George Weale
I’m a software engineer at Google Cloud AI, where I work on AI agents.
I’m interested in how we tell whether an AI did what we asked, and how to make these systems faster and more reliable.
Outside work, I play cello, record jazz and blues as Copperbelt, and go backpacking. I share photos from my travels here too.
Work
- Now
Google Cloud AI Software engineer
I work on evaluating AI agents and improving their reliability, speed, and running costs.
Agent Development Kit- Strust
Strust
I built Strust to run COBOL programs and their Java replacements on the same tests and compare the results.
Project website
Research
- 2026
Predicting whether an AI agent succeededPDF
State-change features helped one fitted procedure within the original agent cohort, but that gain did not transfer convincingly to the new held-out agent. The study separates learner interaction from reusable predictive value.
Checking whether rewritten code behaves the samePDF
External repair candidates show why a disagreement is not automatically a detected defect. Reviewed contracts and concrete witnesses separate real failures from tolerance and mutation differences.
- 2025
Finding what slowed a program downPDF
Timing-based fault rankings can collapse when a clean span shifts. New controlled runs expose the timing boundary, partial repairs and the limits of an alert threshold learned from only two controls.
Strust: comparing COBOL and JavaPDF
Two correct implementations can agree in the wrong replay state. Executed transaction histories expose four such cases, while a cached-output baseline shows why slicing adds work for this unchanged-pair validation task.
Choosing which lipid nanoparticles to testPDF
I compared models on matched candidate pools and testing budgets across unseen publications. Tie handling and uncertain readout directions limit the screening claim; source spreadsheets reconstruct 60 retained candidates.
- 2024
How far ahead can we predict a chaotic system?PDF
With the same four noisy positions and known energy, physical state inference predicted near-term escape much better than the learned models. Independent integration also exposed errors in the later numerical event labels.
Which sensors does a distillation column need?PDF
Two measurements can distinguish reflux from feed-composition changes yet estimate them very poorly. A component model, matched-noise fits and sensitivity directions explain why instrument placement matters.
- 2023
Do fault-detector rankings hold up?PDF
I repaired a Bayesian detector’s recurrence and checked it against exact small-case calculations. Replaying the coherent detector on matched test runs retained its large recall deficit, with unequal realized alert burdens.
When prices change, does the best process change?PDF
Published break-even prices and investment values imply incompatible production factors under the stated accounting boundary. The audit identifies that inconsistency without guessing a corrected process result.
- 2022
Choosing where to reuse heatPDF
A lower heat-demand score can still require more supplied heat and cost. I checked that reversal across alternative dispatches and independently reconstructed the retained designs’ training feasibility.
Writing
Education
Columbia University2024-2025MS in Machine Learning
UC Santa Barbara2020-2024BS in Chemical Engineering
BS in Statistics and Data Science