Pascal Weber

PhD student
Research Group Data Mining and Machine Learning
Faculty of Computer Science, University of Vienna

I am a PhD student in the Research Group Data Mining and Machine Learning at University of Vienna, supervised by Univ.-Prof. Dipl.-Inform. Univ. Dr. Claudia Plant. I am expected to complete my PhD in 2026.

My research focuses on Machine Learning and Data Mining, with particular interest in clustering methods, including deep clustering, density-based clustering, and time-series clustering. I further work on high-dimensional and ordered data, dimensionality reduction, and applications in bioinformatics and sequence analysis.

Machine Learning &
Neural Networks

Research on Machine Learning and Deep Learning, including Deep Neural Networks and Algorithms. Focus on Data Mining and Explorative Data Analysis to discover patterns, structures, and insights in complex datasets.

Unsupervised Learning &
Clustering Methods

Work on Clustering techniques such as Deep Clustering, Density-based Clustering, and Time-Series Clustering, with an emphasis on unsupervised learning methods for structured and unstructured data.

High-Dimensional &
Structured Data

Research on High-dimensional Data and Ordered Data, including Dimensionality Reduction methods to enable efficient analysis, visualization, and learning in complex data spaces.

Bioinformatics &
Sequence Analysis

Application of Machine Learning and Data Mining methods to Bioinformatics, with a focus on String and sequence-based data, enabling pattern discovery and analysis in biological datasets.

Publications

2025	Ultrametric Cluster Hierarchies: I Want ’em All! Andrew Draganov, Pascal Weber, Rasmus Jørgensen, Anna Beer, Claudia Plant, and Ira Assent. Conference on Neural Information Processing Systems. 2025. pp. 1-52. [neurips.cc] [arxiv.org] [code] [reviews] [poster] [slides]	Download [pdf]
2025	sc-GRIP: a Graph Convolutional Approach to Infer Gene Interaction Polarity from Single-cell Data Carolina Elisabeth Atria, Yitao Cai, Pascal Weber, Anna Beer, Nils Kriege, Christian Böhm, Roger Revilla-i-Domingo, and Claudia Plant. International Conference on Data Mining. 2025. pp. 1-10. [code]	Download [pdf]
2024	SHADE: Deep Density-based Clustering Anna Beer, Pascal Weber, Lukas Miklautz, Collin Leiber, Walid Durani, Christian Böhm, and Claudia Plant. International Conference on Data Mining. 2024. pp. 675-680. [ieee.org] [arxiv.org] [code] [slides]	Download [pdf]
2023	CaFe DBSCAN: A Density-based Clustering Algorithm for Causal Feature Learning Pascal Weber, Lukas Miklautz, Akshey Kumar, Moritz Grosse-Wentrup, and Claudia Plant. International Conference on Data Science and Advanced Analytics. 2023. pp. 1-10. [ieee.org] [code] [slides]	Download [pdf]
2022	Deep Clustering With Consensus Representations Lukas Miklautz, Martin Teuffenbach, Pascal Weber, Rona Perjuci, Walid Durani, Christian Böhm, and Claudia Plant. International Conference on Data Mining. 2022. pp. 1119-1124. [ieee.org] [arxiv.org] [code]	Download [pdf]
2021	Coordinate Systems for Pangenome Graphs based on the Level Function and Minimum Path Covers Thomas Büchler, Caroline Räther, Pascal Weber, and Enno Ohlebusch. International Conference on Bioinformatics Models, Methods and Algorithms. 2021. pp. 21-29. [insticc.org] [code]	Download [pdf]
2020	Edge minimization in de Bruijn graphs Uwe Baier, Thomas Büchler, Enno Ohlebusch, and Pascal Weber. Data Compression Conference. 2020. pp. 223-232. [ieee.org] [arxiv.org] [code]	Download [pdf]
2019	On the Computation of Longest Previous Non-overlapping Factors Enno Ohlebusch and Pascal Weber. String Processing and Information Retrieval. 2019. pp. 372-381. [springer.com] [code] [slides]	Download [pdf]