In which I attempt to answer the question: “Please briefly describe your current approach to improving the world”.
I work at a good university in a full time research role in AI for digital diagnostics, and am also doing a PhD in AI interpretability. When my PhD is complete I hope to either stay in academia in a similar research role, or move into industry (which would increase my ability to give). I’m unsure which is the most impactful choice.
It is my hope that my work will lead to a) improved safety through better understanding of deep neural networks, b) the ability to leverage this understanding for knowledge discovery as well as automation, and c) high-impact clinical applications.
Recent work includes:
- A new saliency mapping algorithm which is 20x faster than SoTA and completely model-agnostic, enabling very fast local attribution even for very large inputs or models.
- Using deep learning and interpretability methods to identify previously unknown and highly discriminative morphological features in immune cells from Hoechst stained slides, which should have significant clinical impact as it has potential to decrease immune-profiling costs by an order of magnitude, allowing many more patients to benefit from tailored immunotherapy and more accurate prognosis.
- A broadly applicable neural network training protocol, with which I hope to improve the intrinsic interpretability of DNNs by optimising for feature clustering and branching. (In progress.)