DCU | NUIG | TCD | TU Dublin | UCD

d-real is funded by Science Foundation Ireland and by the contributions of industry partners.

Applications for d-real positions starting in September/October 2022 are being accepted. Positions will remain live on the website until all roles have been filled. Supervisors are currently assessing applications from the first, second and third calls. If you have any questions about the programme please email stephen.carroll@d-real.ie. You can make an application here (through the Good Grants system). You will need to register (free) with Good Grants and confirm your email address to make an application. You will be asked to list your top three topic preferences (listed below). You will also be asked some personal details, your educational history, work experience, technical skill and for a statement on why you would like to join the programme. The system permits the uploading of 5 PDF documents, so you can upload supporting material (CV, personal statement, paper publications, etc. ).

We have a Frequently Asked Questions page here.

Dublin City University

Code: 2022DCU01
Title: Using Disentangled Language Learning for Stylistically and Semantically Controllable Language Generation
Supervision Team: Anya Belz, DCU (Primary Supervisor) / Yvette Graham, TCD (External Secondary Supervisor)
Description: Natural Language Generation (NLG) has made great strides recently owing to the power of transformer language models (LMs). The best generators produce text on a par with high-quality human writing, but have a very high carbon footprint and are marred by hallucinated content, and logical and factual errors. The general question addressed by this thesis proposal is: to what extent can LM learning be disentangled so that different aspects of language can be learnt and retained separately, potentially enabling (i) more direct control over meaning and style, (ii) different systems to share generic knowledge, and (iii) a system to generate the same meaning in any number of different idiolects, among other benefits, including a reduction in energy requirements. The work will start with experiments (i) generalising basic disentangling language-GANs, and (ii) adapting state-of-the-art text-to-image techniques capable of controlling style and semantics of generated images via autoregressive transformers.

Code: 2022DCU02
Title: Development of machine learning tools to predict pathological sequelae of traumatic brain injury
Supervision Team: David MacManus, DCU (Primary Supervisor) / Caitríona Lally, TCD (External Secondary Supervisor) / David Loane, TCD (Additional Supervisory Team Member)
Description: Traumatic brain injuries (TBI) are one of the leading causes of death and disability worldwide. Currently, there is a huge gap in diagnostic and prognostic technologies for TBI likely due to its multiple biological and biomechanical aspects which can cause different neurological pathologies, impairments, and deficits in people. This multifaceted nature of TBI makes it difficult to predict the pathological outcomes using existing technologies. Indeed, it remains difficult to not only diagnose certain TBIs e.g., concussion, but it is also difficult to determine an accurate prognosis. Therefore, the aim of this research is to develop state-of-the-art computational tools to predict TBI pathologies and provide accurate diagnoses and prognoses. This will be achieved by using TBI pathology data (e.g., MRI) with state-of-the-art computer models of the brain to develop novel computational tools utilising machine learning to determine accurate diagnoses and prognoses of TBI from head impacts e.g., sports-related head impacts, falls.

Code: 2022DCU03
Title: Autonomous Mapping and Exploration Using Unmanned Aerial Vehicle in GPS-Denied Environments
Supervision Team: Hossein Javidnia, DCU (Primary Supervisor) / Peter Corcoran, NUIG (External Secondary Supervisor)
Description: As a requirement, majority of the Autonomous Aerial Vehicles (AAV) must be provided with a prior well-constructed environment model. Such prerequisite restricts the performance and applicability of the vehicle and of course providing such a model for complex scenes is not always feasible. This issue calls for the AAVs to perceive an unseen environment in 3D and perform the desired navigation and manipulation tasks. This issue becomes more significant in the absence of GPS signals. Navigating through a GPS-denied environment requires the vehicle to perceive the scene using a LIDAR and 3D sensing modules as well as a complex path planning strategy which is recently being investigated using ML and reinforcement learning methodologies.

Code: 2022DCU04
Title: Immersive Mathematics – Design for All
Supervision Team: Tracey Mehigan, DCU (Primary Supervisor) / Emma Murphy, TU Dublin (External Secondary Supervisor) / Dr Monica Ward, DCU (Additional Supervisory Team Member)
Description: Mathematical equations are an essential component of education for students across all levels however they are not always presented to students in a manner that suits their individual needs. For example, students with dyslexia may struggle with symbols but benefit from spatial representations whereas for blind students, spatial layout becomes a barrier. To address these and other issues, this project explores the potential of multimodal feedback in immersive environments for personalised presentation of mathematics via an Artificial Intelligent Educational (AIEd) framework for automatic adaptation to learner, capability and learning situation.

