Here are some suggested topics for PhD projects from our group members. These projects are merely suggestions and illustrate the broad interests of the research group. Potential research students are encouraged and welcome to produce their own suggestions in these research areas or other research areas in the field. All applicants are invited to contact the academic associated with the project when making an application.
Using Soft Nanomembrane Electronics for Home-based Anxiety Monitoring
Contact: Jim Ang
Sensor-enhanced virtual reality systems for mental health care and rehabilitation. New immersive technologies, such as virtual reality (VR) and augmented reality (AR) are playing an increasingly important role in the digital health revolution. Significant research has been carried out at University of Kent, in collaboration with medical scientists/practitioners, psychiatrists/psychologists, digital artists and material scientists (for novel sensor design and integration with VR). Such projects include designing VR for dementia care, eating disorder therapy, eye disorder therapy and VR-enabled brain-machine interactions. This PhD research can take on the following directions: (1) Co-design of VR for a specific healthcare domains, involving key stakeholders (e.g. patient representatives, clinicians, etc) to understand the design and deployment opportunities and challenges in realistic health contexts. (2) Deploy and evaluate VR prototypes to study the impact of the technologies in the target groups. (3) Design and evaluate machine learning algorithms to analyse behavioural and physiological signals for clinical meaningful information, e.g. classification of emotion, detection of eye movement, etc.
M Mahmood, S Kwon, H Kim, Y Kim, P Siriaraya, J Choi, B Otkhmezuri, K Kang, KJ Yu, YC Jang, CS Ang, W Yeo (2021) Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery‐Based Brain–Machine Interfaces. Advanced Science. 8(19).
S Mishra, K Yu, Y Kim, Y Lee, M Mahmood, R Herbert, CS Ang, W Yeo, J Intarasirisawat, Y Kown, H Lim (2020). Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies. Science Advances. 6 (11), eaay1729.
L Tabbaa, CS Ang, V Rose, P Siriaraya, I Stewart, KG Jenkins, M Matsangidou (2019) Bring the Outside In: Providing Accessible Experiences Through VR for People with Dementia in Locked Psychiatric Hospitals, Proceedings of the CHI 2019 Conference on Human Factors in Computing Systems.
M Matsangidou, B Otkhmezuri, CS Ang, M Avraamides, G Riva, A Gaggioli, D Iosif, M Karekla (2020). “Now I can see me” designing a multi-user virtual reality remote psychotherapy for body weight and shape concerns. Human–Computer Interaction. 1-27.
Optimisation of Queries over Virtual Knowledge Graphs
Contact: Elena Botoeva
Virtual Knowledge Graphs (also known as Ontology-Based Data Access) provide user-friendly access to Big Data stored in (possibly multiple) data sources, which can be traditional relational ones or more novel ones such as document and triple stores. In this framework an ontology is used as a conceptual representation of the data, and is connected to the data sources by the means of a mapping. User formulates queries over the ontology using a high-level query language like SPARQL; user queries are then automatically translated into queries over the underlying data sources, and the latter are executed by the database engines. Efficiency of the whole approach is highly dependent on optimality of the data source queries. While the technology is quite developed when the underlying data sources are relational, there are still many open problems when it comes to novel data sources, such as MongoDB, graph databases etc. The objective of this PhD project is to design novel techniques for optimising data source queries arising in the context of Virtual Knowledge Graphs.
Heuristics for Scalable Verification of Neural Networks
Contact: Elena Botoeva
Due to the success of Deep Learning neural networks are now being employed in a variety of safety-critical applications such as autonomous driving cars and aircraft landing. Despite showing impressive results at various tasks, neural networks are known to be vulnerable (hence, not robust) to adversarial attacks: imperceptible to human eye perturbations to an input can lead to incorrect classification. Robustness verification of neural networks is currently a very hot topic both in academia and industry as neural networks. One of the main challenges in this field is deriving efficient techniques that can verify networks with hundred thousands / millions of neurons in reasonable time, which is not trivial given that exact verification is not tractable (NP- or coNP-complete for ReLU-based neural networks depending on the exact verification problem). Incomplete approaches generally offer better scalability but at the cost of completeness. The aim of the proposed PhD project will be to learn heuristics for efficient verification of neural networks.
