Collaborative Probabilistic Machine Learning (2025 entry onward)
Primary supervisor
Additional supervisors
- Samuel Kaski
Additional information
Contact admissions office
Other projects with the same supervisor
- Learning of user models in human-in-the-loop machine learning (2025 entry onward)
- Trustworthy Multi-source Learning (2025 entry onward)
- Probabilistic modelling and Bayesian machine learning (2025 entry onward)
- Automatic Experimental Design with Human in the Loop (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
We develop probabilistic machine learning methods for helping other agents make better decisions, ultimately human agents in ongoing applications in science and engineering. We use multi-agent formulations to define the assistance problems solved by these agents, and probabilistic modelling for defining the problems the agents together solve in the world. The project has flexibility in how much to focus on multi-agent reinforcement learning, new models of human behaviour, or modelling the scientific and engineering problems to be solved collaboratively. For all these, we are working with top-notch collaborators. I am looking for a student with experience in probabilistic machine learning and preferably reinforcement learning. No formal experience with cognitive science or application domains is required, but is a plus. Additional knowledge in any of the following will be helpful: game theory, multi-agent RL, Bayesian RL, computational rationality, and inverse reinforcement learning.
Keywords: Probabilistic machine learning, multi-agent RL, cooperative AI, user modelling.
Reference: https://www.manchester.ac.uk/discover/news/new-human-ai-research-teams-could-be-the-future-of-research-meeting-future-societal-challenges/; https://www.ukri.org/news/ukri-invests-in-the-next-generation-of-ai-innovators/
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.
- This project requires mathematical engagement and ability substantially greater than for a typical Computer Science PhD. Give evidence for appropriate competence, as relevant to the project description.
- 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?