جست‌وجوی استاد و پژوهشگر

Indrakshi Deyمشاهده پروفایل

استادیار مدعو

• Wireless Communications
• Quantum Communication Networks
• IoT and Sensor Networks
• +۴ مورد دیگر

Dr. Indrakshi Dey is Head of the Programmable Autonomous Systems (PAS) Division at the Walton Institute for Information and Communications Systems Science, South East Technological University, Ireland. She holds adjunct roles as Adjunct Assistant Professor at Trinity College Dublin and SFI Funded Investigator at the CONNECT Centre. Her research focuses on communication systems across diverse domains including IoT, underwater acoustics, space networks, and quantum communication, addressing challenges in propagation modeling, network performance, and adaptive communication techniques. Education: PhD in Electrical Engineering and Computer Science, University of Calgary (2015) Masters in Wireless Communications, University of Southampton (2010) Research Interests: Dr. Dey’s work spans theoretical and applied research in communication systems. Key areas include developing propagation models for novel environments (e.g., underwater, space), optimizing full-duplex and half-duplex networks, and advancing quantum communication protocols. Her recent projects emphasize IoT applications in agriculture and healthcare, leveraging edge computing and machine learning for real-time monitoring systems. Funding & Grants: €400,000 agricultural research grant from the Department of Agriculture (2024) SFI funding through the CONNECT Centre (2022–present) Labs/Teams: Leads the PAS Division and collaborates with the CONNECT Centre for Future Networks, focusing on cutting-edge communication technologies and their societal applications.

Niju Porathur Josephمشاهده پروفایل

مدرس

• Data Analytics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Niju Porathur Joseph is a Lecturer in Business Information Systems within the Department of Accountancy and Economics at the School of Business, South East Technological University (SETU), Waterford (Cork Road Campus). With 23 years of academic experience across institutions in Ireland, Dubai, Bahrain, and India, he plays an active role in teaching and research. He is currently accepting PhD students and contributes to the advancement of business technology education. Research Interests: His primary research areas include Data Analytics, Artificial Intelligence, Machine Learning, Enterprise Resource Planning (ERP), and Supply Chain Management. His work bridges information systems with modern business practices, focusing on digital transformation, intelligent systems in business, and operational efficiency through technology integration. He teaches key modules such as ERP SAP, AIS SAGE50, Supply Chain, Retail Logistics, Project Management, Information Security, Data Analytics, and AI. Dr. Joseph holds a PhD in Computer Science with specialization in Artificial Intelligence, awarded on November 6, 2015. He has published over 35 international research papers indexed in Scopus and Web of Science and has participated in numerous international conferences, contributing to the scholarly discourse in business information systems and applied AI. Scientific Recognition: ORCID: 0000-0003-4891-2036 Over 35 international publications in Scopus and Web of Science-indexed journals Active participation in international academic conferences Teaching & Advising: Dr. Joseph is currently supervising or open to supervising PhD students in areas related to AI, data analytics, and business information systems. His extensive teaching portfolio reflects a strong commitment to equipping students with practical and theoretical knowledge in modern enterprise technologies. Laboratories and Research Environment: While specific labs or research teams are not mentioned in the provided text, his research is likely conducted in collaboration with the digital business or information systems research groups within the School of Business at SETU, focusing on real-world applications of AI and analytics in enterprise environments.

• Network Function Virtualization
• Terahertz Communications
• 6G Networks
• +۱۲ مورد دیگر