Code: 2022DCU05
Title: How Full is Your Tank…Development of a ‘Readiness-to-Perform’ Tool for Monitoring & Predicting Sporting Performance
Supervision Team: Anna Donnla O’Hagan, DCU (Primary Supervisor) / Jane Walsh, NUIG (External Secondary Supervisor) / Sarah Meegan, DCU (Additional Supervisory Team Member)
Description: The sports industry is a multi-billion-dollar industry in which coaches and sport and medicine teams strive to push and progress an athlete’s performance year on year. Coaches and scientists structure training programmes with distinct periods of progressive overload coupled with recovery in an attempt to maximise or sustain performance during specific periods of competition. The objective of this research is to test the most relevant existing, portable, technologies to monitor athletes’ physical, cognitive, and psychological readiness for sporting performance and to develop a protocol combining bio-sensory and performance-based data acquisition (e.g., mobile version of Psychomotor Vigilance Task, CANTAB, wearable HRV/blood pressure devices, mobile EEG ECG etc.) related to performance readiness to aid athletes and coaching personnel on an athletes individualised readiness for sporting performance. The project will ultimately deliver a proposal for a user-friendly tool for monitoring performance readiness to assist with continuous improvement in sporting performance and training practices.

Code: 2022DCU06
Title: Usable Interaction for Generic Menu Navigation in Virtual Reality
Supervision Team: Hyowon Lee, DCU (Primary Supervisor) / Brendan Rooney, UCD (External Secondary Supervisor)
Description: There are many demonstration systems of VR technology in various fields today, but there is a considerable lack of standardized, agreed-upon or proven generic menu navigation interaction within a VR environment resulting in poor usability and user-experience (UX) in most of these demonstration systems. Without offering more usable menu interaction through better understanding of the special characteristics of VR interaction and the affordances it entails, VR devices and tech demos have little chance of becoming a truly ubiquitous interaction platform as its desktop and mobile counterparts. This project will develop usability principles/design guidelines for VR interactivity especially focusing on a generic menu navigation within the VR environment, with a set of usable menu navigation strategies and accompanying suite of UIs for them, both co-evolving and co-refined through a series of design-centric iterations of design-prototyping-testing cycles. Interest in usability, design portfolio of UI/UX projects or any other design-focused practices along with the technical competency to implement those designs is desirable.

National University of Ireland, Galway

Code: 2022NUIG01 (position filled)
Title: On-Device Neural Speech Understanding for consumer devices
Supervision Team: Michael Schukat, NUIG (Primary Supervisor) / Naomi Harte, TCD (External Secondary Supervisor) Peter Corcoran, NUIG, Gabriel Costache, Xperi, Martin Walsh, Xperi (Additional Supervisory Team Members)
Description: Today’s voice-based interfaces rely on a cloud-based infrastructure for data processing and interpretation that causes issues with regard to access to personal voice-data by large corporations. Therefore, there is a growing trend in industry to move data-processing and analysis closer to the source of data – the microphone that senses speech data. This can be achieved using newly-developed neural accelerators (i.e. NVIDIA’s Jetson, Google’s TPU, Xilinx Vitus-AI and Perceive’s Ergo) that implement emerging neural-processing techniques. This research aims to investigate emerging trends in speech analysis and understanding with a focus on neural implementations and optimizations for the above accelerator platforms. It will examine memory and data-bandwidth aspects of recurrence in neural speech analysis, explore neural speech enhancement techniques to pre-process voice signals that are picked up from low-cost microphones, explore speech representations for neural accelerator platforms, and deliver a proof-of-concept smart-speaker, demonstrating feasibility of a practical stand-alone neural speech interface. Previous project experience in digital speech processing and neural network algorithm development is desirable.

