News - Data Science Chair

Our paper "CompTrails: comparing hypotheses across behavioral networks" has been published in the DAMI Journal and is open accessible.

At the 4th international workshop on advanced neural algorithms and theories for recommender systems we won the award for the best Paper with "Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages".

The MAGNET4Cardiac7T Project has been granted 40.000 GPU-hours in the Erlangen National High Performance Computing Center for training several deep learning models for simulating electromagnetic fields.

The dataset of 1TB has been published in our University's research repository and is available for download.

In this joint work with the Chair of Human-Computer Interaction (LS IX) , we introduce a new dataset 'Who is Alyx?' with behaviometric and biometric data from VR users to address a critical issue in this field, which is designed for investigating user identification based on motion in XR environments.

We, the Data Science Chair, moved to the new CAIDAS building at Emil-Fischer-Straße 50. You can find us and our new student pools on the third floor. This move concentrates the AI chairs to support our active research. You are cordially invited to visit us at our new location.

Professor Andreas Hotho takes on the role of Editor-in-Chief at "Transactions on Graph Data and Knowledge (TGDK)," together with Professor Ian Horrocks, Ph. D. Lalana Kagal, and Professor Aidan Hogan. TGDK is a new Diamond Open Access journal diving deep into graph-based abstractions for data and knowledge.

In our paper "Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages" we investigate the utility of non-item pages in transformer based recommender models for next item prediction and show how information from pages like category pages or search results can be leveraged.

In our paper "Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks "" we introduce the Liquor-HGNN model, a novel approach for detecting and localizing leaks in drinking water distribution networks (DWDNs) through the utilization of heterogeneous graph learning.

In our paper "Can Neural Networks Distinguish High-school Level Mathematical Concepts?" we evaluate the ability of various neural networks to classify the relationship between two mathematical expressions. For example, two expressions can be equivalent or one could be a derivative of the other. We show that neural networks are able to learn the underlying mathematical patterns and outperform rule-based systems.