About
I’m Carlos David González Vargas, a computational designer, architect, programmer, and AI engineer with a Master of Science in Artificial Intelligence. I was born in Cartagena and grew up in Bogotá, where I studied architecture and first became interested in connecting design with code. What started as a challenge at university became a genuine curiosity. I learned programming, later became a teaching assistant for introductory programming, and gradually began bringing computation into the way I designed, analyzed, and built projects. After working across Colombia for several years, I’m now based in Boca Raton, Florida, while keeping a strong connection to my Colombian and Caribbean roots.
Challenges are what keep me moving. I enjoy the satisfaction of taking on a problem that does not have an obvious solution, especially when it requires crossing disciplines or learning something new along the way. That curiosity has taken me through architecture, live experiences, creative technology, programming, and artificial intelligence, often combining several of them within the same project. Outside of work, music is a constant part of my life, along with film, art, and a long-standing fascination with science fiction. Those interests inevitably find their way into how I think about technology, spaces, experiences, and the futures we imagine.
Computational Design for Real-World Systems
The common thread across my work is the belief that creative and technical thinking become more powerful when they are allowed to work together. Architecture and design can be deeply technical, just as programming is fundamentally creative. Computational design gives me a way to build bridges between those worlds, creating systems that help us explore more complex ideas, test possibilities, and translate them into something that can actually be produced.
Artificial intelligence is part of that toolbox, not a replacement for the thinking behind the work. I’m especially interested in the algorithms, data, and information-processing systems behind AI, and in how our own datasets can guide models toward specific and useful results. I don’t see AI as the solution to every problem, or as something that replaces creative practice. I see it as another powerful tool that, when understood and directed carefully, can expand what a project is capable of.
Why work with me
Interdisciplinary
Architecture, design, code, and AI come together in the way I approach each project.
I can move between spatial thinking, visual design, programming, data, and technical development without treating them as completely separate disciplines. This lets me understand a project at different scales and build stronger connections between the initial idea and the final result.
Buildable
I translate ideas into workflows, models, and real-world systems that can actually be produced.
A concept is only useful if it can survive the constraints that come after it. I think about fabrication, documentation, performance, materials, production, and implementation while the design is still being developed.
Effective
I move across design, programming, visualization, and production to solve complex challenges.
I use the tools that make sense for the problem rather than forcing every project into the same process. Sometimes the answer is a parametric model, sometimes code, an AI workflow, a drawing set, a prototype, or a combination of all of them.
My workflow
- 01
Discovery
Understanding the goals, context, constraints, and the real problem behind the project.
Before designing anything, I try to understand what actually needs to be solved, who is involved, what already exists, and which limitations will shape the project.
- 02
Concept
Translating the brief into a clear spatial, visual, or computational direction.
This is where the project begins to take form. The objective is to establish a strong idea without deciding too early exactly how it has to be built.
- 03
Research
Studying references, materials, technologies, and systems that can support the idea.
Depending on the project, this can include precedents, fabrication methods, datasets, software, technical documentation, AI models, or physical constraints.
- 04
System Design
Defining the rules, parameters, data, geometry, and workflows behind the project.
Instead of designing only the final result, I often design the system capable of producing it. This makes complex projects easier to modify, coordinate, test, and scale.
- 05
Iteration
Testing ideas through sketches, models, code, AI tools, and real-time experiments.
Different possibilities are tested quickly so decisions can be based on how the system actually behaves rather than on assumptions.
- 06
Development
Building the selected direction into a functional design or computational solution.
Once the system is defined, the work becomes more precise. Geometry, code, interactions, interfaces, documentation, and technical requirements are developed together.
- 07
Optimization
Improving performance, fabrication logic, clarity, interaction, and visual impact.
This is where unnecessary complexity is removed and the system is refined for the conditions in which it will actually operate.
- 08
Production
Preparing the files, models, drawings, animations, documentation, and assets needed to make it real.
For physical projects, this may connect directly with fabrication and installation. For digital work, it can mean preparing the system for implementation, testing, and deployment.
- 09
Deployment
Bringing the system into real-world use, installation, presentation, or experience.
The final step is seeing how the work performs outside the design environment and making sure the original idea survives contact with reality.