Sascha Welten
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Contact Information:
Lehrstuhl Informatik 5
Building: E2, 2nd floor, right corridor
Room: 6235
Ahornstraße 55 52074 Aachen
When the door is open
Sascha Welten received his master's degree in Computer Science in 2019 from RWTH Aachen University. He is working as a research assistant and PhD student at the Chair for Computer Science 5 at RWTH Aachen University since July 2019. His research area includes Distributed Analytics (E.g. Distributed Machine Learning) and Data Quality Management. Research interest: Distributed Data Distributed Analytics Machine Learning Data Quality/Profiling Semantic Web
Interesting Links:
- Adding a Beacon functionality to the Personal Health Train (Finished)
- Effects of Generative Replay-based methods on Catastrophic Forgetting in DA on healthcare data (Finished)
- From Simulation to Real Settings: Investigating the Testability of Distributed Analytics Experiments (Finished)
- Establishing Trust in P2P Distributed Analytics Infrastructures (Finished)
- In-depth investigation into Catastrophic Forgetting in Distributed Analytics using Model Averaging (Finished)
- Developing a Library for the Personal Health Train (Finished)
- Bridging the gap between design and deployment of statistical analyses in Distributed Analytics (Finished)
- You call this secure? – Investigation of security principles for analysis algorithms (Finished)
- A Containerisation Pipeline for Distributed Analytics Algorithms: The Algorithm Assembly Line (Finished)
- Customisable Data Safes for a Distributed Analytics Platform (Finished)
- Applying Anonymization Methodologies to Distributed Analytics (Finished)
- Methods to Improve Decentralised Incremental Learning (Finished)
- A META-Data Description for a Distributed Analytics Platform (Finished)
- Data Curation for Decentralised Model Training using Representation Learning for Feature Embeddings (Finished)
- Performing Distributed Analytics on Decentralised Geo- and Hydrological Data (Finished)
- Model Training through Curiosity-based Latent Space Exploration on Decentralized Data (Finished)
- Development of Generative Models Trained on Decentralised Hydrological Data (Finished)
- Supporting Distributed Analytics Workflows with Monitoring and Transaction Bots (Finished)
- Applying a Curiosity-Module to training on rare events (Cancelled)
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