PhD Study : Adaptive learning for modelling non-stationary systems

Summary

Background:

Real-world environments in today’s highly interconnected digital world are often non-stationary. Learning systems such as classifiers used for pattern recognition, make the stationarity assumption in input data distribution density during training and operation phases. Their field performance may become unsatisfactory and therefore such learning systems may have limited practical use.   A very good example of a non-stationary environment is an magnetoencephalography and electroencephalography (M/EEG) based brain-computer interface (BCI) in which a communication link is established between a computer and the user through a process involving thinking/imagining a set of cognitive tasks.

In this, a classifier is used to detect the electrophysiological correlates, resulting from the cognitive tasks performed by the user [1-4]. The non-stationary characteristics of M/EEG has been one of the primary impediments in developing a practically useful BCI, despite decades of R&D work. In fact prevalence of such non-stationary systems is wide-spread, e.g., autonomous robots, share price trend predictions, spam filtering, surveillance, and bioinformatics. In all these areas, a learning system devised under the assumption of input data distribution invariance results in suboptimal performance while the system is being used either as a classifier or a predictive model.

There is thus an urgent need of developing reliable techniques for monitoring, predicting, categorising and often controlling non-stationary systems’ characteristics in real-world systems.

Research Program:

To account for EEG non-stationarities appearing as covariate shifts, we have recently developed a covariate shift detection and adaptation methodology using an Exponentially Weighted Moving Average (EWMA) modelling strategy and successfully applied in a motor imagery based BCI [1-4]. Advancing this work further, this project aims to investigate how characteristics of non-stationary systems be effectively learned on-line in the absence of manual labelling of the streaming data.

It will involve investigating a range of approaches applied in multiple applications in a collaborative way. It may involve developing techniques for learning from the changes in inputs alone, possibly transductively based on the previous data; devising adaptation strategies that can make effective use of the available information to extract knowledge about the system variability and also making continuous update to account for the new information without forgetting already learnt information possibly using prediction error minimisation (PEM) based active inferencing and Bayesian belief propagation approach.

References:

1. Raza,    Rathee,  Zhouc, Cecotti & Prasad (2018). Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface. Neurocomputing, https://arxiv.org/abs/1805.01044.
2. Chowdhary, Raza, Meena, Dutta & Prasad (2017). Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation. IEEE Transactions on Cognitive and Developmental Systems, PP. DOI: 10.1109/TCDS.2017.2787040.
3. Raza, Cecotti, Li & Prasad (2015). Adaptive Learning with Covariate Shift-Detection for Motor Imagery based Brain-Computer Interface. Soft Computing, 18p, http://link.springer.com/article/10.1007/s00500-015-1937-5#/page-1.
4. Raza, Prasad, & Li, (2015). EWMA Model based Shift-Detection Methods for Detecting Covariate Shifts in Non-Stationary Environments. Pattern Recognition, 48 (3). pp. 659-669.
5. Friston, FitzGerald, Rigoli, Schwartenbeck & Pezzulo (2017). Active inference: A process theory.  Neural Computation, 29(1):1–49, 2017.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Publications - peer-reviewed

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living