Download School of Science and Technology – Vice

School of Science and Technology –
Vice-Chancellor’s Researcher
Development Scheme
Broad Area of Research – Computer
Science
Computational Intelligence, Computational
Neuroscience, Data Mining, Machine Learning and
Computational Modelling.
Important note: Projects within the School of Science and Technology may have
the option of joining a new, University Alliance-sponsored, national Doctoral
Training Alliance in Applied Biosciences for Health, in which students will be part
of an exciting multidisciplinary cohort of postgraduate students and supervisors,
and benefit from regular networking and training opportunities through
attendance at regular national meetings, including residential summer schools,
throughout their PhD studies.
Project Titles (descriptions below)
1. Prof Ahmad Lotfi – Fuzzy Transfer Learning in Human Activity Recognition.
2. Dr Georgina Cosma – Development of Novel Computational Intelligence
Approaches to Cancer Prognosis and Diagnosis.
3. Prof TM McGinnity – Computational Modelling of Biological Learning, Adaptation
and Cognition Utilising Integrated Neural Systems.
1.
Prof Ahmad Lotfi – Fuzzy Transfer Learning in Human
Activity Recognition
The aim of this research is to combine three concepts; Transfer Learning (TL), Fuzzy
System (FS) and Activity Recognition (AR) to address the problem of learning and
recognising Activities of Daily Living (ADL) in an Ambient Assisted Living environment. ADL
is a term used in healthcare to refer to people's daily self-care activities. They are defined
as "the things we normally do, such as feeding ourselves, bathing, dressing, grooming,
work, homemaking, and leisure.
Transfer learning (aka inductive transfer), is a research hypothesis in machine
learning that focuses on storing knowledge gained while solving one problem and applying
it to a different but related problem. Human learners appear to have inherent ways to
transfer knowledge between tasks. For example, a carer who is looking after one patient
could easily adapt learned knowledge to another patient.
Standard machine learning approaches including Artificial Neural Networks focus on a need
for large training data for models to be developed from the same domain as the target
task. This dependency in data could be reduced if tools and concept suitable for handling
uncertainty are used. Fuzzy systems uses the concept of fuzzy membership, linguistic
variable and conditional rules to represent the degree of uncertainty and this could be
utilised to reduce the dependency to numerical data.
Healthcare professionals use a person's ability or inability to perform ADLs as a
measurement of their functional status, particularly in regard to people with disabilities
and the elderly. Activity recognition aims to identify activities as they occur based on data
collected by sensors. Environment sensors such as PIR motion detectors or door entry
magnetic sensors are used to gather information about more complex activities such as
cooking, sleeping, and eating. These recognised activities are representing the ADL.
Considering the chaotic nature of the human activities, application of Bayesian framework,
Hidden Markov Model (HMM) and other statistical techniques are already investigated.
Non-statistical techniques including data mining and machine learning algorithms are also
being used to model different human activities using a large training dataset. The proposed
fuzzy transfer learning for human activity recognition is an alternative approach to
traditional supervised or unsupervised learning techniques to recognise and transfer
learned activities from one scenario to another. This will be expanded to transfer the model
from one user to another user.
As part of our ongoing research, different techniques in human activity recognition are
already investigated. The project supervisory team has extensive knowledge and
experience in Ambient Assisted Living environment, machine learning techniques including
fuzzy systems and human activity recognition.
Specific qualifications/subject areas required of the applicants for this project:
A first class or upper second class UK BSc (Hons) degree (or equivalent) or Master’s degree
in Computer Science, Computer Systems Engineering or Mathematics. Good mathematical
skills for algorithm development and evaluation are essential.
For informal discussion regarding the project, please contact:
[email protected]
2.
Dr Georgina Cosma – Development of Novel
Computational Intelligence Approaches to Cancer
Prognosis and Diagnosis
An important challenge for primary and secondary care physicians remains the accurate
evaluation of the risk of cancer occurrence and prediction of progression, both of which
are essential for determining the optimum treatment and management. Predictive
modelling in medicine involves deriving a mathematical model for the prediction of a future
outcome for patients. Predictive tools can help during the complex decision-making
processes, and provide individualised, evidence-based estimates for cancer patients. The
predictive models can be based on statistical or computational intelligence techniques.
Computational intelligence is a relatively new term, for which there is currently no formal
definition. Computational intelligence algorithms are considered by some researchers to
involve only evolutionary algorithms, neural networks, fuzzy logic, or hybrids of these.
However, others consider a more broad definition of computational intelligence to include
the above mentioned, as well as paradigms such as Bayesian belief networks, multi-agent
systems, case-based reasoning and so on.
Many computational intelligence approaches, such as artificial neural networks and support
vector machines, are known to increase accuracy in cancer prognosis and diagnosis
because these approaches are capable of dealing with the complexity which is typically
found in clinical datasets. However, a majority of these approaches do not provide
qualitative reasoning behind the derived prediction.
The aim of the project is to identify the factors which are predictors of cancer prognosis
