جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Cardiovascular Mechanics
• Computational Fluid Dynamics
• +۵ مورد دیگر

Fiona Malone is a Lecturer in the Department of Mechanical & Industrial Engineering at the University of Galway, Ireland, where she also serves as a Principal Investigator for both the Engineering Research Group (ERG) and the Medical and Engineering Technologies Gateway (MET). Her research bridges mechanical engineering principles with medical applications, focusing on cardiovascular mechanics and stroke-related phenomena. Dr. Malone's research interests center on the intersection of biomedical engineering and cardiovascular mechanics, with particular emphasis on embolism, atrial fibrillation, and aortic arch hemodynamics. She employs patient-specific modeling approaches to investigate blood flow patterns and emboli trajectory paths under various physiological conditions. Her work combines experimental in vitro studies with computational methods to better understand stroke mechanisms and develop potential interventions. Analysis of Dr. Malone's publications reveals a consistent focus on cardiovascular biomechanics with increasing integration of machine learning techniques in recent years. Her earlier work concentrated on mechanical characterization of embolus analogues and hemodynamics in patient-specific models, while her more recent publications incorporate computational methods like gradient boosting algorithms to address class imbalance problems in medical data analysis. Dr. Malone has achieved significant recognition in her field with an h-index of 78 according to Scopus metrics. Her publications have garnered citations across multiple disciplines, demonstrating the interdisciplinary impact of her research. As a Principal Investigator, Dr. Malone leads research initiatives within the Engineering Research Group and the Medical and Engineering Technologies Gateway. Her collaborative network spans multiple institutions and disciplines, with frequent co-authorship with researchers specializing in both engineering and medical fields. Her research has practical implications for understanding stroke mechanisms and developing improved diagnostic and therapeutic approaches.

Brendan Mullaneمشاهده پروفایل

پژوهشگر ارشد

• Electrical Engineering
• Semiconductor Design
• Digital Signal Processing
• +۳ مورد دیگر

Brendan Mullane is a Senior Research Fellow at the University of Limerick , affiliated with the Faculty of Science and Engineering and the Department of Electronic and Computer Engineering . Research Focus: Advanced analog/digital converter design, dynamic element matching, and biomedical signal processing. Key Contributions: Development of high-order noise shaping techniques for DACs, hardware implementations for brain injury detection, and optimization of ADC/DAC architectures. Contact: brendan.mullane@ul.ie Research Interests: Brendan's work centers on precision semiconductor design , with a focus on error correction in digital-to-analog converters and bandpass filtering techniques . His research bridges VLSI optimization and biomedical diagnostics , particularly in applying qEEG analysis for clinical applications. He has extensively explored mismatch shaping , inter-symbol interference mitigation , and switched-capacitor filter performance , contributing to the field of embedded systems for signal processing . Scientific Contributions: His publications demonstrate a strong emphasis on high-speed, low-noise analog circuits and on-chip testing methodologies . Key trends include programmable error shaping , real-time FFT processing , and IEEE 1500 standard optimization for ADC/DAC testability.

Kashif Naseer Qureshiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Healthcare Systems
• +۶ مورد دیگر

Kashif Naseer Qureshi is an Associate Professor in the Department of Electronic and Computer Engineering at the Faculty of Science and Engineering, University of Limerick, Ireland. His research focuses on the intersection of artificial intelligence, healthcare systems, blockchain technology, and wireless communication networks. His recent work explores: AI-driven healthcare architectures (tri-tier models, distributed networks) Blockchain applications in medical and smart city systems Advanced machine learning techniques for data imbalance problems Edge computing security and privacy frameworks Next-generation AI language models with ethical considerations Publications demonstrate a strong emphasis on practical implementations across IoT, cybersecurity, and smart infrastructure domains. His email contact is KashifNaseer.Qureshi@ul.ie .

