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Dr Kathleen Curran BSc (Hons), MSc, Phd


Dr Curran is an Assistant Professor in Diagnostic Imaging in UCD School of Medicine and an Affiliated Principal Investigator in the Centre for Biomedical Engineering. She is a funded investigator in the Science Foundation Ireland centre for research training in machine learning (ML-Labs) and directs the UCD machine learning in medical imaging research group. Her research interests are in Medical Image Analysis and Machine Learning applied to MRI, Diffusion Magnetic Resonance Imaging and Positron Emission Tomography data. Her group conducts basic and applied research in MRI of the brain, heart and the musculoskeletal system. These multidisciplinary international research studies span medical imaging, computer science and biomedical engineering, have a proven success in publications and encourage innovative approaches amongst her postgraduate researchers. Ultimately, she hopes to bridge the gap between researchers and clinicians and build real-world clinical applications with the support of industrial partners. In partnership with Axial Medical Printing Ltd. she was awarded the 2019 InterTrade Ireland FUSION Project Exemplar Award and is a winner of an Enterprise Ireland Commercialisation Fund 2020. She has authored several book chapters and is currently editor for a special issue on AI enhanced Diffusion MRI in neuroimaging for the journal Frontiers in Neurology. She also leads an MSc programme on AI in medical image analysis.


Katterine Rios

PhD Student

Co-supervisors: Dr Kathleen Curran (UCD)

Katterine Rios focuses on the biomechanics of the stomatognathic system and spine. Her PhD project aims to unravel the influence of the upper cervical spine (C0-C2) position on lumbar spine biomechanics and the pathophysiological processes by applying numerical simulations using computational modelling.


Fangyijie Wang

PhD Student

Co-supervisors: Dr Kathleen Curran (UCD) and Dr Guénolé Silvestre (UCD)

Fangyijie Wang is a PhD student in the School of Medicine and a member of the SFI ML-Labs programme. He completed an MSc Computer Science degree in 2014, and an MSc Statistics degree in 2021. He worked as a Data Engineer in finance and IoT industries for couple of years. In 2022, he decided to return to school and pursue his PhD in AI in Medical Imaging at UCD. His current research focuses on fetal ultrasound imaging analysis by deep learning techniques. 

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Dr. Helena Bartels

Dr Helena Bartels is a graduate of the Royal College of Surgeons Ireland, class of 2014. She is undertaking specialist training in Obstetrics and Gynaecology with the Royal College of Physicians in Ireland. She is completing a PhD under the supervision of Prof Donal Brennan and Dr Kathleen Curran through University College Dublin, while working as a clinical research fellow in the National Maternity Hospital and St Vincents University Hospital. Her thesis focuses on Placenta Accreta Spectrum, a rare complication of pregnancy where the placenta becomes abnormally attached to the womb. Her thesis aims to develop prediction models for Placenta Accreta Spectrum by incorporating a multimodal approach including immunohistochemistry, proteomics and radiomics. She works in close collaboration with Placenta Accreta Ireland, a patient advocacy and support group for those who have been impacted by placenta accreta, in developing educational resources and conducting research exploring the lived experience of placenta accreta.


Niamh Belton

PhD Student

Co-supervisors: Dr Kathleen Curran (UCD) and Dr Aonghus Lawlor (UCD)

Niamh is currently pursuing a PhD  in Machine Learning and Artificial Intelligence, specialising in the medical domain. Her research focuses on the development of novel Anomaly Detection techniques that aim to automatically identify and localise anomalous regions in medical images including MRIs, X-rays and CTs. Such techniques can be integrated with computer aided detection systems which can then be used to assist radiologists in their diagnoses, ultimately resulting in improved patient care. Niamh’s research also focuses on eliminating the requirement for large annotated datasets which are costly and tedious to acquire by developing techniques that are capable of training on only a limited number examples, all while preserving model accuracy.


Carles Garciacabrera

I am a second-year PhD student in the school of Electronic Engineering at DCU, as a member of the ML-Labs Programme. In my research, I explore semi-supervised machine learning algorithms over Cardiac MRI. My recent works have involved synthetic labelling of unannotated time-frames in short axis Cardiac MRI using image registration to segment the different tissues. I hope to extend my work to long axis cuts and to explore multimodality with electrophysiological records.


