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21 projects

The Thoughts

  1. explaining thingsJUL 2026How do you explain a complex, honestly boring technical subject so someone leans in instead of glazing over?
  2. 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.
  3. 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.
  4. experiences are dataMAY 2026You learned what a room does to you by being in rooms. Nobody sat you down with a dataset.
  5. 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.
  6. when the tool scores peopleAPR 2026I build things that give a room a score. The obvious next question, and the one I would ask me, is what happens the day somebody uses that score to decide who gets the room.
  7. latent spaceAPR 2026Machine learning borrowed the word space from us, and I would like to check what it did with it.
  8. 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.
  9. 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.
  10. heritage meets new techDEC 2025The 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.
  11. neuroaestheticsDEC 2025Neuroaesthetics is the unglamorous name for a question everyone already feels: why does one room settle you and another keep you on edge?
  12. 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.
  13. physics solversNOV 2025A physics solver is the most honest collaborator I have: it doesn't care what I intended, only what I actually built.
  14. computationOCT 2025Computation is not the same as using a computer, and separating the two took me longer than I would like to admit.
  15. what an llm actually isSEP 2025A large language model is a machine that guesses the next word. That is not me being kind to you, that is the whole thing.
  16. generative aiDEC 2023Generative AI made the inspired gesture free. Anyone can produce a thousand renders before lunch, and a render is a promise, not a product.
  17. 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.
  18. connecting the dotsJUN 2020When the art class was asked who here dislikes drawing, mine was the only hand that went up. Awkward.

Encoding Urban Risk

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

TYPEComputation & ResearchTEAMMACAD MACHINE LEARNING · TEAM OF 4STACKPYTHON · OPENSTREETMAP · RANDOM FOREST · SHAPLINKSBLOG (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.