Event data modelling

Survival/time-to-event analysis is an important field of statistics concerned with understanding the distribution of events over time. Survival analysis presents a unique challenge as we are also interested in events that do not take place, which we refer to as ‘censoring’. Survival analysis methods are important in many real-world settings, such as healthcare (disease prognosis), finance and economics (risk of default), commercial ventures (customer churn), engineering (component lifetime), and many more.

Recently, there has been an increased interest in applying machine learning to survival analysis in order to make more powerful predictions. We encourage successful candidates to explore areas of machine learning within survival analysis that are of interest to them and to pursue novel methods. We also encourage open-source software development in R or Julia. In particular, the candidate will be expected to contribute to the survival analysis development in MLJ. As well as advances in model development, research will also be expected to explore practical aspects of model comparison including validation and benchmarking.

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Possible topics

• Deep learning based methods for survival analysis
• High-dimensional marked event processes
• Active learning for longitudinal household survey data
• Exploring survival data representations and automated preprocessing strategies
• Large language models, transformer and diffusion models for event analysis
• Integrated fairness evaluations for longitudinal models
• Recovering individual trajectories from aggregated data at different resolutions
• Spatio-temporal modelling of crime and tumour growth using scientific machine learning
• High-dimensional feature extraction for multi-omic time-to-event models

Further reading

• Kvamme, H., Borgan, Ø., & Scheel, I. (2019). Time-to-event prediction with neural networks and Cox regression. Journal of Machine Learning Research, 20(129), 1–30.
• Lee, C., Zame, W. R., Yoon, J., & van der Schaar, M. (2018). Deephit: A deep learning approach to survival analysis with competing risks. In Thirty-Second AAAI Conference on Artificial Intelligence.
• Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 24. https://doi.org/10.1186/s12874-018-0482-1
• Gensheimer, M. F., & Narasimhan, B. (2019). A scalable discrete-time survival model for neural networks. PeerJ, 7, e6257.