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21 projects

The Thoughts

  1. behavior information modelingJAN 2026For thirty years we've been getting very good at Building Information Modeling: every beam, every duct, every clash, tracked to the millimeter.
  2. neuroaestheticsDEC 2025Neuroaesthetics is the unglamorous name for a question everyone already feels: why does one room settle you and another keep you on edge?
  3. physics solversAPR 2026A physics solver is the most honest collaborator I have: it doesn't care what I intended, only what I actually built.
  4. generative aiJUN 2024Generative AI made the inspired gesture free. Anyone can produce a thousand renders before lunch, and a render is a promise, not a product.
  5. extended realitySEP 2022I started in a lab strapping headsets onto students to see whether a lesson lands harder when you can walk around inside it.
  6. comfort as dataMAY 2026You don't walk into a room and average your experience. You walk in and the thing that's wrong is the thing you notice: the glare, the echo, the cold draft on the back of your neck.
  7. drawing as interfaceMAR 2026For most of my life the drawing was the output. You designed the thing, then you drew it to prove it existed.
  8. evolutionary searchNOV 2025Evolutionary search is what I reach for when a problem is too tangled to solve head on: describe what good looks like, then let a population of designs breed, mutate, and compete until something clever falls out.
  9. heritage meets new techAPR 2026The usual choice with an old building is a trap: treat it as a museum piece no one may touch, or drop a glass-and-steel box in the middle and call it modern.
  10. buildings that respondJUN 2026Right now a building is a fixed guess. Someone chose the ceiling height and the window size years before you arrived, and you live inside their averaged assumptions whether they fit you or not.
  11. connecting the dotsJUN 2020When the art class was asked who here dislikes drawing, mine was the only hand that went up. Awkward.
  12. pelagñouJUN 2026Some projects hand you a tool; Pelagñou handed me a library card.
  13. explaining thingsJUL 2026How do you explain a complex, honestly boring technical subject so someone leans in instead of glazing over?
  14. adjacency is not accessJUN 2026A floor plan tells you which rooms touch. It will not tell you which rooms you can actually reach, and the gap between those two drawings is where the architecture is hiding.

Encoding Urban Risk

RESEARCH

Can we predict crime from urban features? A machine learning test.

Street shape alone predicts crime poorly. Saying precisely where the certainty ends was the most honest thing the pipeline produced.

MACAD MACHINE LEARNING · TEAM OF 4

PYTHON · OPENSTREETMAP · RANDOM FOREST · SHAP · TRAINED · ~36,000 STREET SEGMENTS

BLOG (opens in new tab)
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WHAT

An applied machine-learning pipeline that classifies street segments into low, medium, and high risk from morphological features: connectivity, visibility, enclosure, proximity to transit. It trains on roughly 36,000 London street segments and stands on the urban safety literature, Jacobs to Space Syntax, encoded as measurable features. A team of four, all hands on everything: María Sánchez Domínguez, Charles Abi Chahine, Lakzhmy Mari Zaro, and me.

WHY

You cannot design collective efficacy, but you can design a street. If public map data carries any signal about safety, the people shaping streets should know how much, and how much is none.

HOW

  1. Encode each street segment from OpenStreetMap into spatial features grounded in the safety literature.
  2. Test the model family honestly: regressions, decision trees, random forests, clustering, and a Kohonen map, with SHAP explaining every prediction.
  3. Wrap the pipeline in a usable assessment interface, so a neighborhood can be scored without opening a notebook.

WHAT CAME OF IT

Spatial form alone predicts crime poorly; crime is social and economic before it is geometric. What the features do organize is coherent street typologies that transfer across cities. Being precise about where the uncertainty begins is the most honest contribution the project makes.