• Paediatric Inflammatory Bowel Disease
• Microbiome in IBD
• Clinical Outcomes Research
• +۴ مورد دیگر

Seamus Hussey is a Clinical Senior Lecturer at the Royal College of Surgeons in Ireland (RCSI), Department of Paediatrics and Child Health. He is also a Consultant Paediatric Gastroenterologist at Our Lady’s Children’s Hospital, Crumlin, Dublin. His primary research focus is on paediatric inflammatory bowel disease (IBD), particularly the microbiome in IBD, clinical outcomes, and novel therapeutic strategies. He leads the DOCHAS project (Determinants and Outcomes in Children and Adolescents with IBD) at the National Children’s Research Centre. Prof. Hussey is an elected member of the Porto IBD Group of the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN). His research interests include paediatric IBD epidemiology, host-microbial interactions, and translational therapies. He collaborates internationally on projects related to IBD pathogenesis and treatment efficacy. His recent publications highlight advancements in understanding IBD mechanisms, including neutrophil subsets’ role in intestinal disease and the impact of anti-TNF therapy in obese children with IBD. Prof. Hussey has secured grants for projects such as 'Engineered recombinant activated protein C as a novel therapeutic agent for children with IBD' and 'Outcomes of Modified Exclusive Elemental Nutrition in Paediatric Crohn’s Disease.' He is actively involved in clinical training, supervising Specialist Paediatric Registrar Trainees since 2015. Professional roles include Clinical Lead in Paediatric Gastroenterology and membership in the National Endoscopy Clinical Advisory Group. His work aligns with UN Sustainable Development Goal 3 (Good Health and Well-Being), focusing on improving paediatric gastrointestinal health outcomes globally.

• Biostatistics
• Mathematical Modelling
• Financial Modelling
• +۱ مورد دیگر

Dr. Noreen Quinn is a Lecturer in Statistics and Course Co-ordinator for the H.Dip. and M.Sc. in Data Science and Analytics at the Department of Mathematics, Munster Technological University. Her expertise spans Biostatistics, Mathematical Modelling, Financial Modelling, and Prediction Models. Education: PhD in Statistics, Dublin City University M.Sc. in Industrial and Financial Mathematics, Dublin City University B.Ed in Education and Mathematics, Mary Immaculate College, Limerick Research Interests: Application of statistical methods in biomedicine, agriculture, and finance Development of predictive models for complex systems Statistical analysis of livestock and dairy production data Integration of computational tools (SAS, SPSS, Excel, Minitab) in applied research Teaching: Specializes in advanced statistical modules at postgraduate levels, including Biostatistics (Level 9), Applied Statistics and Probability (Level 8), and courses in mathematics for business and social sciences. Publications: Over 10 peer-reviewed articles focusing on statistical applications in epidemiology, agricultural science, and industrial systems. Recent work includes studies on verotoxigenic E. coli detection and lactation curve modelling for Irish dairy cows. Technical Expertise: Proficient in statistical software including SAS, SPSS, Excel, and Minitab.

• Artificial Neural Networks
• Anomaly Detection
• Machine Learning
• +۴ مورد دیگر

Mehran Hossein Zadeh Bazargani is a Marie Curie Post Doctoral Fellow at the School of Mathematics and Statistics , University College Dublin. His research focuses on brain-inspired artificial neural networks, anomaly detection in medical data, and interpretable machine learning. He has contributed to the MED-I consortium and founded the educational platform MLDawn . Education: PhD in Machine Learning (University College Dublin) Research Interests: Developing artificial neural networks for perceptual learning and decision-making, anomaly detection in time-series (ECG, EEG) and medical images (fMRI, X-ray), and computational neuroscience applications under the Free Energy Principle. Teaching Activities: Founder of MLDawn (2018–Present), lecturer at Queens University Belfast (2020), teaching assistant at UCD (2017–2020), and instructor in Iran (2013–2015). Scientific Contributions: 8 publications across anomaly detection, medical image analysis, and molecular communication. Key works include the D-RBFDD network and de-identification frameworks for medical data. Emails: mehran.hosseinzadehbazargani@ucd.ie, mldawn2018@gmail.com

