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Probabilistic modelling and Bayesian machine learning


   Department of Computer Science

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  Prof Samuel Kaski  Applications accepted all year round  Competition Funded PhD Project (UK Students Only)

About the Project

I am looking for a doctoral student to join my new Manchester group to work on new probabilistic models and inference techniques. I have exciting research topics available around the following areas, and I am also open to new suggestions: (1) simulator-based inference, for combining first-principles models with learning from data, (2) Bayesian deep learning, (3) Bayesian reinforcement learning and inverse reinforcement learning, (4) with multi-agent modelling, and (5) privacy-preserving machine learning and synthetic data generation. Can be theoretical or applied work or both; the group has excellent opportunities for collaboration with top-notch partners in multiple applications, including user interaction, health, medicine, synthetic biology and digital twins.


References

Jälkö et al. (2021) Privacy-preserving data sharing via probabilistic modeling. Patterns 2:100271, https://doi.org/10.1016/j.patter.2021.100271
Hegde et al. (2019). Deep learning with differential Gaussian process flows. AISTATS (2019), notable paper award.
Lintusaari et al. (2018). ELFI: Engine for likelihood-free inference. Journal of Machine Learning Research 19(7):1-7 https://www.jmlr.org/papers/volume19/17-374/17-374.pdf
My most up-to-date publication list is at: https://research.cs.aalto.fi/pml/publications.shtml

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