I'm working towards graduate research in trustworthy AI and ML security, reproducing papers I want to understand, and writing up what I learn along the way. I want AI systems that are more transparent and evaluable, and I want to be part of the work that gets us there.
Applying for graduate study in trustworthy ML, starting Fall 2027. If you're working on this, I'd love to hear from you! Always looking to collaborate on interesting projects. :)
Recent writing
All posts →- Recovering confidence from labels alone 3/6
Part 2 got us a baseline. Now we ask the model about wobbled copies of a point, and use how reliably it still gets them right as a stand-in for confidence. It beats the baseline, though the obvious way of counting the answers does worse than not augmenting at all.
- The gap attack, and some ML fundamentals 2/6
To build the gap attack I first had to learn how to train a neural network, so this post is mostly the ML setup (CNNs, tensors, loss, gradient descent), with the working attack at the end.
- What is a membership inference attack? 1/6
The first in a series cataloguing my journey reproducing papers I want to properly understand. Starting with the core idea and vocabulary of membership inference attacks.