciPy, Pandas, PyTorch, SciKit, Transformers), LLM, SQL, machine learning, deep learning, statistics, high-performance computing, optimization, linear algebra, and data visualization. I have experience with statistical modeling for private equity investments at Adaggio Music Fund and improving data quality and model performance in risk management for hedge fund investments at Pyrite Risk Experts. My thesis focuses on physics-constrained neural networks, particularly solving nonlinear systems of differential equations in epidemiology.
Applied Mathematics, PhD Candidate -
Artificial intelligence, Deep Learning, Physics-Informed Neural Networks, Dynamic Systems.
Physics, MSc - Quantum Field Theory, Quantum Computing.
Physics, BSc - Group Theory.
- Investment Statistical Modeling, Time series forecasting, Classification Models, and Database Modeling.
- Fraud Detection, Prompt Engineering,and Data Gathering.
- [Data Science - Basics]
- Deep Learning - PINNs: Demonstrates my expertise in Deep Learning Frameworks—implementations for scientific computing using PyTorch and TensorFlow.
- Natural Language Processing: This collection represents my efforts to leverage NLP techniques for extracting insights from textual data. Covering a spectrum of applications, from sentiment analysis to language translation, each project is meticulously designed to illustrate the capabilities and practical applications of NLP within real-world contexts.