AI_CDT_DecisionMaking

Details

Visualising data objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as biology, chemistry, psychology and social science, facilitating knowledge discovery. The intuitively uninterpretable high-dimensional data and network data become visually scrutable upon being mapped into 2 or 3-dimensional spaces, enabling insights about the underlying structure and distribution of the data. However, due to the heavy data compression into a space with only 2 or 3 degrees of freedom, information loss is inevitable, thus it is natural to drop “unimportant” data patterns in a visualisation result.

In practice, the desired data pattern and structure to preserve in a visualisation result can vary across domains due to the different nature of the downstream decision-making tasks that the visualization results will serve. An important research question is how to enable a representation learning algorithm to develop an ability to choose what main data pattern/structure to preserve?

This PhD project will approach this question by developing modelling strategies and pipelines to enable human-in-the-loop representation learning algorithm design, asking users to decide what data pattern/structure should be preserved by an algorithm and injecting the preference into algorithm design. The project will focus on a particular scientific domain and test the visualisation results by examining how well it can assist a series of decision making tasks through collaborating with experts from the problem domain.

Desirable student background:

Machine learning with strong skills on mathematical/probabilistic modelling and optimisation, and with special interest in scientific data analysis.

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project. For any questions please contact the UKRI AI Decisions CDT Team ( [email protected] ).

How to apply:

Please apply through the below link for the PhD Artificial Intelligence CDT:

https://pgapplication.manchester.ac.uk/psc/apply/EMPLOYEE/SA/s/WEBLIB_ONL_ADM.CIBAA_LOGIN_BT.FieldFormula.IScript_Direct_Login?Key=UMANC1251000021489F

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language Certificate (if applicable)

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. ( Equality, diversity and inclusion | The University of Manchester )

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs. Our selection process is free from bias, and we are committed to ensuring fair and equal opportunities for all applicants.