Dr. Alan Davy is Head of Department at the Walton Institute for Information and Communications Systems Science, within the Department of Computing and Mathematics at South East Technological University (SETU), Ireland. He is an active faculty member and currently accepting PhD students, indicating ongoing academic leadership and research supervision. His research expertise lies at the intersection of advanced networking and artificial intelligence. Key interests include Network Function Virtualization (NFV) , 6G Terahertz Communications , Digital Twin technologies , and Quality of Service (QoS) management in latency-sensitive environments. He also explores applications of deep learning and LSTM models for Service-Level Agreement (SLA) prediction and violation prevention. The most recent publications demonstrate a strong trend in applying machine learning to network automation and performance optimization, particularly in next-generation wireless systems. His work bridges theoretical modeling with practical implementation in software-defined and virtualized network architectures. Dr. Davy has contributed to editorial leadership, including a special section in IEEE Access on Software-Defined Networks for Smart Grids. His research has resulted in over 68 publications and significant citation impact, with work referenced in patents, highlighting real-world relevance. He supervises postgraduate research, with a record of five supervised works, though specific student names are not listed. His research is conducted within the Walton Institute, a center focused on information and communications systems science, suggesting collaborative team-based projects in next-generation networking technologies.

Nadia Elghobashi-Meinhardtمشاهده پروفایل

استادیار

• Computational Chemistry
• Theoretical Chemistry
• Molecular Dynamics
• +۹ مورد دیگر

Nadia Elghobashi-Meinhardt is a Lecturer/Assistant Professor at the School of Chemistry, University College Dublin, holding an Ad Astra Fellowship. She earned her BSc in Chemistry from Stanford University (1999), followed by an MSc (2001) and PhD (2005) in Theoretical Chemistry at the Free University Berlin. Her postdoctoral work includes theoretical chemistry at the Fritz Haber Institute (2006) and computational biophysics at Heidelberg University (2007-2009). She later led a junior research group at the Free University Berlin (2011) and studied metalloenzyme catalysis at Technical University Berlin (2019). Her research focuses on computational biophysics , combining quantum mechanics (QM), molecular mechanics (MM), hybrid QM/MM, and machine learning to decipher structure-function relationships in biological systems. Key areas include biocatalysis in metalloenzymes, lysosomal protein dynamics in sterol homeostasis, and applications of computational methods to diseases like Niemann-Pick Type C (NPC). Her work bridges structural biology and quantum chemistry, with implications for clean energy and therapeutic development. Recent studies examine TRPML2 ion channels (2025), phosphatidylserine synthase regulation of SREBP pathways (2025), and transaminase-driven cascades for chiral N-heterocycles (2025). She also investigates sialin transporters (2024) and vitamin-K-dependent enzymes (2024), contributing to understanding lysosomal and metabolic disorders. Scientific Awards Ad Astra Fellowship (2023) Member of the Royal Society of Chemistry (2024) At UCD, she teaches Symmetry and Computational Chemistry (CHEM30230), Applied Introductory and Physical Chemistry (CHEM10130), and Scientific Enquiry (SCI10010), while coordinating Advanced Topics in Physical Chemistry (CHEM41430). She supervises PhD students in projects involving lysosomal proteins, nitrogenase mechanisms, and computational approaches to disease-related pathways.

Marc Sturrockمشاهده پروفایل

مدرس

• Biophysics
• Computational Biology
• Gene Regulatory Networks
• +۶ مورد دیگر

Dr. Sturrock is a Lecturer in Biophysics and Computational Biology in the Department of Physiology. He holds a B.Sc. in Applied Mathematics (2009) and a Ph.D. (2013) from the University of Dundee, focusing on spatio-temporal models of gene regulatory networks. His postdoctoral work includes research at The Ohio State University (macromolecular crowding, cell polarization) and Imperial College London (stochastic gene expression, synthetic Turing patterns). B.Sc., Applied Mathematics, University of Dundee (2009) Ph.D., University of Dundee (2013) His research centers on computational modeling of biological systems, including gene regulation, cell polarization, and cancer dynamics. Publications highlight the role of stochasticity in gene networks, spatial effects in signaling, and applications to glioblastoma and synthetic biology. Topics span mathematical biology, systems biology, and biophysics. Recent articles emphasize machine learning integration for genomic selection, Bayesian network inference, spatio-temporal cancer modeling, and stochastic simulations. Key methodologies include agent-based modeling, synthetic data generation, and compartmentalized systems analysis. He has collaborated with institutions such as the Mathematical Biosciences Institute at Ohio State and synthetic biology groups at Imperial College London, though no current lab affiliations are detailed in the provided text.