and diagnosis tasks (prostate cancer and lung cancer), and to develop a new
computational intelligence approach which can be used for cancer prediction, and which
can achieve higher predictive accuracy than the existing approaches. The new
computational intelligence method should be able to provide qualitative reasoning behind
the prediction, and therefore provide the combinations of factors and their weightings
which have derived the prediction. The derived method will be integrated in mobile apps
and tablets. These apps will be capable of collecting and storing ‘big data’ which could be
used for making cancer risk predictions in the future. Such data could include
‘conventional’ information on diet, physical activity, smoking, exposure to pollution, and
aspirin use, and be enhanced on the basis of additional biomarkers such as peripheral
blood phenotypic and other ‘liquid biopsy’ derived data. It is expected that the new
approaches that will be developed will improve existing approaches for predicting cancer
risk and the delivery of personalized approaches for the management and treatment of
patients with cancer that are focused on reducing risk and recurrence.
Students will work with a subset of data extracted from The Health Improvement
Network (THIN) database which is a large UK primary care database; and with data from
the British Association of Urological Surgeons (BAUS).
Specific qualifications/subject areas required of the applicants for this project:
BSc and/or MSc in subject areas: computer science, computational intelligence, machine
learning, data science, statistics and optimization, operational research.
For informal discussion regarding the project, please contact:
[email protected]
3.
Prof TM McGinnity – Computational Modelling of
Biological Learning, Adaptation and Cognition Utilising
Integrated Neural Systems.
Specific qualifications/subject areas required of the applicants for this project:
2.1 honours degree or above in STEM-related subjects such as Computer Science,
Mathematics, Physics or Computational Neuroscience. This project would suit a student
with good programming/modelling skills (e.g. in Matlab) and a background in one of the
following: computer science, physics, mathematics, computational neuroscience or a
closely related discipline, together with a strong interest in multi-disciplinary modelling of
brain signal processing, neural networks or cognition.
Brain information processing, learning and cognition are dependent on neural connections
formed during development and modified during life. The structural complexity, scale,
extensive and substantially unknown connectivity, and limited accessibility to neurons,
complicate the study of the dynamics of brain networks. Despite important advances
having been made by biologists and neuroscientists/computational neuroscientists, the
exact ways in which neuronal circuits interconnect and their precise information processing
and dysfunction in health and disease are still active areas of research.
Two main approaches have been utilised by scientists to understand the fundamental (i.e.
non-psychological) processes of learning and cognition. In the first (more biological)
approach, the cellular and molecular mechanisms involved in learning and memory have
been analysed in simple animals and in circuits of mammalian brains. The second (more
computational) approach involves algorithmic modelling of cells and axonal/synaptic
interconnections, at various network scales on powerful computational platforms. This
latter approach is entirely dependent on the accuracy and fidelity of the neuronal and
synaptic models. The ideal research platform would be a hybrid system, that has available
a controllable and fully observable bio-computational, 3D structured and electronically
interconnected matrix of in-vitro biological cells, with direct computational control of
stimulation and read-back of intercellular communication signals. This would allow for the
possibility of computational modelling using accurate (real) cells under precise
experimental control.
This PhD project is one component of an integrated effort to develop and exploit a
controllable and fully observable platform of real biological cells for computational
modelling. A cross-disciplinary team of experts in nanotechnology, biology and
computational modelling is driving the overall effort and establishing reliable, biologically
relevant and well-characterised multi-layer nanofiber-based 3D tissue scaffolds. These
scaffold systems will have a range of applications (e.g. studies of neural development and
function) but are particularly applicable to computational modelling of brain signal
processing for learning and adaptation. The project will build upon our recent
developments of novel bio-modified nanofiber lattices that can be electrically stimulated.
The PhD Project
This PhD project will be focused on the development of approaches for computational
modelling of inter-cell communication, where biological cells are deposited on a hybrid,
nanostructured lattice (other members of the team will focus on the nanotechnology and
cell deposition aspects). The PhD student will:

Explore whether bioactive nanofiber lattices, with co-cultures of distinct
physiologically active populations of cells found in the nervous system, can be
interconnected (bio-electronically) to produce simple models of stimulus-response
signalling events in brain tissue;



Develop algorithms which address the computationally controlled formation of
synaptic interconnect by guided growth, utilising graphene-based interconnect
incorporated into the nanofiber-based 3D tissue scaffolds;
Research computational approaches for studying synaptic modification utilising
computational neuroscience learning algorithms;
Explore the potential of the platform for studies of larger scale neural development.
Specific qualifications/subject areas required of the applicants for this project:
2.1 honours degree or above in STEM-related subjects such as Computer Science,
Mathematics, Physics or Computational Neuroscience.
For informal discussion regarding the project, please contact:
[email protected]