Aedin Culhaneمشاهده پروفایل

استاد

• Cancer Genomics
• Bioinformatics
• Single Cell Biology
• +۸ مورد دیگر

Aedin Culhane is a Professor of Biomedical Sciences (Cancer Genomics) at the University of Limerick School of Medicine. She leads the Limerick Digital Cancer Research Centre and co-leads the all-island eHealth-Hub for Cancer. Her expertise spans computational oncology, multi-omics data integration, and clinical bioinformatics, with over 20 years of experience in cancer genomics, including roles at the Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health in Boston, USA. Education: PhD in Cloning and Characterisation of Interleukin-18 in rat brain, University of Manchester Research Interests: Cancer computational genomics, digital health, single-cell genomics, spatial biology, tumor microenvironment, immune-oncology, and open science initiatives like Bioconductor and OHDSI. She focuses on algorithm development for integrative multi-omics data analysis and translational oncology, including tumor drug resistance mechanisms and CAR-T cell therapies. Recent Article Trends: Her work emphasizes spatial profiling of cancer microenvironments, CAR-T cell engineering, and genomic minimal datasets for precision oncology. Key themes include immune response modulation, Myc activity regulation in treatment persistence, and CRISPR-based functional genomics. Awards: Young European Research Universities Network (YERUN) Open Science Award (2023) Bioconductor Community Award (2021) Grants & Collaborations: Leads the €730,000 Chan Zuckerberg Initiative-funded eHealth-Hub for Cancer. Active in the 1+ Million Genomes Program and Genomic Data Infrastructure (GDI) projects. Collaborates internationally in spatial profiling and multi-omics integration through hackathons and open-source initiatives. Labs & Teams: Directs the Limerick Digital Cancer Research Centre, a hub for AI-driven cancer research and translational genomics. Engages with global networks like the Human Cell Atlas and National Spatial Profiling Platform.

• Machine Learning
• Explainable AI
• Healthcare Data Analytics
• +۶ مورد دیگر

Dr. Alison O'Connor is an Assistant Professor at the University of Limerick , affiliated with the Department of Computer Science & Information Systems , Ageing Research Centre , and Lero – the Irish Software Research Centre . Her research bridges machine learning with mechanics of materials and healthcare data analytics , focusing on applications in structural integrity , Industry 4.0 , and medical informatics . Education : PhD in Mechanical Engineering (Imperial College London, 2015-2019), Graduate Diploma in Advanced Materials (2008-2009), and BSc in Aeronautical Engineering (2004-2008). Her research interests span explainable AI for healthcare decision-making, finite element analysis in nuclear steel integrity, and cold rolling process modeling . Recent publications highlight her work on agent-based patient pathway simulations and MRI texture analysis of traumatic brain injury. She contributes to journal peer-review and university committees as a core member. Publication trends show dual expertise: 50% in structural integrity and materials science, 50% in neuroscience and medical diagnostics . Key subfields include dopamine regulation , microdialysis techniques , and computational medicine .

• Battery Technology
• Electrochemistry
• Materials Science
• +۴ مورد دیگر

Dominika Capkova is a Researcher in the Department of Chemical Sciences at the University of Limerick. Her research focuses on advanced battery technologies with particular expertise in lithium-sulfur batteries and electrochemical energy storage systems. She has established herself as a prominent researcher in the field of battery materials with numerous publications spanning from 2018 to 2025. Her research interests center around electrochemistry and materials science with specific focus on lithium-sulfur battery technology , metal-organic frameworks for energy storage , and machine learning applications for battery performance prediction . Her work addresses critical challenges in battery technology including the polysulfide shuttle effect, cathode material design, and state-of-charge/state-of-health estimation. An analysis of her recent publications reveals a strong trend toward integrating machine learning techniques with electrochemical battery research . Her work spans fundamental materials development (metal-organic frameworks, sulfur cathodes) to applied aspects (battery management systems, degradation modeling). The interdisciplinary nature of her research combines chemistry, materials science, and computational methods to advance next-generation energy storage solutions. Dr. Capkova has actively collaborated with researchers across multiple institutions, as evidenced by her co-authorship on numerous publications. Her research output shows consistent growth, with increasing publication counts from 2018 to 2022, demonstrating her active engagement in the field. While specific grant information isn't detailed in the provided text, her extensive publication record suggests successful funding acquisition to support her research activities. Her work appears to be conducted within research groups focused on advanced battery materials and electrochemical energy storage systems , with particular emphasis on solving the technical challenges associated with lithium-sulfur battery technology. The collaborative nature of her publications indicates she works within multidisciplinary teams addressing various aspects of battery technology development.

