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In my PhD I focus on analyzing deep neural networks learning behaviour while working on computer vision tasks, such as historical documents image analysis.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The focus of my PhD is on developing deep learning methods for analysing historical document image datasets.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In my PhD project I am working on historical document analysis, especially segmentation algorithms, and on the reproducibility or their result.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 

 

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Intestinal Gland Classification using Graph Edit Distance (GED) 

 

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