Code: 2022NUIG02 (position filled)
Title: Executable Design: AI Tools for Music
Supervision Team: James McDermott, NUIG (Primary Supervisor) / Seán O’Leary, TU Dublin (External Secondary Supervisor)
Description: This project is about new AI-enabled creative tools for musicians and new creative ways for music consumers to interact with music. Modern AI methods can generate plausible-sounding music, at least in some cases. However, AI has not achieved anything like “understanding” of the internal structures, relationships and patterns which are deliberately designed by human composers. This understanding can best be represented by short programs, since (by the Church-Turing thesis) no other representation can be more powerful. Short programs are those which best capture all possible regularity and structure. We will use modern metaheuristic search methods such as genetic programming, and neural program synthesis, to automatically create short programs which, when executed, output given pieces of pre-existing music. Simple manipulations of these programs will give natural variations and extensions of the music, enabling exciting new tools for creativity.

Code: 2022NUIG03 (position filled)
Title: IntentPredictVR: A deep learning-based pedestrian intent prediction from gaze, head, and body pose aided with synthetic data from VR
Supervision Team: Ihsan Ullah, NUIG (Primary Supervisor) / Susan McKeever, TU Dublin (External Secondary Supervisor), Peter Corcoran, Michael Schukat, and Michael Madden (all NUIG)(Additional Supervisory Team Members)
Description: Recent technology that works on deep neural networks has shown good results for autonomous vehicles. Synthetic data generation from game engines e.g. unreal and using it for training deep learning models where data is scarce, has shown promising results in various applications. Pedestrian protection is a critical challenge for these vehicles due to pedestrian’s abrupt movement. For pedestrian safety, we will use a similar technique in our system that will utilize pedestrian gaze, head and body pose information from real and synthetic images to predict the pedestrian intended direction. This plays a vital role in warning the autonomous vehicle before approaching the pedestrian and avoiding accidents, thus saving human lives.

Trinity College Dublin

Code: 2022TCD01 (position filled)
Title: Procedural Generation of Narrative Puzzles
Supervision Team: Mads Haahr, TCD (Primary Supervisor) / Marguerite Barry, UCD (External Secondary Supervisor)
Description: Narrative puzzles are puzzles that form part of the progression of a narrative, whose solutions involve exploration and logical as well as creative thinking. They are key components of adventure and storydriven games, and often feature in large open-world games. However, filling large open worlds with engaging content is challenging, especially for games with procedurally generated worlds, such as Minecraft (2011) and No Man’s Sky (2016). Systems exist for generating narrative puzzles procedurally, but they lack context about many narrative elements, such as character motivation, plot progression, dramatic arc, as well as player modelling. This project will improve procedurally generation of narratives for small-scale narrative games as well as large-scale open world games by integrating new types of narrative elements as well as player modelling into the Story Puzzle Heuristics for Interactive Narrative eXperiences (SPHINX) framework, potentially resulting in dynamically generated narratives of increased sophistication and significantly improved player experience.

Code: 2022TCD02 (no longer recruiting)
Title: Deep Learning for Magnetic Resonance Quantitative Susceptibility Mapping of Carotid Artery Plaques
Supervision Team: Caitríona Lally, TCD (Primary Supervisor) / Catherine Mooney, UCD (External Secondary Supervisor) / Alan Stone, St. Vincent’s Hospital Dublin (Additional Supervisory Team Member)
Description: Carotid artery disease is the leading cause of ischaemic stroke. The current standard-of-care involves removing plaques that narrow a carotid artery by more than 50%. The degree of vessel occlusion, however, is a poor indication of plaque rupture risk, which is ultimately what leads to stroke. Plaque mechanical integrity is the critical factor which determines the risk of plaque rupture, where the mechanical strength of this tissue is governed by its composition. Using machine learning approaches and in vivo imaging, and in particular Quantitative Susceptibility Mapping metrics obtained from MRI, we propose to non-invasively determine plaque composition and hence vulnerability of carotid plaques to rupture. This highly collaborative project has the potential to change diagnosis and treatment of vulnerable carotid plaques using non-ionizing MR imaging which would be truly transformative for carotid artery disease management.