• Statistical Machine Learning
• Bayesian Nonparametrics
• Mixture Models
• +۱۳ مورد دیگر

Keefe Murphy is a Lecturer in Statistics within the Department of Mathematics and Statistics at Maynooth University , Faculty of Science & Engineering, and is affiliated with the Hamilton Institute . He is an active researcher in statistical machine learning, Bayesian nonparametrics, and clustering/classification of complex, high-dimensional data. Education: PhD in Statistics, University College Dublin MSc in Statistics, University College Dublin BSc in Economics & Mathematics, University of Limerick Research Interests: His work centres on developing and extending statistical methodologies for supervised and unsupervised learning , with emphasis on mixture models, latent variable models, Bayesian nonparametrics, and computational statistics . He actively contributes novel algorithms and software implementations, including the R packages IMIFA , MoEClust , and MEDseq available on CRAN. Current projects include extensions to Bayesian Additive Regression Trees (BART) , handling missing data , modelling multivariate count data , and variable selection in model-based clustering. Publication Profile: His recent publications (2021–2025) demonstrate a clear trajectory in advancing Bayesian machine learning methods, with contributions to Gaussian process BART models , sparse factor analysis , and educational data mining . Collaborative work spans learning analytics and multi-omic prostate cancer biomarker discovery , illustrating broad interdisciplinary impact. Awards & Recognition: Distinguished Dissertation Award (The Classification Society, 2020) Service & Advising: He serves as Associate Editor for Statistical Analysis and Data Mining and on departmental committees (Course Committee, PR Committee). He has successfully supervised PhD student Mateus Maia (graduated 2024) and currently teaches modules such as Advanced R Programming , Introduction to Data Science , and Nonparametric Statistics . Labs & Collaborations: He is affiliated with the Hamilton Institute , which fosters interdisciplinary research in applied mathematics and statistics, providing a collaborative environment for advancing computational and methodological statistics.

Barry O'Sullivanمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Constraint Satisfaction
• +۳ مورد دیگر

Professor Barry O'Sullivan is a leading academic and industry expert in artificial intelligence at University College Cork , where he serves as a full professor in the School of Computer Science & IT . He directs the Insight Centre for Data Analytics and the SFI Centre for Research Training in AI, and holds influential advisory roles at Europol's GRACE project, Leuven.ai institute, and the Computational Sustainability Network. His €300m+ R&D funding achievements and 15+ recent publications highlight his technical and societal impact. Co-founder and Chief AI Officer, Stimul.ai Member of University College Cork Governing Body Vice Chair, European Commission High-Level Expert Group on AI Former President, European AI Association His research spans AI ethics , machine learning , and computational sustainability , with recent work focusing on explainable AI for medical diagnostics, energy-efficient scheduling in manufacturing, and human-AI collaboration mechanisms. His publications reveal expertise in constraint programming, anomaly detection, and multilingual reasoning systems. Notable awards include fellowships from AAAI, EurAI, and the Royal Irish Academy, alongside leadership roles in AI policy committees for the European Commission and Irish health research ethics. He leads a team of PhD researchers at UCC, including Marco Dalla and Sharmi Dev Gupta, while directing major European AI initiatives like the ALTAI assessment framework.

• High Performance Heterogeneous Computing
• Energy Efficient Computing
• Parallel Computing
• +۴ مورد دیگر

Alexey Lastovetsky is an Associate Professor at the School of Computer Science, University College Dublin (UCD), where he is the founding Director of the Heterogeneous Computing Laboratory (HCL). He holds a PhD from the Moscow Aviation Institute and a Doctor of Science (Habilitation) from the Russian Academy of Sciences. His research focuses on high-performance heterogeneous computing, energy-efficient computing, and parallel algorithms for modern architectures. He has published over 175 peer-reviewed papers and authored influential monographs in the field. Education: PhD (Moscow Aviation Institute), Doctor of Science (Russian Academy of Sciences) Affiliations: UCD since 2001; previously Senior Scientist at Lomonosov Moscow State University (1989–1994) and Principal Scientist at the Russian Academy of Sciences (1995–1999) His research interests span heterogeneous computing , energy-efficient systems , and high-performance parallel algorithms . He has developed novel methodologies for workload distribution optimization, energy modeling, and communication performance analysis. His work on the OpenH programming model and SUARA communication algorithm exemplifies his contributions to scalable parallel computing. He has secured over €4.5M in grants , including four prestigious SFI Investigator awards. His recognition includes being ranked in the Stanford/Elsevier Top 2% Scientists list since 2020 and a ScholarGPS Highly Ranked Scholar distinction. He has organized over 300 international conferences and serves on editorial boards of journals like Journal of Parallel and Distributed Computing . Teaching responsibilities include coordinating modules on High-Performance Computing , Parallel Computing , and UNIX Programming . His lab, HCL, is a global leader in heterogeneous computing research, advancing energy-efficient and scalable solutions for modern HPC platforms.

