Designing Intelligent Systems
Exploring how mathematics, machine learning, and software engineering can be combined to create intelligent, human-centred systems, with a focus on computational geometry, recommendation systems, computer vision, and scalable software design.
Academic Background & Goals
I currently study Digital Product Architecture at Thomas More, focusing on the intersection of software engineering, UX systems, and emerging technologies. My academic journey is driven by a desire to understand not just how systems look, but how they perform, scale, and interact with complex human behaviours.
Previously, I worked in fintech and blockchain integration at Kin, a Canadian messaging platform. This role exposed me to real-world distributed systems, performance optimization, and scalable API design in production environments.
I am transitioning towards formal academic research in Computer Science and Artificial Intelligence, with active interests in Machine Learning systems, Human-Computer Interaction (HCI), Computational Geometry, and Intelligent Decision Systems.
COMPUTATIONAL
LEARNING LOG
The Computational Learning Log documents my active exploration of computer science fundamentals, mathematical foundations, and system architectures. It serves as an academic record of self-directed research, progression, and practical insights gained during my studies.
Vector Spaces & Embeddings
u · v = ∑ ui vi = ||u|| ||v|| cos θExploring high-dimensional vector representations, inner product spaces, and coordinate projections that form the spatial architecture of user recommendation models.
Stochastic Gradients & Descent
θt+1 = θt - η ∇θ L(θt)Analyzing gradient fields, Hessian structures, and stochastic processes to optimize neural network cost manifolds through backpropagation systems.
Entropy & Divergence Geometry
DKL(P || Q) = ∫ℝ p(x) log(p(x)/q(x)) dxStudying information theoretic distances, probability distributions, and Kullback-Leibler measures to align model likelihood representations with real data.
Graph Structures & Networks
G = (V, E) ⟹ A ∈ ℝ|V| × |V|Modeling relational architectures using topological networks, adjacency matrices, and connectivity trees to study message passing in graph neural systems.
COMPUTATIONAL EXPERIMENTS
RESEARCH DOMAINS
I investigate how computer science, machine learning, and mathematics can be synthesised to build intelligent, human-centred systems. My research spans convolutional networks, high-dimensional vector spaces, WebGL graphics, and adaptive user state machines.
BEHIND
theRESEARCH DOMAINS
I investigate how computer science, machine learning, and mathematics can be synthesised to build intelligent, human-centred systems. My research spans convolutional networks, high-dimensional vector spaces, WebGL graphics, and adaptive user state machines.
I explore the intersection of theory and application using Python, TypeScript, PyTorch, and Next.js. My academic trajectory is focused on constructing software architectures grounded in mathematical reasoning and computational geometry.