Norma Bargaryمشاهده پروفایل

استاد

• Functional Data Analysis
• Statistical Modeling of High-Dimensional Data
• Biostatistics
• +۶ مورد دیگر

Norma Bargary is a Professor in the Department of Mathematics and Statistics at the University of Limerick. She holds multiple leadership roles in national research centers, including Principal Investigator at the Confirm Centre for Smart Manufacturing, Insight Centre for Data Analytics, SSPC (Research Ireland Centre for Pharmaceuticals), and MACSI (Mathematics Applications Consortium for Science and Industry). She serves as Vice-Director of the SFI Centre for Research Training in Foundations of Data Science and collaborates with institutions like the Royal Statistical Society, where she is a statistical Ambassador based in Ireland. Chair of Data Science and Statistical Learning, University of Limerick (2021→) Senior Lecturer in Statistics, University of Limerick (2020→) Lecturer roles at University of Limerick and University College Dublin (2013–2020) Her research focuses on statistical models for large, high-dimensional, time-varying data (e.g., sensor measurements, human movement, -omics datasets). Applications span biostatistics, biomechanics, pharmaceuticals, and smart manufacturing. She specializes in functional data analysis, mixed-effects smoothing, finite mixture clustering, and Gaussian process modeling. Recent publications highlight trends in industrial analytics (Gaussian process models for manufacturing), health data science (Raman spectroscopy for drug identification), and epidemiological modeling (SEIR models for pandemics). Her methodological work includes functional principal component analysis and nonlinear filtering techniques. Scientific Awards : Advanced Scholar Award, Hamilton Prize, Silver Medal, Best Postgraduate Presentation (Conference for Applied Statistics in Ireland), and functional data analysis recognition. Her €16M+ funded work integrates academia and industry through grants from SFI, Enterprise Ireland, EU programs (FP7, H2020), and corporate partnerships. She leads the SFI Frontiers of the Future grant and contributes to UN Sustainable Development Goals via data science education.

Shirin Moghaddamمشاهده پروفایل

دانشیار

• Survival Analysis
• Bayesian Statistics
• Statistical Modelling
• +۴ مورد دیگر

Dr. Shirin Moghaddam is an Associate Professor and Lecturer in Statistics and Data Science at the University of Limerick , within the Department of Mathematics and Statistics . She is also an active member of the Limerick Digital Cancer Research Centre and the Mathematics Applications Consortium for Science and Industry (MACSI) . Her work bridges advanced statistical methodologies with real-world medical applications, particularly in oncology and neurodegenerative disease research. Education: BSc in Statistics – University of Tehran MSc in Mathematical Statistics – University of Tehran PhD in Statistics – University of Galway (NUIG) Research Focus: Dr. Moghaddam's research centers on survival analysis , Bayesian modeling , and machine learning , with a strong emphasis on translational cancer research . She develops predictive models for time-to-event outcomes in prostate cancer and other diseases, integrating genomic and clinical data to improve diagnostic and prognostic accuracy. Her work often involves interdisciplinary collaboration, combining statistics with bioinformatics, clinical oncology, and molecular biology. Scientific Contributions & Trends: Her recent publications reflect a consistent trajectory in applying advanced statistical techniques—especially Bayesian imputation and machine learning—to high-dimensional biomedical data. A recurring theme is the use of mRNA and protein biomarkers to enhance prediction of survival outcomes in prostate cancer patients, both pre- and post-operatively. Her 2024 and 2023 papers in PLoS ONE and Cancers highlight this focus, while her 2023 Molecular Neurobiology paper extends similar methodologies to Parkinson’s disease biomarkers. Leadership & Service: Chair, Young Statisticians Section – Irish Statistical Association (2023–present) Member – Cancer Trials Ireland Collaborations & Networks: Dr. Moghaddam collaborates extensively within Ireland and internationally, contributing to national cancer research initiatives and statistical modeling consortia. Her affiliations with MACSI and the Limerick Digital Cancer Research Centre position her at the intersection of applied mathematics, data science, and clinical research.