Machine learning and methods development in neuroimaging
Contact: Howard Bowman
I have current practical work on interpretable machine learning applied in neurology and psychiatry, with particular focus on predicting recovery from stroke using structural MRI and identifying biomarkers of migraine and epilepsy using MEG and EEG. In particular, black- box machine learning has limited value in healthcare, since clinicians, patients and carers need explanations of the decisions made by artificial intelligence systems. One area we investigate to provide interpretable machine learning is Bayesian graphical models. I am keen to take on further PhD students in this area.
Computational and cognitive neuroscience
Contact: Howard Bowman
I have a number of lines of research that combine behavioural and neuroimaging experiments with computational modelling in order to understand how the mind emerges from the brain. A main focus is on understanding the role of consciousness in human perception. This uses neural network models and dynamical systems to understand the electrophysiological data that we record. I also have interests in memory systems, the role of oscillations in the brain, spiking neural networks and human attention. Some of these areas could be investigated using recordings from the human brain with depth electrodes in epilepsy patients, data which we have accessed to. There are a number of areas here that could be explored in a PhD.
Understanding Spiking Neural Networks
Contact: Dominique Chu
Spiking Neural Networks (SNN) are brain-like neural networks. Unlike standard rate coding neural networks, signals are encoded in time. This makes them ideal for processing data that has a temporal component, such as time-series data, video or music. Another advantage of SNNs is that there exists neuromorphic hardware that can efficiently simulate SNNs. SNNs are generally thought to be “more powerful” than standard rate coding networks. However, it is not clear precisely in what sense they are more powerful, or what precisely it is that makes them more powerful. The idea of this project is to investigate this claim using a combination of mathematical and computational methods. As such the project will require an interdisciplinary research methodology at the interface between mathematics, computer science and neuroscience. The project would be suitable for a student who wishes to become and expert in an up-and-coming method in artificial intelligence. It has the scope for both theoretical investigations, but will also require implementing neural networks.
Training algorithms for spiking neural networks
Contact: Dominique Chu
Spiking neural networks encode information through the temporal order of the signals. They are more realistic models of the brain than standard artificial neural networks and they are also more efficient in encoding information. Spiking neural networks are therefore very popular in brain simulations. A disadvantage of spiking neural networks is that there are not many efficient training algorithms available. This project will be about finding novel training algorithms for spiking neural networks and to compare the trained networks with standard artificial neural networks on a number of benchmark AI tasks. An important part of this project will be not only to evaluate how well these spiking neural networks perform in relation to standard networks, but also to understand whether or not they are, as is often claimed, more efficient in the sense that they need smaller networks or fewer computing resources. The main approach of the model will be to gain inspiration from existing theories about how the how the human brain develops and learns. These existing theories will then be adapted so as to develop efficient training algorithms. This project will be primarily within AI, but it will also provide the opportunity to learn and apply techniques and ideas from computational neuroscience.
Machine Learning for the Pharmacology of Ageing
Contact: Alex Freitas
Recently, there has been a growing interest in ageing research, since the proportion of elderly people in the world’s population is expected to increase substantially in the next few decades. As people live longer, it becomes increasingly more common for a person to suffer from multiple age-related diseases. Since old age is the ultimate cause or the greatest risk factor for most of these diseases, progress in ageing research has the potential to lead to a more cost-effective treatment of many age-related diseases in a holistic fashion. In this context, researchers have collected a significant amount of data about ageing-related genes and medical drugs affecting an organism’s longevity – mainly about simpler model organisms, rather than humans. This data is often freely available on the web, which has facilitated the application of machine learning methods to the pharmacology or biomedicine of ageing, to try to discover some knowledge or patterns in such datasets.
