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Learning of User Models in Human-in-the-Loop Machine Learning

   Department of Computer Science

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  Prof Samuel Kaski  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

The goal is to develop and test Bayesian inference techniques for learning of advanced user models from observational data. The problem setting resembles inverse reinforcement learning, but new techniques need to be developed to cope with the model evolving along time as the user learns, and the user model has several nested multi-agent levels, and bounded-rationality constraints from cognitive science. The student will work with a team of researchers, co-supervised by top-level experts on this topic on both machine learning (Prof. Samuel Kaski) and cognitive science (Prof. Andrew Howes), and be able to apply the techniques in several exciting use cases with industry and academics of other fields.

Professor Sami Kaski from the Department of Computer Science has been appointed among the first Turing Artificial Intelligence (AI) World-Leading Research Fellow. The fellowships, named after AI pioneer Alan Turing, are part of the UK’s commitment to further strengthen its position as a global leader in the field.

Through his fellowship, Professor Kaski aims to overcome a fundamental limitation of current AI systems, that they require a detailed specification of the goal before they can help. Machine learning, where solutions to problems are automatically learnt from data, is a form of AI with great promise for addressing a number of challenges. This includes healthcare, where AI can detect patterns associated with diseases and health conditions by studying healthcare records and other data.

Further information can be found at:

Informal enquiries regarding this topic and future projects can be directed to Professor Samuel Kaski ([Email Address Removed]).

Applications can be made via the standard process although we recommend checking your suitability before applying. 


Çelikok et al. (2021). Teaching to Learn: Sequential Teaching of Agents with Inner States. arXiv:2009.06227 (
Peltola et al. Machine Teaching of Active Sequential Learners. NeurIPS 2019 (
Kangasrääsiö et al. Parameter inference for computational cognitive models with Approximate Bayesian Computation. Cognitive Science 43 (2019): e12738 (

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