Misgina Tsighe Hagos

Misgina Tsighe Hagos is a first year PhD student at the SFI centre for research training in machine learning (ML-Labs) in the School of Computer Science, UCD. His PhD project is supervised by Dr. Brian Mac Namee, and co-supervised by Dr. Kathleen Curran. He is working on transparent medical image analysis solutions where domain experts can participate in the model building process by combining model interpretability and Human in the Loop (HITL) machine learning techniques. He is interested in implementing contrastive explanations such as semifactuals and counterfactuals for model interpretability. His proposed project will provide experts with explanations and enable them to interact with a trained model by reporting back inaccurate image features the model may have picked up in its training phase. This will be followed by model correction or unlearning incorrect features, which in turn improves classification performance.


James Callanan

Master Student

Co-Supervisors: Kathleen Curran and Madeleine Lowery

James is a final year Biomedical Engineering Master's student. He is working in the area of Explainable Artificial Intelligence and his final year project is focused on trying to discover concepts of importance in classifying cardiac pathologies.


Gabriel Sjölund

Master Student

Supervisor: Kathleen Curran

Gabriel Sjölund is an exchange student from Biomedical Engineering at Chalmers University of Technology doing his master’s thesis with the research group. With Dr. Kathleen Curran as supervisor he works on building deep learning models to predict pseudoprogression in lung cancer patients undergoing immunotherapy.


Malin Medbo

Master Student

Supervisor: Kathleen Curran

Malin is a final year Master’s student in Biomedical Engineering at Chalmers University of Technology, Sweden. She will spend this spring semester with the Machine Learning in Medical Imaging and Diagnostics Research Lab working on her Master's thesis in deep learning and medical imaging. The thesis is done with Dr. Kathleen Curran as supervisor and aims to use DL models and radiomics on multi-modal data to predict lung cancer trajectory during treatment.


Ronan Hearne

ML Research Engineer

Supervisor: Dr Kathleen Curran (UCD)

Ronan is a Machine Learning Research Engineer working with the team after graduating with a ME and BSc in Biomedical Engineering. His final year project focused on applying ML and DL models to multi-modal and multi-timepoint medical imaging data. To achieve this, Ronan worked closely with physicians in the Mater Misericordiae and St. Vincent’s University Hospital to build out a radiomics workflow for predicting final outcomes for head-neck cancer patients.


Katie Noonan

Katie Noonan is a final year Master's student in the School of Biomedical Engineering. Her thesis focuses on using machine learning techniques to help accelerate the genetic screening processes of zebrafish. She is currently working on building frameworks which automatically segment the spine and vertebrae of zebrafish separately, as well as tools to analysis the resulting segmentation masks.


Yushi Yang

PhD Student

Yushi Yang is a PhD student in university of Bristol. His research is about the collective behaviour of zebrafish. Typically he measure the movement of a group of fish in 3D, analyse their trajectories, and compare the real fish with computer simulations. He applies machine learning algorithms to process the fish videos, and Monte-Carlo/Molecular dynamic algorithms to simulate the fish.


Erika Kague, PhD

Postdoc Researcher - International Collaborator

Dr. Erika Kague is a geneticist, evolutionary and developmental biologist currently working as a Senior Research Associate at the University of Bristol in the Hammond group. Dr. Kague carries research in the fields of bone biology with high interest in osteoporosis, osteoarthritis and craniosynostosis. She is a member of the GEFOs (Genetics Factors of Osteoporosis), GEMSTONE (Genomics of MusculoSkeletal traits Translational Network)  and the Craniosynostosis Consortium. Dr. Kague is an expert in developing in vivo rapid tools for functional assessment of bone associated genes utilising zebrafish.


Adil Dahlan

Machine Learning Research Engineer

Adil Dahlan is a biomedical engineer graduate currently working as a machine learning research engineer. His research focus is applying machine learning and deep learning algorithms onto medical data and musculoskeletal images.

Nicholas McCarthy.jfif

Nicholas McCarthy, PhD

Senior Machine Learning Research Engineer

Nicholas McCarthy is research engineer and scientist working at applying artificial intelligence and machine learning to medical domains. He has prior experience working as a researcher in both academia and industry, with focuses on machine learning and image analysis in digital pathology, computer vision, knowledge graphs and data privacy.


Brendan St John

Software Engineer

Brendan St John, Software Engineer with over 10 years experience working in telecommunications, e-commerce and cybersecurity.

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