Code: 2022TCD03 (position filled)
Title: Motion in the Metaverse: Perception of identity and personality from embodied humans
Supervision Team: Rachel McDonnell, TCD (Primary Supervisor) / Brendan Rooney, UCD (External Secondary Supervisor) / Aphra Kerr, TU Dublin (Additional Supervisory Team Member)
Description: The Metaverse is expected to be a digital reality combining social media and gaming, with augmented and virtual reality, allowing users to interact virtually for both work and entertainment. The Metaverse cannot exist without avatars, which are virtual manifestations of the humans interacting in the space. In this project, we will investigate the perception of motion of avatars in social interactions in immersive Virtual Reality. In particular, we are interested in how the quality of motion mapped from the human affects perception of personality and personal identity. Additionally, the ethical, legal and social implications around how human motion data is captured and stored in the Metaverse will be investigated as part of this project.

Code: 2022TCD04
Title: Personalisation of relapse risk in autoimmune disease
Supervision Team: Mark Little, TCD (Primary Supervisor) / John Kelleher, TU Dublin (External Secondary Supervisor) / Declan O’Sullivan, TCD and Alain Pitiot (Ilixa software)(Additional Supervisory Team Members)
Description: The PARADISE study targets development of a clinical decision support tool that personalises immunosuppressive drug (ISD) therapy in autoimmune disease. Using ANCA vasculitis as the exemplar condition and leveraging off the Rare Kidney Disease registry and biobank, we will focus on deep phenotyping of the patient in remission. At this time point, we hypothesise that residual sub-clinical immune system activation renders the patient at high risk of subsequent relapse of the disease. Conversely, reversion of the immune system to a healthy resting state may indicate a very low flare risk. By using a novel semantic web technology, we will integrate clinical, patient app-derived and multi-modal biomarker data streams to generate explainable machine learning models that predict the risk of flare. These will inform the physician about increasing ISDs or, indeed, discontinuing them altogether. We envisage that this assessment will reduce both relapse and ISD-associated infection, reduce healthcare costs, increase quality of life and build human capital in a research area of importance to Ireland.

Code: 2022TCD05
Title: Authenticity in Dialogue
Supervision Team: Carl Vogel, TCD (Primary Supervisor) / Eugenia Siapera, UCD, (External Secondary Supervisor)
Description: Authenticity in communication is of utmost importance to those who attempt to feign authenticity and is also relevant to those who would prefer to banish inauthenticity, whether the sphere is public relations, politics, health care, dating or courts of law. Dialogue interactions in multiple modalities will be analyzed with the aim of identifying features that discriminate authentic and pretended engagement. The work will involve assembling a multi-modal corpus of evidently un-scripted dialogue interactions, annotation with respect to authenticity categories of interest, and analysis through combinations of close inspection, semi-automated processing and data mining to identify features that separate authentic and inauthentic dialogue communications..

Code: 2022TCD06
Title: Automotive Audio System Integration and Tuning in Virtual Reality
Supervision Team: Enda Bates, TCD (Primary Supervisor) / Andrew Hines, UCD (External Secondary Supervisor) / Martin Walsh, Xperi/DTS (Additional Supervisory Team Member)
Description: This project involves the development of a VR system for automotive audio system integration and tuning, in collaboration with Xperi/DTS. Modern automotive audio systems are often acoustically tuned in-situ using a real physical car, however, at this late stage of the design process, it may be impossible to implement any significant modifications. Ideally, much of this acoustic design could be done earlier in the design phase of the automobile and the work done remotely or virtually, thereby potentially greatly reducing the acoustics design cycle of a new car model, and yielding cost savings to the manufacturer. This project will therefore examine the use of VR for the accurate simulation of both the visual and acoustic characteristics of automobiles and associated audio systems, and compare the effectiveness of designing the acoustic system using the traditional on-site approach with newly-developed remote simulation methodologies based on this VR model.

Code: 2022TCD07
Title: When digital feels human: Investigating dialogue naturalness with multivariate neural data
Supervision Team: Giovanni M. Di Liberto, TCD (Primary Supervisor) / Benjamin Cowan, UCD (External Secondary Supervisor)
Description: The interaction with digital systems has become a pervasive daily experience (e.g., video-calls, dialogue systems). One major barrier remains that users need to adapt the way they communicate to each particular digital system, for example constituting a challenge for inclusivity. This project will identify objective indices that quantify the naturalness of a conversation by using bio-signals recorded with electroencephalography and pupillometry. These metrics will inform us on how exactly different digital communication strategies (e.g., video-call software) impact cognition (e.g., cognitive load, phonological processing, temporal expectations). In doing so, this project will inform us on the key elements for producing adaptive dialogue systems.

