Developing optimized open-source software for gravitational wave detection algorithms to leverage Apple Silicon architecture as a cost-effective alternative to traditional GPU computing.

• Optimizing Fast Fourier Transform implementations to exploit Apple M-series unified memory architecture for gravitational wave signal processing

• Benchmarking Apple Silicon GPU performance against NVIDIA GPUs for compute-intensive astrophysical algorithms

• Developing scripts and infrastructure to run PyCBC gravitational wave detection algorithms more efficiently on Apple hardware

• Building cost-performance analysis comparing Apple Silicon vs traditional HPC solutions for scientific computing

• Contributing optimized implementations to open-source PyCBC framework for LIGO data analysis

Investigating spectral properties of high-dimensional covariance matrices using random matrix theory (Marchenko-Pastur

distributions, Tracy-Widom statistics) to develop robust eigenvalue estimators for portfolio risk models with finite-sample

corrections

• Implementing Principal Component Analysis, Kernel PCA, and Sparse PCA for dimensionality reduction in correlated

financial systems; applying eigenvalue shrinkage methods (Ledoit-Wolf, Oracle Approximating Shrinkage) to improve

covariance estimation

• Developing stochastic simulation frameworks in Python to model signal extraction from noisy covariance structures;

extending to factor models, anomaly detection in time series, and high-dimensional hypothesis testing

Front and Backend Web Development

Engineered full-stack social media web application in 8-week agile sprint using Flask backend with MySQL database,

implementing RESTful API endpoints for user authentication, post creation, and real-time event feeds

• Architected content-based recommendation engine using TF-IDF vectorization and cosine similarity for personalized event

discovery, integrating collaborative filtering to achieve 35% increase in user engagement