Andrew Ilyas

Andrew Ilyas, a PhD student at MIT soon to be a professor at CMU, dives deep into the fascinating world of machine learning. He explains how datasets influence model predictions and why adversarial examples are crucial features rather than mere bugs. The discussion spans the complexities of robustness, black box attacks, and biases in data collection, especially in the ImageNet dataset. Ilyas also shares innovative solutions to self-selection bias and his ambitious future research plans in the field.