Code: 2022TCD08
Title: An Intervention using a Personalized VR Dance/Game Tutor with Progress Monitoring for Survivors of Childhood Cancer
Supervision Team: Juliette Hussey, TCD (Primary Supervisor) / Shirley Coyle, DCU (External Secondary Supervisor) / Carol O’Sullivan, TCD (Additional Supervisory Team Member)
Description: Recent improvements in survival rates from childhood cancer have resulted in a shift in focus, with more emphasis being placed on exploring and reducing late-effects, addressing cognitive and physical rehabilitation and enhancing the independence of long-term childhood CNS tumour survivors. Known late-effects amongst survivors of childhood CNS tumours include endocrinopathies, obesity, cerebrovascular complications, cardiovascular conditions, neurologic conditions, neurocognitive dysfunction and pulmonary dysfunction, all of which can negatively impact on overall quality of life. In this project, we will design a personalized intervention using a VR dance/game tutor for survivors of childhood cancer. The intervention will be motivating and enjoyable for the target population and will involve the use of VR headsets and low/medium-cost tracking devices. The system will be personalized for each participant and will be designed to include motivational and monitoring features.

Code: 2022TCD09
Title: AI powered dynamic network control for real-time video streaming
Supervision Team: Marco Ruffini, TCD (Primary Supervisor) / Suzanne Little, DCU (External Secondary Supervisor)
Description: This project aims to develop intelligent control mechanisms to make network configuration decisions based on real-time information from live video streaming and AR/VR services. The focus is on the cooperative performance optimization for live video streaming and AR that considers the interplay between service prioritization and resource availability prediction when making control decisions. The goal is to make predictions for video processing (e.g., encoding level, chunk size) based on the anticipated capacity and latency in the network, which depends on multiple environmental factors, including user behavior (i.e., due to the highly interactive nature of the AR/VR content). On the other hand, we will also make predictions about the network performance and rely on various control loops, defined by the O-RAN architecture, to dynamically reconfigure the network to match different traffic requirements.

Code: 2022TCD10
Title: Designing apps to support psychotropic medication management and tapering
Supervision Team: Gavin Doherty, TCD (Primary Supervisor) / Jane Walsh, NUIG (External Secondary Supervisor) / Cathal Cadogan, TCD (Additional Supervisory Team Member)
Description: Psychotropic medications, such as antidepressants, are widely used in the treatment of mental health disorders, and can be a valuable approach. However, evidence supporting long-term use is often lacking and can be seen as inconsistent with a recovery-oriented approach which emphasises service user engagement, respect for autonomy and personhood. Previous research on technology supports for people taking medication have not focussed on the use of psychotropic medications, and the majority of the existing literature on the topic has focussed on medication adherence. This PhD studentship will examine the design of technology to support people taking psychotropic medication, taking a more holistic view, looking at safe and managed medication tapering/discontinuation as well as adherence and management. It will involve work with multiple stakeholders, including clinicians, user groups and people who use or have used these medications. The PhD will be centered in Human-Computer Interaction, with interdisciplinary input from a supervision team which involves academics in Psychology and Pharmacy.

Technological University Dublin

Code: 2022TUD01
Title: Co-design of an interactive wellness park: A multimodal physical web installation for outdoor rehabilitation and wellness
Supervision Team: Damon Berry, TU Dublin (Primary Supervisor) / Mads Haahr, TCD (External Secondary Supervisor) / Emma Murphy, TU Dublin (Additional Supervisory Team Member)
Description: Physical rehabilitation is a critical and widely-applicable healthcare intervention. Increased engagement in rehabilitation improves health outcomes and reduces healthcare costs. Prescribed outdoor physical exercise can promote social interaction and improve quality of life. For this work, a co-designed physical web installation will be created to make managed rehabilitation exercises more engaging and sustainable. QR codes, NFC, and BLE will enable low-barrier connections to web resources to support managed exercise. The user interface will be co-created by service users and clinicians informed by behaviour change theory to create a personalised and accessible outdoor digital rehabilitation intervention. The proposed system will comprise:
– Physical web installation residing on wooden posts in a healthcare campus.
– Web infrastructure enabling exercise regimes to be personalised.
Through co-design with stakeholders, physical web UX will be investigated to assess different approaches in order to produce a working installation and to develop an accessible and reproducible design.

