I am a PhD candidate working on the Graph-based Signature Verification project. In this project, graph-based methods are applied to the topic of signature verification. Additionally, we combine the graph-based approaches with deep learning methods in multiple classifier systems.
The focus of my PhD is on developing deep learning methods for analysing historical document image datasets. As part of this, I work on classification, representation learning and training strategies that improve performance and reduce training time for deep neural networks. I'm also interested in ensuring reproducibility for deep learning experiments, so I helped develop and continue to work on DeepDIVA, a PyTorch based framework for reproducible deep learning experimentation.
I am working on developping novel graph-based and deep learning methods. My main application is in digital pathology, where I aim to develop tools that allow physicians to become faster and more accurate with their diagnosis.
This graph captures the interaction of lymphocytes and tumor buds
Intestinal Gland Classification using Graph Edit Distance (GED)