Machine Learning Engineer | ML for Finance
Building data-driven financial systems using machine learning and software engineering.
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Computational Finance & Quantitative Modeling
A structured implementation of core quantitative finance models and methodologies, progressing from foundational stochastic processes through option pricing, volatility modeling, and portfolio optimization. The repository implements Brownian motion and Geometric Brownian Motion simulations, Black-Scholes pricing with Greeks calculation, Monte Carlo methods, GARCH and stochastic volatility models, and mean-variance portfolio optimization with risk parity approaches. Includes rigorous backtesting frameworks and risk metrics (Sharpe ratio, maximum drawdown, volatility analysis) for model validation and performance evaluation. Emphasizes computational implementation of mathematical models describing asset behavior, risk dynamics, and uncertainty quantification in financial systems. The modular architecture supports experimentation with different modeling assumptions and comparative analysis across methodologies. Serves as a foundation for understanding how mathematical frameworks translate to computational finance applications before extending to machine learning techniques.
Machine Learning Portfolio
A curated portfolio of machine learning and AI projects developed across undergraduate and graduate study, with a primary focus on core machine learning, deep learning, and applied model development. The repository spans supervised and unsupervised learning, neural networks, computer vision, time-series modeling, and optimization, with an emphasis on understanding algorithms, implementing models end-to-end, and evaluating performance through experimentation and visualization. Projects range from foundational implementations to applied ML systems built in Python and PyTorch, reflecting a progression from algorithmic fundamentals to more advanced, research-oriented applications.
Spatial Analytics Engine
A research-focused framework for spatial–semantic modeling in whole-slide image (WSI) analysis for computational pathology. Spatial-Semantic Mamba (SS-Mamba) preserves 2D spatial structure and meaningful semantic relationships during patch sequencing and aggregation for Multiple Instance Learning. The framework emphasizes efficient preprocessing, spatially aware sequence construction, and state-space modeling using Mamba architectures, enabling linear-time scalability for large-scale pathology slides. Core components include Python-based preprocessing pipelines, spatially informed sequence construction, and Mamba-based state-space models designed to support experimental evaluation, ablation studies, and visualization. The repository is intended for reproducible academic research, benchmarking, and scholarly review rather than production deployment.