Code: 2022TUD02 (position filled)
Title: Controllable Consistent Timbre Synthesis
Supervision Team: Seán O’Leary, TU Dublin (Primary Supervisor) / Naomi Harte, TCD (External Secondary Supervisor)
Description: The goal of the research will be to provide control over the design of consistent musical instruments. Until recently sound synthesis has been dominated by two approaches – physical modelling and signal modelling. Physical models specify a source. Once the source is specified the family of sounds coming from the source can be synthesised. Signal models, on the other hand, specify waveforms and so are very general. The major downside of signal models is that many parameters are required to specify a single sound. The goal of this project is to use machine learning algorithms to synthesise the parameters for a family of sounds related to a single source. This project will marry machine learning and signal processing techniques, including research into the use of generative algorithms, signal models and sound representations.

Code: 2022TUD03
Title: Empowering older adults to engage with their own health data using multimodal feedback
Supervision Team: Emma Murphy, TU Dublin (Primary Supervisor) / Enda Bates, TCD (External Secondary Supervisor)
Description: Health data from physiological sensors is often conveyed to users through a graphical interface but this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Real-time user feedback may not be conveyed easily from sensor devices through visual cues alone, but auditory and tactile feedback can provide immediate and accessible cues from wearable devices. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. This research will involve an exploration of the potential of multimodal cues to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate etc. By creating innovative and inclusive user feedback it is more likely that users will want to engage and interact with new devices and with their own data.

Code: 2022TUD04
Title: Verbal Language to Irish Sign Language Machine Translation: A Linguistically Informed Approach
Supervision Team: Irene Murtagh, TU Dublin (Primary Supervisor) / Andy Way, DCU (External Secondary Supervisor)
Description: Machine translation (MT) of verbal language (speech/text) has garnered widespread attention over the past 60 years. On the other hand, computational processing of signed language has unfortunately not received nearly as much attention, resulting in its exclusion from modern language technologies. This exclusion, leaves deaf and hard-of-hearing individuals at a disadvantage, aggravating the human-to-human communication barrier, while suppressing an already under resourced set of languages further for the estimated 72 million deaf people in the world. This aim of this project is to develop a linguistically motivated sign language machine translation (SLMT) avatar that will translate between English (text/speech) and Irish Sign Language (ISL). The project will focus, in particular, on current linguistic and technical challenges in relation the computational modelling and processing of sign language. This will involve research in sign language linguistics, computational linguistics, natural language processing and virtual character animation.

Code: 2022TUD05
Title: Composing New Solutions to the Cocktail Party Problem: An Evolutionary Optimization Approach to Speech Reconstruction
Supervision Team: Ruairí de Fréin, TU Dublin (Primary Supervisor) / James McDermott, NUIG (External Secondary Supervisor)
Description: The human brain can separate-out one speaker from a mixture of speakers. This problem is commonly called the cocktail party problem. Deploying signal enhancement techniques in assisted living environments to enhance socialization opportunities for inhabitants is an open problem. Techniques that can blindly separate an arbitrary number of speech/music sources using a small number of anechoic mixtures, in real time, could be used to profound effect in removing acoustic clutter in these scenarios, however, the resulting separated speech does not sound natural. This project will develop algorithms for composing natural sounding speech for separated speakers using speaker-tuned fitness functions and evolutionary optimization. The resulting algorithms will be used to develop systems for natural speech production to encourage social interaction amongst older adults.

Code: 2022TUD06 (position filled)
Title: Monitoring and Short-term Forecasting of Atmospheric Air Pollutants Using Deep Neural Networks
Supervision Team: Bianca Schoen-Phelan, TU Dublin (Primary Supervisor) / Soumyabrata Dev, UCD (External Secondary Supervisor)
Description: Air pollution is a persistent problem in most of the world’s cities. It has a significant negative influence on citizen health and quality of life. Therefore, it is important to continuously monitor the pollution concentrations and provide short-term forecasts. Historically, this forecasting has been done quite poorly; traditional statistical forecasting methods are unreliable for short-term predictions. Most models are statistical and are limited in range of forecast time and effectiveness. The goal of this PhD project is to create an intelligence system that uses a combination of computer vision and deep learning technologies to identify, monitor, and forecast air pollution in real time, as well as provide residents with an early warning system. This PhD project will also assess the key meteorological variables that affect atmospheric air pollutant concentrations and examine the forecasting model’s effectiveness for the island of Ireland, particularly for the Dublin metropolitan area.

