Automatic Experimental Design with Human in the Loop (2025 entry onward)
Primary supervisor
Additional information
- Filstroff et al. (2021). Targeted Active Learning for Bayesian Decision-Making. arXiv:2106.04193
- Mikkola et al. Projective Preferential Bayesian Optimization. ICML 2020
- Sundin et al. (2019). Active Learning for Decision-Making from Imbalanced Observational Data International Conference on Machine Learning, 36th International Conference on Machine Learning, ICML 2019 (10):10578-10587.
Contact admissions office
Other projects with the same supervisor
- Trustworthy Multi-source Learning (2025 entry onward)
- Learning of user models in human-in-the-loop machine learning (2025 entry onward)
- Probabilistic modelling and Bayesian machine learning (2025 entry onward)
- Collaborative Probabilistic Machine Learning (2025 entry onward)
Funding
- Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.
Project description
Applications are invited for a fully funded (tuition fees plus stipend) 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 generalize Bayesian automatic experimental design to multi-agent models consisting of an AI assistant and the human user, resulting in the AI assistant being able to decide its next actions. Tentative solutions involve developing fast probabilistic surrogates for existing simulator-type models and experimental design with approximate inference. The student will work alongside a team of researchers, supervised by a machine learning expert, and will have access to exciting application opportunities in both companies and academia.
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?