AI / Machine learning / Mathematics

We pick hard problems and run experiments on them.

Maskana Labs is a small AI, machine learning and mathematics house. We formalise a problem, try ideas against it, and keep what survives contact with the evidence.

01 - What we work on

Three disciplines, one habit: ask a sharp question and test it.

f(x; theta)

Machine learning

Models, training dynamics and evaluation. We care about why something works as much as whether it does.

for all e, exists d

Mathematics

Optimisation, probability and structure. Clear statements and proofs where we can get them, careful numerics where we can't.

agent -> world -> agent

Artificial intelligence

Systems that reason, plan and act. We build small, inspectable versions first and scale only what earns it.

02 - How we work

Problem first, method second.

01

Pose

Write the question down in a sentence anyone on the team can challenge.

02

Formalise

Turn it into something precise: an objective, a metric, a conjecture, a baseline to beat.

03

Experiment

Run the smallest test that could prove us wrong. Cheap, fast, and many of them.

04

Check

Prove it, ablate it, or reproduce it from scratch. Results that only work once don't count.

05

Write up

Record what we tried, what failed and what held, so the next experiment starts further along.

03 - Lab notebook

Experiments, in the open.

2026-10-11
Monte-Carlo-VarianceDoes variance reduction change Monte Carlo's 1/√N error rate, or only its constant?
Complete
2026-10-11
Double-DescentDoes test error keep rising past the interpolation threshold, and does ridge regularisation tame it?
Complete
2026-10-11
Optimizer-LandscapesHow sensitive are gradient descent, momentum and Adam to learning rate, and does that depend on the landscape?
Complete
04 - Contact

Got a problem worth chewing on?

Tell us what you are stuck on, or what you wish someone would test. We read everything.

hello@maskanalabs.com