Code: 2022TUD07 (position filled)
Title: Computerized support for the Patient Generated Health Data lifecycle
Supervision Team: Dympna O’Sullivan, TU Dublin (Primary Supervisor) / Lucy Hederman, TCD (External Secondary Supervisor) / Damon Berry, TU Dublin (Additional Supervisory Team Member)
Description: Patient generated health data (PGHD) is increasing in importance with more patients self-managing their conditions. Integrated PGHD including enhanced computational support for acquisition, analytics and actions can provide a holistic view and facilitate shared decision making. There are numerous challenges – PGHD is large in volume and variety, originating from medical devices, sensors and apps. Multiple standards leads to semantic and syntactic incompatibilities. Sociotechnical factors – adherence to self-management regimes, adoption of technology and perceived trustworthiness of PGHD are important considerations. This research will design, develop and evaluate a digital platform for the PGHD lifecycle and focus on the following challenges;
– Understanding the requirements of patients and clinicians for self-management;
– Developing computerized support to capture and represent PGHD;
– Developing methods for analysis of PGHD that can support clinicians from a remote management perspective while also supporting patients with self-management;
– Evaluating the platform with patients and clinicians from HSE living labs.

University College Dublin

Code: 2022UCD01 (position filled)
Title: Data Aware Design: A framework for understanding human-data interaction in HCI and AI
Supervision Team: Marguerite Barry, UCD (Primary Supervisor) / Aphra Kerr, TU Dublin (External Secondary Supervisor) / David Coyle, UCD / Catherine Mooney, UCD / Dave Lewis, TCD / Declan O’Sullivan, TCD (Additional Supervisory Team Members)
Description: Many everyday digital interactions are reliant on personal data being processed, often passively and without user awareness, to allow for health tracking, social media and digital content delivery, voice assistant services, etc. The dependence of these applications on data and machine learning has created an urgent need for data aware design in computer science education, and in human computer interaction (HCI) and AI research. This PhD project will investigate how application designers understand and design for data use across services that use AI. Using qualitative methods, including interviews with practitioners, it will explore human-data interaction (HDI) and human computer interaction (HCI) theory and methods for understanding data interactions. The aim is to develop an interdisciplinary framework for data aware design in teaching and research, to support understanding between and to promote human-centred AI application design. This project would suit applicants interested in qualitative research for understanding people and practices.

Code: 2022UCD02 (position filled)
Title: Supporting help-seeking and recommendations for mental health in young adults
Supervision Team: David Coyle, UCD (Primary Supervisor) / Gavin Doherty, TCD (External Secondary Supervisor) / Marguerite Barry, UCD and Claudette Pretorius, UCD (Additional Supervisory Team Members)
Description: Seeking help is a critical first step in addressing mental health difficulties. Evidence suggests that positive help-seeking experiences contribute to an increased likelihood of future help-seeking and to improved mental health outcomes. Increasingly help-seeking now starts online. However, help-seeking is a complex process. This project will address known limitations of current online help-seeking technologies, including a tendency towards information overload, medicalized recommendations, and a lack of personalization. It will focus on the help-seek needs of young adults, aged 18-25 and will be undertaken in collaboration with national youth mental health organisations. The aim is to develop guided help-seeking technologies including voice and chat-based agent systems, social help-seeking technologies, and conversational recommender systems. The research will be guided by past research that has emphasised the importance of four key design considerations: support for different levels of human connectedness, accessible and trustworthy information, personalisation that respects autonomy, and the need for immediacy. From a theoretical perspective it will explore how traditional models of help-seeking can be integrated with theories of information search and of engagement in Human Computer Interaction.