• Generative AI
• Computer Vision
• Deep Learning
• +۶ مورد دیگر

Dr. Mohamed Saadeldin is an Assistant Professor at the School of Computer Science, University College Dublin (UCD). He holds a B.Sc. (Honours) in Electrical & Electronic Engineering from the University of Khartoum (2005) and a Ph.D. in Computer Science from UCD (2013). His research spans generative AI, computer vision, and foundation models, with applications in healthcare, biomedicine, robotics, and energy. Recently, he has focused on AI-driven solutions for healthcare disparities, such as automated breast cancer screening using low-cost ultrasounds, and multimodal foundation models for precision medicine. He also explores LLM-driven UAV navigation and energy grid management using transformer models. Education: B.Sc. (Honours) in Electrical & Electronic Engineering, University of Khartoum, 2005 Ph.D. in Computer Science, University College Dublin, 2013 Teaching: Coordinates modules in Computer Graphics, Data Structures and Algorithms, and Mobile Computing at UCD’s joint college in China (BDIC). His research interests include generative AI for healthcare, computer vision in robotics, and deep learning for agricultural optimization. Notable projects include vision foundation models for medical imaging, collaboration with Systems Biology Ireland on multimodal models for drug repurposing, and energy grid management via smart meter data analysis. He has authored/co-authored over 30 publications, including IEEE transactions and book chapters. Grants and collaborations involve industry partnerships on load disaggregation and academic projects like VistaMilk for dairy sector applications. His early work on ultrasonic systems for indoor localization and gesture control demonstrates his long-standing expertise in signal processing and human-AI interaction.

Jufan Zhangمشاهده پروفایل

استادیار

• Nanopore Manufacturing
• Micro/Nano Manufacturing
• Biomedical Devices
• +۳ مورد دیگر

Dr. Jufan Zhang is an Assistant Professor at the School of Mechanical and Materials Engineering, University College Dublin, Ireland. He is also a member of the UCD Centre of Micro/Nano Manufacturing Technology and the SFI I-Form Advanced Manufacturing Research Centre. His research focuses on advanced manufacturing technologies including nanopore arrays, microneedle devices, geometrical waveguide AR displays, and atomic-scale manufacturing. He holds a PhD from Harbin Institute of Technology, China. Education: B.E., M.E., and Ph.D. in Mechanical Engineering from Harbin Institute of Technology, China Professional Roles: Faculty member, Lab Manager, and funded investigator in multiple research centers Research Interests: Manufacturing of large-scale solid-state nanopore arrays for biomedical and renewable energy applications Design of microneedle drug delivery systems Optical waveguide innovations for ultra-thin AR displays Surface texturing for improved joint tribology Atomic-scale manufacturing processes Recent articles highlight advancements in nanopore array applications, laser shock peening for additive manufacturing, and AR display technologies. His work bridges fundamental research with industrial applications through collaborations with companies like Watson Biotech and Changan University. Awards include the Career Development Award (UCD), International Science and Technology Cooperation Base recognitions (Shaanxi and Xi’an provinces), and top downloaded article honors. He actively participates in global conferences and serves on editorial and review boards for journals like Small and International Journal of Advanced Manufacturing Technology . Teaching responsibilities include coordinating modules like Automotive Design and Thermodynamics, with recent teaching awards for curriculum reform. He supervises PhD students and manages over 30 grants totaling €multi-million, including EU and industry partnerships. Lab affiliations include the UCD AI Healthcare Hub and the Una Europa Self-Steering Committee on Future Materials, advancing interdisciplinary research in manufacturing and healthcare.

• Artificial Intelligence
• AI Ethics
• Organizational Innovation
• +۳ مورد دیگر

Javier Amaya is an Assistant Professor of Technology and Operations Management at University College Dublin's School of Business, and a research affiliate at Saïd Business School, University of Oxford. He holds a PhD in Management from the University of Oxford and a Master’s from Cranfield University. His research focuses on integrating AI into expert-driven organizations, emphasizing ethical AI adoption and human-AI collaboration. He co-developed the capAI conformity assessment protocol and cofounded oxethica, an AI auditing startup. Amaya teaches operations management and sustainable supply chain courses at UCD. His work has been recognized through awards including the EurOMA’s Harry Boer Best Student Paper Award and the Academy of Management Best Paper Award Runner-up. He coordinates the 'Building Sustainable SCA' module and has completed a Harvard certification in teaching with cases. His research explores topics like algorithmic aversion in inventory management and the ethical governance of AI systems under EU regulations.