PhD Projects

Marcel Gygli-Würsch

In my PhD I focus on building an Open Source framework that allows DIA researchers to perform easier experiments with state-of-the-art methods as Web Services. I am the main developper of DIVAServices.

Paul Märgner

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.

Michele Alberti

In my PhD I focus on analyzing deep neural networks learning behaviour while working on computer vision tasks, such as historical documents image analysis. I am also a developper of DeepDIVA.

Vinaychandran Pondenkandath

The focus of my PhD is on developing deep learning methods for analysing historical document image datasets.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.

Lars Vögtlin

In my PhD project I am working on historical document analysis, especially segmentation algorithms, and on the reproducibility or their result. I am also a co-developper of DeepDIVA.

Linda Studer

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)