This project will focus on developing machine learning algorithms for analysing data about the pharmacology of ageing, i.e., data about medical drugs or chemical compounds that can be used as an intervention against ageing, mainly in model organisms. The broad type of machine learning method to be developed will be supervised machine learning (mainly classification), but the specific type of algorithm to be developed will be decided later, depending on the student’s interest and suitability to the target datasets. Note that, although this is an interdisciplinary project, this is a project for a PhD in Computer Science, so the student will be expected to develop a novel machine learning method. As examples of interdisciplinary papers on machine learning for ageing research, see e.g. (the first paper is particularly relevant for this project, whilst the second includes a broader discussion about machine learning for ageing research):
D.G. Barardo, D. Newby, D. Thornton, T. Ghafourian, J.P. de Magalhaes and A.A. Freitas. Machine learning for predicting lifespan-extending chemical compounds. Aging (Albany NY), 9(7), 1721-1737, 2017.
Fabris, J.P. de Magalhaes, A.A. Freitas. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188, April 2017.
Machine Learning with Fairness-Aware Classification Algorithms
Contact: Alex Freitas
This project involves the classification task of machine learning, where an algorithm has to predict the class of an object (e.g. a customer or a patient) based on properties of that object (e.g. characteristics of a customer or patient). There are now many types of classification algorithms, and in general these algorithms were designed with the only (or main) goal of maximizing predictive performance. As a result, the application of such algorithms to real-world data about people often leads to predictions which have a good predictive accuracy but are unfair, in the sense of discriminating (being biased) against certain groups or types of people – characterized e.g. by values of attributes like gender or ethnicity. In the last few years, however, there has been a considerable amount of research on fairness-aware classification algorithms, which take into account the trade-off between achieving a high predictive accuracy and a high degree of fairness. The project will develop new classification algorithms to cope with this trade-off, focusing on classification algorithms that produce interpretable predictive models, rather than black box models.
Friedler, A.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P. and Roth, D. A comparative study of fairness-enhancing interventions in machine learning. Proc. 2nd ACM Conf. on Fairness, Accountability and Transparency (FAT’19), 329-338. ACM Press, 2019.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. A survey on bias and fairness in machine learning. arXiv preprint: arXiv:1908.09635. 2019.
Explainable Artificial Intelligence Systems for Massive-Scale Nonstationary Data Streams
Contact: Xiaowei Gu
Thanks to the rapid development of information technology and electronic manufacturing industry, massive volumes of streaming data are generated from various aspects of Internet-based activities, in different forms such as texts, images, audios, videos, etc. The information embedded within the streaming data is of paramount importance for enhanced insight into and decision-making about the underlying problem. The need for extracting invaluable information from such data has attracted numerous international organizations and companies to make efforts in order to deploy advanced data mining techniques. However, the very high volume, velocity, variability and complexity of the streaming data have posed great challenges to traditional AI technologies in data-intensive applications. Particularly, the lack of transparency and explainability has been a great barrier for the relevant techniques to be practically implemented in life-critical and financial applications. Therefore, a significant demand exists for developing more advanced data-intensive technologies that entail high performance and efficiency, while enjoying model transparency and explainability. The main aim of this project is to develop cutting-edge computational intelligence technologies for massive-scale data stream mining and modelling in nonstationary environments. In particular, the work of this project will construct an advanced explainable AI methodology through integrating the latest developments in deep learning, ensemble learning, fuzzy systems and pattern recognition. The developed methodology will be further implemented for a carefully selected real-world application, to be chosen from a range of possible problem domains, including: autonomous driving scene analysis, remotely sensed imagery analysis, and high-frequency trading data analysis. The project will provide a platform for an exceptional doctoral candidate to undertake research, involving both theoretical development and experimental investigation, within a world-leading research team for computational intelligence.
How creative are crime-related texts and what does this tell us about cyber crime?
Contact: Shujun Li, Anna Jordanous
The main aim of the PhD project is to investigate if crime-related texts can be evaluated in terms of creativity using automatic metrics. Such a study will help understand how crime-related texts are crafted (by criminals and by automated tools, possibly via a hybrid human-machine teaming approach), how they have evolved over time, how they are perceived by human receivers, and how new methods can be developed to educate people about tactics of cyber criminals. The four tasks of the PhD project will include the following: (1) collecting a large datasets of crime-related texts; (2) developing some objective (automatable) creativity metrics using supervised machine learning, targeted towards evaluating the creativity of crime-related texts (e.g., phishing emails, online hate speech, grooming, cyber bullying, etc.); (3) applying the creativity metrics to the collected data to see how malevolent creativity has evolved over years and for different crimes; (4) exploring the use of generative AI algorithms to create more creative therefore more deceptive crime-related texts.
