Meet the ART-AI students and read about their research interests and backgrounds.
ART-AI Cohort 1
Ronny Bogani
To What Degree AI Can Empower, Protect and Further Realise the Rights of Children and Young People in Accordance with the UN Convention on the Rights of the Child
Oscar Bryan
Machine Learning for the Detection of Unexploded Ordnance Using Synthetic Aperture Sonar
George Fletcher
Intelligent 3D Character Creation & Animation
Catriona Gray
The Adoption and Use of Artificial Intelligence Technologies (AITs) by Humanitarian Organisations
Akshil Patel
Autonomous Skill Acquisition
Elena Safrygina
Translating Spatio-Temporal Imaging Data Into Clinical Data Using Machine Learning
Jack E. Saunders
Reactive Collision Avoidance Using Deep Reinforcement Learning for the Application of UAV Delivery
Jack R. Saunders
Crossing the Uncanny Valley: Using Deep Learning for Realism in Facial Animation
Mafalda Teixeira Ribeiro
Controlled Biological Neuron Growth for the Creation of Animats and Novel AI Techniques
Elsa Zhong
How Interactive AI Can Be Used to Manipulate Humans
Damian Ziubroniewicz
Fair Machine Learning
I will explore AI’s potential as a digital guardian in the online environment. My project is relevant to present debate concerning both the means and the desirability of classifying individuals as ‘children’ using the current system based on the age of the child, and how to remain consistent with the best interests of the child in the digital world; how AI can be utilized to evaluate the individual developmental maturity of children independent of age, and how artificial intelligence may be employed to protect and ensure children’s rights to development, information, education, participation and privacy in a safe and secure digital environment. My research is intended to ascertain whether AI acting as a digital guardian could redefine the position and power of children in relation to the competing protective interests of parents and policy interests of the State using the United Nations Convention for the Rights of the Child (UNCRC) as a regulatory framework.
Dr Emma Carmel
Prof Joanna Bryson (Hertie School of Governance, Berlin)
AI and the Law, AI and Human Rights
Bachelor of Arts, University of Central Florida; Juris Doctor, Florida State University; Master of Laws in Human Rights, University of Edinburgh.
My research area is the application of machine learning methods for the detection of sea disposed, unexploded ordnance. Advances in underwater sensing technologies (synthetic aperture Sonar) and autonomous vehicles has resulted in large amounts of survey data at a high (cm) resolution. This development motivates the development of autonomous mapping and assessment for the remediation of historic weapons dumped at sea.
Dr Alan Hunter
Dr Tom Fincham Haines
Sonar, deep learning, Bayesian machine learning and explainable AI.
MEng Mechanical Engineering at University of Bath, ART-AI MRes
I am investigating the use of AI in rapid character and asset creation for highly stylised avatars or high quality rapid character creation with semantically meaningful controls assisting design. The focus is on leveraging the rich quadrupedal motion capture data of CAMERA here at Bath via deep learning techniques. My project will also consider the ethical issues concerned with high quality avatar generation, and the potential impacts on the creative industries (eg games industry ‘crunch‘).
Supervisors
Prof Darren Cosker
Dr Polly McGuigan
Machine Learning, particularly concerned with Animation and Procedural Character Generation for the creative industries, with broader interests in Reinforcement Learning, Graphics, and Vision.
MMath (4 years) from the University of Oxford
I will examine decisions relating to the adoption and use of data-driven AITs as decision-supporting tools by humanitarian actors within the ‘global refugee regime’. Adopting an institutional ethnographic approach, my project aims to identify the forces shaping these decisions as well as their ethical and political implications.
Dr Emma Carmel
Displacement, humanitarianism, Science and Technology Studies (STS), governance, policy anthropology, socio-legal studies, decoloniality.
LLB Law, MRes Sociology, MSc Refugee and Forced Migration Studies
My project will develop algorithms that give artificial agents the ability to autonomously form useful high-level behaviours, such as grasping, from available behavioural units (for example, primitive sensory and motor actions available to a robot). This allows a developmental process during which an agent can learn behaviours of increasing complexity through continuously building on its existing set of skills. Using high-level skills aggregates the agent’s behaviour which facilitates transparency through explainability.
Dr Özgür Şimşek
Dr Iulia Cioroianu
All things Machine Learning with a particular interest in Reinforcement Learning and Transparency and Explainability in AI.
