Learning of user models in human-in-the-loop machine learning (2025 entry onward)
Primary supervisor
Additional information
- ??elikok 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
- Kangasr????si?? et al. Parameter inference for computational cognitive models with Approximate Bayesian Computation. Cognitive Science 43 (2019): e12738.
Contact admissions office
Other projects with the same supervisor
- Trustworthy Multi-source Learning (2025 entry onward)
- Probabilistic modelling and Bayesian machine learning (2025 entry onward)
- Collaborative Probabilistic Machine Learning (2025 entry onward)
- Automatic Experimental Design with Human in the Loop (2025 entry onward)
Funding
- Directly Funded Project (UK Students Only)
This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.
Project description
Studentship related to Turing AI World-Leading Researcher Fellowship: Human-AI Research Teams - Steering AI in Experimental Design and Decision-Making
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:
https://www.ukri.org/news/global-leaders-named-as-turing-ai-world-leading-researcher-fellows/.
https://www.manchester.ac.uk/discover/news/new-human-ai-research-teams-could-be-the-future-of-research-meeting-future-societal-challenges/.
Informal enquiries regarding this topic and future projects can be directed to Professor Samuel Kaski (samuel.kaski@manchester.ac.uk).
Applications can be made via the standard process although we recommend checking your suitability before applying.
Person specification
For information
- Candidates must hold a minimum of an upper Second Class UK Honours degree or international equivalent in a relevant science or engineering discipline.
- Candidates must meet the School's minimum English Language requirement.
- Candidates will be expected to comply with the University's policies and practices of equality, diversity and inclusion.
Essential
Applicants will be required to evidence the following skills and qualifications.
- You must be capable of performing at a very high level.
- You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.
Desirable
Applicants will be required to evidence the following skills and qualifications.
- You will have good time management.
- You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
General
Applicants will be required to address the following.
- Comment on your transcript/predicted degree marks, outlining both strong and weak points.
- Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
- How well does your previous study prepare you for undertaking Postgraduate Research?
- Why do you believe you are suitable for doing Postgraduate Research?