AI Research Scientist (US)

How do you design an ablation study to isolate which component of a new architecture is responsible for a performance improvement?

I remove or replace one component at a time while holding everything else constant, including training data, hyperparameters, and random seed where possible, and measure the performance delta for each change independently. Changing multiple components at once makes it impossible to attribute the improvement, so the discipline is in testing one variable per experiment even when it's slower.

What's your approach to determining whether a novel result is a genuine improvement or a product of favorable random seed selection?

I run the same experiment across multiple random seeds and report the mean and variance, not a single best run. A result that only appears with one specific seed and disappears across others is not a genuine improvement, and reporting variance alongside the mean is what lets a reader judge whether the effect is real.

How do you evaluate a new algorithm against existing baselines fairly when compute budgets differ across methods?

I match compute budgets across methods wherever possible, since an unfair comparison where the new method got more training steps or a larger search space isn't a valid result. When budgets genuinely can't be matched due to architectural differences, I report the discrepancy explicitly rather than presenting the comparison as apples to apples when it isn't.

How do you decide when a research direction has hit diminishing returns and it's time to pivot?

I track the marginal improvement per unit of research effort over successive iterations, not just whether the metric is still moving in the right direction. When incremental changes stop producing meaningful gains despite reasonable effort, that's the signal to reassess the direction rather than continuing to iterate on the same core idea hoping for a breakthrough.