• Population Balance Models
• Machine Learning
• Computational Fluid Dynamics (CFD)
• +۵ مورد دیگر

Mehakpreet Singh is an Associate Professor at the Department of Mathematics and Statistics, University of Limerick, Ireland. His research bridges mechanistic modeling with industrial applications in the dairy and pharmaceutical sectors, employing advanced computational techniques such as artificial neural networks (ANN), Discrete Element Method (DEM), and finite volume schemes. He holds a PhD in Applied Mathematics from Indian Institute of Technology Kharagpur (2015) and an MSc in Mathematics from Guru Nanak Dev University (2008). Teaching Interests: Numerical Analysis, Calculus, Engineering Mathematics Research Focus: Population balance models, machine learning for industrial processes, numerical solutions of integro-partial differential equations, and convergence analysis. His work spans analytical, semi-analytical, and computational methods for nonlinear systems. Publication Trends: Recent works (2023–2025) emphasize machine learning in battery technology, efficient finite volume schemes for fragmentation equations, and numerical methods for nonlinear boundary value problems. Key subfields include Lane-Emden equations, homotopy perturbation, and meshfree approaches. Scientific Awards: Marie Skodowska Curie Individual Fellowship Collaborations: Active in interdisciplinary research with affiliations to the Mathematics Applications Consortium for Science and Industry (MACSI). His work aligns with UN Sustainable Development Goals (SDGs) 7 (Affordable Energy) and 12 (Responsible Consumption).

Jamie Wilsonمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Gaussian Processes
• +۲ مورد دیگر

Jamie Wilson is a Postdoctoral Researcher in Data Science and Machine Learning affiliated with the Mathematics Applications Consortium for Science and Industry (MACSI) and CONFIRM Smart Manufacturing at the University of Limerick's Department of Mathematics and Statistics. His research focuses on Machine Learning Data Science Gaussian Processes Time Series Analysis Exoplanet Atmospheres Jamie completed his PhD in Astrophysics at Queen's University Belfast, where he applied statistical and machine learning techniques to astrophysical time series and exoplanet atmosphere analysis. He currently works on an SFI CONFIRM project developing data analytics for optimizing manufacturing and industrial processes.

• Machine Learning
• Electrical Engineering
• Data Storage Systems
• +۴ مورد دیگر

Barry Fitzgerald is an Assistant Lecturer in the Department of Electrical and Electronic Engineering at the Faculty of Engineering and the Built Environment. His research focuses on applying machine learning to address challenges in data storage systems, particularly NAND flash memory endurance prediction, bit error correction, and process optimization. He has contributed to interdisciplinary work combining machine learning with process mining for industrial applications. Key research areas include machine learning algorithms for error prediction in flash memory, classification models, and system engineering. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure. Fitzgerald has published extensively in peer-reviewed journals and conferences, with notable contributions in IEEE Access, Procedia Manufacturing, and international machine learning symposia. Collaboration highlights include partnerships on NAND flash endurance modeling and process optimization techniques. His research bridges electrical engineering and computer science, emphasizing practical solutions for data storage reliability and efficiency.

Ian McLoughlinمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Deep Learning
• Hyperspectral Imaging
• +۳ مورد دیگر

Ian McLoughlin is a Senior Lecturer in the Department of Computer Science & Applied Physics. His research focuses on medical imaging, deep learning, and biomedical applications, with contributions to cancer diagnosis, tumor classification, and forensic healthcare technologies. He has an h-index of 13 and has published extensively in peer-reviewed journals and conferences since 2021. Key research interests include developing efficient machine learning models for medical diagnostics, such as brain tumor classification using neural networks and hyperspectral imaging, as well as optimizing diagnostic tools like lightweight deep learning frameworks for breast cancer. His work also explores mobile health applications for risk assessment in forensic settings. Collaborations include projects with researchers like H. Ayaz, D. Tormey, and S. Unnikrishnan, focusing on perfusion MRI analysis and computational biology. While no scientific awards are explicitly mentioned, his contributions align with UN Sustainable Development Goals related to health and well-being. No grants or advising details are provided in the text. His research spans interdisciplinary areas at the intersection of computer science and healthcare, with a focus on practical clinical applications.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۵ مورد دیگر

