Location:
The University of Exeter’s Department of Computer Science is inviting applications for a PhD studentship fully funded by CEMPS and UKRI to commence on 25th September 2023 or as soon as possible thereafter. For eligible students the studentship will cover Home tuition fees plus an annual tax-free stipend of at least £17,668 for 3.5 years full-time, or pro rata for part-time study. The student would be based in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter.
This is a 3.5 year PhD studentship funded by the College of Engineering, Mathematics and Physical Sciences, which is a match fund of a UKRI Future Leaders Fellowship (Grant no: MR/S017062/1) Transfer Optimisation System for Adaptive Automated Nature-Inspired Optimisation supported by UKRI.
Project Description:
Data science (DS) and artificial intelligence (AI) have achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. Building effective machine learning (ML) models from data is an integral part of the majority of DS and AI workflows. The complexity of these tasks highly rely on human ML experts thus are often beyond non-ML-experts. The rapid growth of ML applications has created a demand for off-the-shelf ML approaches that can be used easily and without expert knowledge. This is also known as automatic machine learning (AutoML) which automatically obtain a ML pipeline from data and have become an increasingly popular area in the ML community.
In practice, the design of an AutoML system is essentially a multi-objective optimization problem with more than one performance criterion to consider simultaneously. These criteria, such as predictive performance, model size, prediction speed, fairness and interpretability will vary between projects and have inherent trade-offs. ML pipeline is design by human thus should work for human. Unfortunately, only a few efforts have focused on the interaction between human and AutoML. Partially due to this concern, the current AutoML is still another black box for both ML experts and non-ML-experts to use in practice.
In this project, we aim to develop a revolutionary AutoML system that keeps human-in-the-loop. It consists of the following objectives.
1. Develop interactive data-driven evolutionary multi-objective optimization approaches that proactively learn the decision makers’ preference and progressively guide the search towards the solution(s) of interest.
2. Develop a human computer interaction (HCI) platform (not limited to high-dimensional visualization techniques) that plays as the interface to bridge the decision maker and the computer.
3. Develop effective preference learning approaches to model human preference and reward/fitness function(s) from multimodal data. In addition, we are also interested to investigate and understand the human’s role and mental behavior during the interaction from the perspective of eye tracking, EEG or even fMRI data, which can feedback into a better HCI design.
4. Develop inverse modeling techniques to unbox the black box. This provides interpretability to the trade-off solutions and multi-criterion decision-making. In particular, we are interested in modeling and understanding the dependency between decision variables and multiple conflicting objective functions, as well as the inverse mapping between objective functions to decision variables by using neural symbolic regression.
Note that the successful PhD student is not expected to carry out all these four objectives in this project but just selected ones pertinent to her/his research interests and experience.
This award provides annual funding to cover Home tuition fees and a tax-free stipend. For students who pay Home tuition fees the award will cover the tuition fees in full, plus at least £17,668 per year tax-free stipend. Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend.
The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence on 25th September 2023. The collaboration with the named project partner is subject to contract. Please note full details of the project partner’s contribution and involvement with the project is still to be confirmed and may change during the course of contract negotiations. Full details will be confirmed at offer stage.
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.
The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic
Entry requirements
Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology.
If English is not your first language you will need to meet the required level (Profile A/B/C) as per our guidance at https://www.exeter.ac.uk/pg-research/apply/english/
How to apply
In the application process you will be asked to upload several documents.
• CV
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Research proposal
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to [Email Address Removed] quoting the studentship reference number.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.
The closing date for applications is midnight on Tuesday 11th July 2023.
Interviews will be held virtually/on the University of Exeter Streatham Campus mid July 2023.
If you have any general enquiries about the application process please email [Email Address Removed] or phone 0300 555 60 60 (UK callers) +44 (0) 1392 723044 (International callers) .
Project-specific queries should be directed to the main supervisor Dr Ke Li ([Email Address Removed])
For further information and to submit and application please visit -
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