Computational creativity and automated evaluation
Contact: Anna Jordanous
In exploring how computers can perform creative tasks, computational creativity research has produced many systems that can generate creative products or creative activity. Evaluation, a critical part of the creative process, has not been employed to such a great extent within creative systems. Recent work has concentrated on evaluating the creativity of such computational systems, but there are two issues. Firstly, recent work in evaluation of computational creativity has consisted of the system(s) being evaluated by external evaluators, rather than by the creative system evaluating itself, or evaluation by other creative software agents that may interact with that system. Incorporation of self-evaluation into computational creativity systems *as part of guiding the creative process* is also under explored. In this project the candidate will experiment with incorporating evaluation methods into a creative system and analyse the results to explore how computational creativity systems can incorporate self-evaluation. The creative systems studied could be in the area of musical or linguistic creativity, or in a creative area of the student’s choosing. It is up to the student to decide whether to focus on evaluation methods for evaluating the quality of output from a creative system or the creativity of the system itself (or both). The PhD candidate would be required to propose how they would will explore the above scenarios, for a more specific project. Anna is happy to guide students in this and help them develop their research proposal.
Expressive musical performance software
Contact: Anna Jordanous
Traditionally, when computational software performs music the performances can be criticised for being too unnatural, lacking interpretation and, in short, being too mechanical. However much progress has been made within the field of expressive musical performance and musical interpretation expression. Alongside these advances have been interesting findings in musical expectation (i.e. what people expect to hear when listening to a piece of music), as well as work on emotions that are present within music and on how information and meaning are conveyed in music. Each of these advances raises questions of how the relevant aspects could be interpreted by a musical performer. Potential application areas for computer systems that can perform music in an appropriately expressive manner include, for example, improving playback in music notation editors (like Sibelius), or the automated performance of music generated on-the-fly for ‘hold’ music (played when waiting on hold during phone calls). Practical work exploring this could involve writing software that performs existing pieces, or could be to write software that can improvise, interpreting incoming sound/music and generating an appropriate sonic/musical response to it in real time.
Information Visualisation Directed by Graph Data Mining
Contact: Peter Rodgers
Motif Finding in Set Based Data
Contact: Peter Rodgers
Seeking overrepresented subgraphs, or “Motifs” in graphs is widespread. For example, they are used to analyse gene data [DD07], social networks [WG06] and criminal patterns [DM15]. The process works by sampling a large number of subgraphs of similar size, placing equal subgraphs (isomorphic subgraphs) into buckets and counting the size of the buckets. Data analysts then examine subgraphs that occur more than would be expected by chance. This project would apply the same general process to set based data to see if insights can be derived from detecting motifs. Such data example social network data, where people have might have shared interests and are therefore in the same set. Tags on data (such as twitter hashtags) can also be used classify items into sets. The tasks in this project would be: (1) to derive suitable real world data, (2) to work out randomization strategies that produce random data sets for comparison. (3) design methods to sample small sections (set systems) of this data. (4) develop fast set system isomorphism algorithms (equivalent to the hypergraph isomorphism problem). (5) implement statistical analysis methods to indicate over represented smallset systems. (6) analyse these to see if useful information can be derived from the overrepresented cases.
[DD07] Das, M. K., & Dai, H. K. (2007). A survey of DNA motif finding algorithms. BMC bioinformatics, 8(7), 1.
[DM15] Davies T, Marchione E (2015) Event Networks and the Identification of Crime Pattern Motifs. PLoS ONE 10(11): e0143638. doi:10.1371/journal.pone.0143638
[WG06] Wei, X., & Gang, Z. (2006). U.S. Patent Application No. 11/603,284.