Dr. Gregory Doyle is a Senior Lecturer in the Department of Computing at South East Technological University (SETU), part of the Faculty of Science. He holds a PhD in Computer Science (Data Science) from SETU, an MSc in Computer Applications from Dublin City University, and a BSc in Applied Computing from Waterford Institute of Technology. His research focuses on AI, machine learning, data science, InsurTech, Fintech, and service systems, with a strong emphasis on industry collaboration. Education: PhD in Computer Science (Data Science), SETU, 2010 MSc in Computer Applications, Dublin City University, 1996 BSc in Applied Computing, Waterford Institute of Technology, 1993 Dr. Doyle leads the Advanced Machine Intelligence Research (AdMIRe) group and directs the InsurTech Network Centre (INC) at SETU. His work spans applied research in intelligent systems, including projects on adaptive exergames for elderly exercise motivation and optoelectronic systems for data analysis. He has secured funding from national and European agencies and actively supervises postgraduate students. His recent publications explore topics such as tensiography platforms, data diffraction methods, and simulation models for engineering applications. He is deeply involved in interdisciplinary efforts, contributing to the UN Sustainable Development Goals through education and innovation.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Kelly O Brien is an Assistant Lecturer in the Department of Information Technology at the Faculty of Applied Sciences and Technology. Her research focuses on computer vision, neural networks, and deep learning applications in manufacturing and healthcare. Her work contributes to UN Sustainable Development Goals related to quality education and industry innovation. Research interests include object detection, neural network optimization, and quality control systems. Her recent work explores deep learning efficiency with EEG data, illumination effects on industrial vision systems, and CNN applications in medical devices. No scientific awards explicitly mentioned. No student advisees listed in provided data. Labs/teams: No specific labs or teams detailed in the text.

• Catalysis
• Nanomaterials
• Environmental Chemistry
• +۶ مورد دیگر

Dr. Md Shaheen Sarkar is a Lecturer in the Department of Applied Science at the Faculty of Applied Sciences and Technology. His research focuses on sustainable materials, catalytic processes, environmental chemistry, and nanomaterials. His work aligns with UN Sustainable Development Goals, particularly in pollution prevention and affordable clean energy. Key research areas include: Design of eco-friendly adsorbents for heavy metal removal using bio-waste derived polymers Development of heterogeneous nanocatalysts for organic synthesis reactions Optimization of photocatalytic systems for water treatment and energy applications Mechanical property prediction of polymers via machine learning His recent studies emphasize: Cellulose-based chelating ligands for industrial wastewater remediation Palladium and copper nanocatalysts for cross-coupling reactions Hydrogen production and storage technologies Research methodologies incorporate advanced material characterization, computational modeling, and eco-friendly synthesis approaches.

• Biomedical Image Processing
• Machine Learning
• Computer Vision
• +۱ مورد دیگر

Saritha Unnikrishnan is Lecturer in Computing at Atlantic Technological University (ATU) Sligo, Ireland, affiliated with the Department of Computer & Electronic Engineering. She serves as Principal Investigator at Health and Biomedical Research Centre (HEAL) and Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE). Her research develops AI and machine learning solutions for biomedical challenges including tumor characterization from MRI, microplastic analysis, and educational chatbot systems. Key methodologies include deep learning architectures, graph neural networks, and hyperspectral image processing applied to medical diagnostics. Recent work includes explainable AI models for glioma grading, nanoparticle characterization in industrial emulsions, and comparative studies of chatbot platforms for student FAQs. Publications appear in high-impact journals including International Journal of Pharmaceutics and Journal of Magnetic Resonance Imaging.