Currently, I am a Senior Machine Learning & Big Data Engineer at inovex, where I bridge the gap between advanced analytics demand and enterprise-grade data platforms. My work focuses on building resilient, high-quality data ecosystems that deliver actual business value.
I specialize in the lifecycle of Data Quality, ensuring that both analytics and AI solutions are built on a foundation of integrity and maintainability. My core areas of expertise include:
- Data Governance & Master Data: Optimizing data structures for ERP systems and ensuring seamless integration with business processes and AI tools. Helping SAP customers navigate the complexities of data quality and AI integration.
- Strategic Consulting and Data Engineering: Advising on maintainable system architectures at both the technical and organizational levels that match the core business needs. Introducing Data Mesh concepts to help with ownership and scalability of data products. Implementing data quality frameworks and best practices into the modern data platforms (currently focused on Snowflake).
I finished my PhD with Prof. Wolfgang Maass at the Institute of Machine Learning and Neural Computation at Graz University of Technology (TU Graz), working on biologically plausible working memory in spiking neural networks. At the same time I was freelance Deep Learning Consultant for the automotive industry at Virtual Vehicle.
I still enjoy doing a bit of research through suppervision of master theses and occasional publications. See the Supervision section below for more details.
For current list of my publications, visit my google scholar page.
In a previous life, I was working as a full-stack (django) developer at Institute of Human-Centred Computing (HCC).
Detailed resume available on request.
Activity
- [post] Exploiting Foundation Models for Improved Language Identification from Speech
- [post] Privacy challenges of Entity Matching and Record Linkage
- [post] Similarity search and Deduplication at scale
- [post] Pruning and Sparsification of Neural Networks
- [talk] Deep Learning, Neuroscience and the future of AI @ Berlin Buzzwords [video]
- [post] All about Positional Encoding
- [post] How to migrate binary bloat out of git repository
- [talk] inovex 2020 TechDay: Brain-inspired AI [slides]
- [conference] Bernstein Conference of computational neuroscience 2020 [poster] [video]
- [summer school] AI-Enabled Mobility. Sep 2020
- [post] Corrupt, sparse, irregular and ugly: Deep learning on time series
- [paper] Spike-frequency adaptation provides a long short-term memory to networks of spiking neurons.bioRxiv. May 2020
- [paper] A solution to the learning dilemma for recurrent networks of spiking neurons.Nature Communications. July 2020
- [slides] Human Brain Project SP9 2018 workshop: introduction to LSNN
- [paper] Long short-term memory and learning-to-learn in networks of spiking neurons.NeurIPS. 2018
- [slides] Learning-2-Learn 2018 workshop: gradient free methods for training neural networks
Supervision
- 2021 Master Thesis "Vehicle position estimation with dynamic vision sensor" – Armin Baur
- 2021 Master Thesis "Autodecoder for 3D surface completion from deficient 3D scans" – Robin Baumann
- 2022 Master Thesis "Entity Matching by means of Contrastive Learning" – Pia Schmidt
- 2023 Master Thesis "Identification and classification of spoken language in audio files" – Benedikt Augenstein
- 2025 Master Thesis WIP "Deep Learning Approaches for Acoustic Source Recognition" – Lukas Ringeisen
- 2026 Master Thesis WIP "Leveraging Foundation Model Embeddings for Enhanced Multivariate Outlier Detection in Tabular Data" – Florian Veitz