How a robot learns natural language from human tutor
Contact: Giovanni Masala
How a robot learns natural language from human tutor? How can a cognitive architecture, bridging the insights of the human brain mechanisms, be used to model an AI robotic agent specialised in natural language understanding? Can we use emotion detection to improve attention and information storing mechanisms, in the AI agent memory? How embodying the AI agent in a humanoid robot can help the attention mechanisms in managing non-verbal input? The main aim of this project is to develop a novel affective grounded conversational robotic agent, able to learn and reproduce capabilities for empathic language development, understanding and grounding, similarly to a child who learns from a human teacher. The agent will develop the ability to communicate through natural language from scratch, by interacting with a tutor, recognising their verbal and non-verbal inputs as well as emotions, and, finally, grounding the word meaning in the external environment. The project will consider an existing neuro-cognitive architecture under development [1-4], based on a Human-like approach to learning, progressively incrementing knowledge and language capabilities through experience and ample exposure, using a corpus based on early language lexicons (preschool literature). It will be integrated with visuo-spatial information processing mechanisms for embodied language acquisition, exploiting affective mechanisms of emotion detection for learning and cognition. The agent will be embodied into a humanoid robot as opposed to a computer or a virtual assistant, to enable real-world interactions with the human and the external environment, to learn and refine its natural language understanding abilities guided or depending on the teacher’s emotions and visual input (object associations with the words, facial expression, and gestures). Emotions will influence the cognitive attention of the robotic agent, modulating the selectivity of attention on specific tasks, words, and objects, and motivating actions and behaviour.
An alternative version of the PhD proposal (or second PhD topic using the same cognitive architecture embodied in a social robot) can consider more in-depth human – robot cooperation based on natural language understanding.
1) Giorgi, I, Cangelosi A., and MASALA G. L “Learning Actions from Natural Language Instructions using an ON-World Embodied Cognitive Architecture”, in FRONTIERS IN NEUROROBOTICS, 2021, https://doi.org/10.3389/fnbot.2021.626380.
2) Giorgi, I. Golosio, B., Esposito, M., Cangelosi A. and MASALA G. L., “Modelling Multiple Language Learning in a Developmental Cognitive Architecture,” in IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2020 doi: 10.1109/TCDS.2020.3033963.
3) Giorgi I, Cangelosi A and MASALA G.L (2020), A Bilingual Cognitive Robot that Learns like a Toddler, at IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Canberra, Australia, 1-4 December, 2020
4) Golosio B, Cangelosi A, Gamotina O, MASALA GL, A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language. PLoS ONE 10(11): e0140866, 2015.
Explainability and Interpretability of Machine/deep learning techniques in medical imaging
Contact: Giovanni Masala
In medicine is very important the acceptance of the Machine Learning systems not only in terms of performances but also considering the degree to which a human can understand the cause of a decision.
The application of Computer Aided Detection Systems in radiology is typically based on Deep Learning Systems thanks their high performance. In general, more accurate models are less explainable (Gulum et al, 2021) and there is a scientific interest in the field of Explainable Artificial Intelligence, to develop new methods that explain and interpret ML models. There is not a concrete mathematical definition for interpretability or explainability, nor have they been measured by some metric; however, a number of attempts have been made in order to clarify not only these two terms but also related concepts such as comprehensibility.
The possible target of this research is a model to discover the severity of Breast arterial calcifications. Breast arterial calcification (BAC) is calcium deposition in peripheral arterioles. It is regularly found in mammograms but rarely included in final reports because it is considered a benign finding, not relevant to the diagnosis of breast cancer. There is increasing evidence that BAC is a good indicator of the risk of cardiovascular disease. The accurate and automated detection of BACs in mammograms remains an unsolved task and the technology is far from clinical deployment. The challenging task is to develop an explainable model applicable to BAC detection, able to discriminate between severe against weak BACs in patients’ images.
 Mostafavi, Leila et al. (2015) “Prevalence Of Coronary Artery Disease Evaluated By Coronary Ct Angiography In Women With Mammographically Detected Breast Arterial Calcifications”. PLoS One 9;10(4):e0122289, (2015).
 Mazidi, Najim, Carl Roobottom, and Giovanni Masala. “Automatic Quantification of Breast Arterial Calcification on Mammographic Images.” Innovation in Medicine and Healthcare Systems, and Multimedia. Springer, Singapore, 2019. 283-292.
 Linardatos et al (2021). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23(1), 18.
 Gulum, et al (2021). A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging. Applied Sciences, 11(10), 4573.