Code: 2022UCD03
Title: Ethical AI: Developing principles and methods for ethical and trustworthy recommender systems
Supervision Team: Susan Leavy, UCD (Primary Supervisor) / Josephine Griffith, NUI Galway (External Secondary Supervisor)
Description: AI driven recommender systems are becoming increasingly influential in society. However, without appropriate regulatory frameworks, ethical guidelines and auditing tools, they have the potential to promote harmful content and lead to the perpetuation of social injustice. Ethical issues presented by recommender systems concern issues such as inappropriate content, risks to privacy and autonomy and lack of algorithmic transparency (Milano et al., 2020). In response, regulations are being developed nationally and within the EU and many public and private organisations have published ethical guidelines for AI. This research project aims to address the significant challenges in translating policies and guidelines for ethical AI into implementable design principles, methods and tools to enable audits and ethical evaluation. This project will develop a theoretical framework for ethical recommender systems, an audit methodology and tools to enable systematic evaluation with particular consideration for the impact on potentially vulnerable user groups.

Code: 2022UCD04 (position filled)
Title: VR for good: Exploring the active mechanisms underlying the use of Virtual Reality to prompt positive mood and well-being
Supervision Team: Brendan Rooney, UCD (Primary Supervisor) / Pamela Gallagher, DCU (External Secondary Supervisor)
Description: Numerous studies report examples of virtual reality experiences being used to prompt positive emotions and improve health and well-being. Yet little is understood about the way in which such positive psychological outcomes are designed into the virtual experiences – what are the active mechanism by which such immersive experiences can bring about positive emotions? This study explores the way in which the design of virtual reality experiences interact with individual characteristics of the user to impact perception, attention, emotion and mood, empathy, and well-being. In order to do so, the study will identify and refine effective measures to be used in the research (these may include using self-report, physiological and cognitive performance measures). Then building on the exploration of prospective mechanisms, the study will field test some candidate experiences to prompt positive outcomes.

Code: 2022UCD05
Title: Imagining and Designing the Metaverse
Supervision Team: Eugenia Siapera, UCD (Primary Supervisor) / Aphra Kerr, TU Dublin (External Secondary Supervisor) / Cathy Ennis, TU Dublin (Additional Supervisory Team Member)
Description: Interest in the metaverse has increased dramatically since Mark Zuckerberg’s talk in October 2021. By turning to the metaverse, Facebook/Meta indicated a paradigm shift from a platform and social media based internet to an immersive, integrated and experience-based environment. But what precisely the metaverse will be is still undetermined, indicating that the current period will shape its future form. It is therefore important to study the sociotechnical imaginaries around the metaverse as they will end up feeding into relevant policy and to the design of metaverse applications. The project focuses on two key areas, games and health, and seeks to identify the sociotechnical imaginaries of metaverse applications in these areas as they are encountered among different publics, including technology developers, gamers/users, and public bodies. The project explores their views on challenges around user engagement, privacy and other ethical issues, including transparency, human dignity, individual and societal wellbeing, transparency, accountability and non-discrimination. The outcome of the research is expected to include a set of ethical and policy guidelines for the metaverse.

Code: 2022UCD06 (position filled)
Title: Real-time Vision-based Product Placements in Multimedia Videos
Supervision Team: Soumyabrata Dev, UCD (Primary Supervisor) / Mimi Zhang, TCD (External Secondary Supervisor)
Description: Product placement and embedding marketing are recently used extensively for advertisement in today’s skip-ad generation. In this PhD project, we use computer vision and deep learning techniques to accurately perform product placement in multimedia videos. We intend to use convolutional neural networks for accurately detecting existing adverts in videos, tracking them across image frames, and replacing them with new advertisements for targeted audiences. The designed neural networks will be evaluated on available manually annotated data and synthetic datasets. Such developed techniques will have wide-ranging impacts on a variety of applications, including sports billboard marketing, retail fashion advertising, and amongst others.

Code: 2022UCD07
Title: Perspective taking in multiparty human-machine dialogue
Supervision Team: Benjamin R Cowan, UCD (Primary Supervisor) / Vincent Wade, TCD (External Secondary Supervisor)
Description: Work on human-machine dialogue suggests that people take their partner’s perspective into account when interacting with speech based automated dialogue partners. Perspective taking more generally is seen as critical to successful communication. Through smartspeakers, speech agents have become devices that hold conversations with multiple users in interaction. This move from dyadic to multiparty dialogue is likely to be accelerated as agents take a more proactive approach to engaging users in dialogue, becoming member of a team rather than the sole target of the interaction. This PhD aims to identify how perspective taking mechanisms manifest in mixed human-speech agent teams, and how this influences user language choices when engaging with the speech agent.