Mathematical Engineering & Artificial Intelligence Student | Quantitative Finance | Investment Analytics | Machine Learning | Optimization
I am studying Mathematical Engineering and Artificial Intelligence at ICAI, with a growing focus on quantitative finance, investment banking, financial data analytics, and model-driven decision-making.
My work combines machine learning, mathematical optimization, NLP, computer vision, and applied software engineering. I am especially interested in how statistical modeling, portfolio construction, backtesting, risk analysis, and automated research pipelines can support better investment and corporate-finance decisions.
- Quantitative finance, portfolio construction, and algorithmic investment strategies
- Investment-banking analytics: valuation support, market screening, comparable analysis, and financial research
- Machine learning for prediction, classification, and decision support
- Optimization under real-world constraints
- NLP for information extraction, entity recognition, and alert generation
- Dashboards, reporting, and analytical storytelling
| Project | Description | Stack |
|---|---|---|
| Portfolio & Investment Research | Ongoing work focused on online portfolio selection, backtesting, transaction costs, risk metrics, and realistic allocation constraints. | Python Pandas NumPy Finance |
| Strength Training Optimizer | Personalized optimization model built with Pyomo to allocate training volume across goals, constraints, and session limits. Although applied to training, the core logic is constrained allocation, which is directly related to scheduling, resource allocation, and portfolio optimization. | Python Jupyter Notebook Pyomo Optimization |
| Discrete Mathematics Coursework | Coursework covering modular arithmetic, RSA cryptography, and graph-based GPS routing, with useful foundations for algorithms, networks, routing, and quantitative problem solving. | Python Algorithms Graph Theory Cryptography |
| Project | Description | Stack |
|---|---|---|
| Football Commentary NLP | End-to-end NLP pipeline for football match reports: sentiment/outcome prediction, named entity recognition, optional OCR, and alert generation. The project shows how unstructured text can be transformed into structured signals, a useful pattern for news analytics and financial text processing. | Python NLP NER Sentiment Analysis OCR |
| Student Performance Grade Prediction | Regression-based machine learning project for predicting students' final grades from academic-performance features, focused on model comparison, feature analysis, and prediction quality. | Python Jupyter Notebook Scikit-learn Pandas |
| Project | Description | Stack |
|---|---|---|
| Computer Vision Final Project | Real-time OpenCV system combining a visual password workflow with a two-player Snake game controlled by colored markers and Kalman-filtered tracking. | Python OpenCV Kalman Filter |
| Clover Pit | Unity slot-machine game with weighted paylines, store upgrades, adaptive difficulty, and physics-based lever interaction. | Unity C# ShaderLab |
Alongside the public repositories on this profile, I have worked on academic and team-based projects involving:
- supervised learning and predictive modeling,
- NLP pipelines for classification, entity extraction, and alert generation,
- computer vision systems using OpenCV,
- mathematical optimization with Pyomo and Gurobi,
- distributed computing experiments with Dask, Spark, and AWS EMR,
- deep learning exercises with PyTorch,
- statistical analysis, dashboards, and visualization.
Some projects were developed in private, shared, or university repositories, so this profile includes cleaned public versions where possible.
