Full Stack Developer at IBM, deeply passionate about building cloud-native AI systems grounded in solid mathematical and algorithmic foundations. My work sits at the intersection of theoretical computer science and systems engineering — from spectral graph theory and discrete Fourier transforms (DFT/FFT) to distributed APIs, foundation models, and generative AI workflows. I’m particularly interested in: • Full stack system design (Java, Node.js, REST APIs) • Applied cloud systems (GCP, watsonx, AWS) • Algorithm design & complexity theory • Spectral methods (Laplacian eigenmaps, circulant matrices) • Fourier-based reasoning in ML (signal, graph & attention) Influenced by the clarity of MIT's 6.006 and 6.046 thinking, I aim to build systems that are not only functional, but mathematically elegant. Currently exploring how linear algebra meets learning — via DFTs, PCA.