BSc Mathematical Sciences and MSc Machine Learning and Autonomous Systems at University of Bath
Organiser of BathML meetup (2019-)
Tumour heterogeneity at the protein level has been associated with poor prognosis in several human carcinomas. Current approaches for assessing protein function rely on intensity-based methods, which are limited by their subjectivity and specificity. A novel assay using amplified, time-resolved Forster resonance energy transfer (FRET) is a highly specific and sensitive method and can be adapted to any protein.
The aim of the project is to combine both methods to reveal molecular heterogeneity at the protein level and, using machine learning techniques, translate it to interpretable format, which can be widely used by clinicians.
Prof Banafshé Larijani
Dr Julian Padget
Probabilistic modelling, Machine Learning, and interdisciplinary applications to biomedical sciences.
My research aim is to extract valuable information and automate the inference of clinical data using Machine Learning.
Specialist diploma in Engineering, Ural Federal University, Ekaterinburg
MSc Data Science, Birkbeck College, University of London
Unmanned aerial vehicles (UAV) lack the required safety technologies for regulations to allow for delivery applications. Current legislation is very restrictive, only permitting their operation within line of sight and during certain daylight hours. This is for good reasons as UAVs can weigh up to 25kg and travel at speeds approaching 45 m/s which presents enormous threat to individual safety with several reports describing injuries or property damage from UAV crashes.
I hope to research the dynamic environments delivery UAVs will encounter mid-flight for collision avoidance systems. More specifically pop-up collisions that require highly reactive manoeuvres.
Dr Wenbin Li
Dr Pejman Iravani
Training manoeuvres using deep reinforcement learning and exploring new ways that visual intelligence can impact the trajectory generation of UAVs. Then finding ways this research can help increase trust for UAV delivery.
MEng Mechanical Engineering at Cardiff University
The project involves capturing micro-level idiosyncrasies in individual faces and reconstructing them using state-of-the-art deep learning technologies, in particular generative adversarial networks. The project also touches on the ethical issues surrounding the use of realistically reconstructed faces, such as those posed by deepfakes.
Prof Darren Cosker
Dr Anthony Little
My research interests include the use of AI methods, particularly GANs in computer modelling for CGI in films and games, with a specific focus on faces.
BSc in Mathematics from the University of Southampton
My project focuses on developing devices for directing the growth of biological neurons, as well as recording and stimulating these using electrical or optical means. The cultured neuronal network on the device can then interact with a simulated environment through an animat – a simulated robot. This setup could offer novel insights into the mechanisms behind intelligent behaviour at a miniaturised level, which could then be used to improve or create novel AI approaches. Given the interdisciplinarity of this project, concepts of ethics, accountability, responsibility, and transparency are crucial from growing biological cultures to any derived AI applications.
Dr Benjamin Metcalfe
Dr Christof Lutterorth
Dr Michael Proulx
Using bioelectronics for better monitoring and understanding of cellular behaviour, and the mechanisms behind intelligence in order to develop novel AI techniques.
I received an MEng in Electrical and Electronic Engineering from the University of Bath, with a final year project focusing on the development of highly sensitive microfluidic lab-on-chip devices. I also had an industrial placement at Intel UK where I conducted digital design and verification of chips for mobile applications.
My research is to investigate how conversational interactive AI (CIAI) (e.g.: Amazon Alexa) can influence humans by creating or enhancing human cognitive biases. Cognitive biases are universally inherent in the human brain. CIAI can create or enhance cognitive biases to manipulate or nudge humans. The current research starts from arguing if people really want to get rid of biases from AI or if AI should use inherent human biases to some extent to influence humans. The research would then investigate how to influence humans with CIAI. The results are expected to be directly used to set industry standards of CIAI and to address the lack of clear standards and accountability of CIAI.
Prof Eamonn O’Neill
Conversational interactive AI, human and machine biases, behavioural economics.
BSc in Applied Psychology and Medical Law, Chongqing Medical University
MSc in Social Psychology, Lancaster University
Two years of working experience in the AI industry; past research projects include affective computing and rehabilitation robots for the treatment of children with ASD.
The project focuses on the development of fairness adjusted machine learning algorithms. The aim is to create models which reduce the presence and impact of bias and unfairness, such as discrimination, in machine learning outputs while preserving optimal predictive capacities.
Dr Özgür Şimşek
Dr Iulia Cioroianu
Prof Nick Pearce
Machine Learning, data science, algorithmic bias, fairness.
My main research interest involves developing new techniques in the computational sciences to answer questions and problems in the social sciences.
MSc Data Science, University of Liverpool. My research project focused on the development of an algorithm with the ability to measure and quantify social value in the digital city context. I also have 1 year of professional experience as a data scientist.
