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Ben Redden

Ben Redden

 https://orcid.org/0009-0007-2003-4047
20262026

Research activity per year

Personal profile

Research Focus

Machine learning and statistical methods for causal discovery and interpretability in multivariate time series. This includes both post-hoc analysis of temporal models and the development of principled methodological approaches for uncovering causal structure. Applications span financial and clinical time series, alongside broader contributions to causal inference and time series modeling.

Research Interests

- Causal discovery in time series
- Temporal deep learning models (LSTM, GNNs, Transformers)
- Post-hoc interpretability and model probing
- Causal inference and causal machine learning
- Granger causality and temporal dependence modeling
- Statistical learning theory for time series
- Financial time series analysis
- Clinical and physiological time series (e.g., healthcare data)
- Representation learning for sequential data

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  • 1 Conference contribution

  • Causal-INSIGHT: probing temporal models to extract causal structure

    Redden, B., Wang, H. & Li, S., 21 Mar 2026, (Accepted) Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2026). IEEE, 8 p. (Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN )).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution