Summary#

Mathematics minor providing rigorous foundation in linear algebra, statistics, and numerical methods. This mathematical depth enables implementing algorithms from scratch rather than treating libraries as black boxes—a key differentiator for understanding and optimizing ML and graphics systems.

How I Apply This Skill#

• Completed Mathematics minor alongside Computer Science degree
• Implemented SVD, QR factorization, eigenvalue analysis from scratch in R
• Built Echo State Network requiring spectral radius and reservoir dynamics
• Applied PCA for dimensionality reduction on socioeconomic datasets
• Created Markov chains and graph algorithms using probability theory
• Implemented transformation matrices for computer graphics pipelines

Key Strengths#

• Linear Algebra: Matrix decomposition, eigenvalues, vector spaces, transformations
• Statistics: Regression, hypothesis testing, probability distributions, PCA
• Numerical Methods: Gram-Schmidt orthogonalization, iterative algorithms
• Graph Theory: Shortest path algorithms, network analysis, Markov processes
• Calculus: Optimization, gradient methods, continuous mathematics

Related Projects#

• Singular Value Decomposition - Matrix decomposition from scratch
• Gram-Schmidt & QR Factorization - Orthogonalization algorithms
• Echo State Network - Spectral radius and reservoir dynamics
• Markov Chains - Probability and state transitions
• Graphics Pipeline - Transformation matrices