CV
Education
- Ph.D in Machine Learning, University of Mannheim (Completed May 2025)
- Focus: Machine Learning, Tabular Data, Tree-Based Models, Explainable AI
- M.S. in Business Informatics (Data Science Track), University of Mannheim, 2019
- B.S. in Business Informatics, University of Mannheim, 2017
Work experience
- 05/2025-now: Assistant Professor (Akademischer Rat)
- Technical University of Clausthal
- Topics: Deep Learning for Tabular Data, Tree Ensemble Methods, Explainable AI
- 10/2024 - 03/2025: Adjunct Lecturer
- Technical University of Clausthal
- Lecture: “Grundlagen der Künstlichen Intelligenz” (Introduction to AI)
- 09/2019-05/2025: Scientific Researcher and PhD Candidate
- University of Mannheim
- Topics: Deep Learning for Tabular Data, Tree Ensemble Methods, Explainable AI
Skills
- Technical Skills
- Deep Learning (TensorFlow, PyTorch, JAX)
- Predictive Modeling
- Computer Vision
- Reinforcement Learning
- Time Series Forecasting
- Programming
- Python
- R
- Java
- C/C++
- Languages
- German (native)
- English (Fluent)
- Italian (Conversational)
Publication List
First Author Publications
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
Sascha Marton, Tim Grams, Florian Vogt, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
ICLR 2025 (Spotlight)
Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory
Sascha Marton, Moritz Schneider, Jannik Brinkmann, Stefan Ludtke, Christian Bartelt, Heiner Stuckenschmidt
ICLR 2025 Workshop on New Frontiers in Associative Memories
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
ICLR 2024
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
AAAI 2024 (Oral)
Explaining neural networks without access to training data
Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
Machine Learning Journal (2024)
Explanations for Neural Networks by Neural Network
Sascha Marton, Stefan Lüdtke, Christian Bartelt
Applied Sciences (2022)
Further Publications
DCBM: Data-Efficient Visual Concept Bottleneck Models
Katharina Prasse, Patrick Knab, Sascha Marton, Christian Bartelt, Margret Keuper
ICML 2025
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models
Patrick Knab, Sascha Marton, Christian Bartelt
ECAI 2025
Disentangling Exploration of Large Language Models by Optimal Exploitation
Tim Grams, Patrick Betz, Sascha Marton, Stefan Lüdtke and Christian Bartelt
ECAI 2025
Which LIME should I trust? Concepts, Challenges, and Solutions
Patrick Knab, Sascha Marton, Udo Schlegel, Christian Bartelt
XAI 2025
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
Andrej Tschalzev, Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
NeurIPS 2024
Interpreting Outliers in Time Series Data through Decoding Autoencoder
Patrick Knab, Sascha Marton, Christian Bartelt, Robert Fuder
ECML-PKDD 2024 Workshop on Explainable AI for Time Series and Data Streams
Bias mitigation for large language models using adversarial learning
Jasmina S Ernst, Sascha Marton, Jannik Brinkmann, Eduardo Vellasques, Damien Foucard, Martin Kraemer, Marian Lambert
ECAI 2023 Workshop on Fairness and Bias in AI