جست‌وجوی استاد و پژوهشگر

• Heat Transfer and Fluid Flow
• Computational Fluid Dynamics
• Machine Learning
• +۵ مورد دیگر

Dr. Shahid Husain serves as a Research Fellow at the University of Limerick's Department of Mathematics and Statistics under a prestigious Marie Skłodowska-Curie postdoctoral fellowship (2022-2024), supervised by Professor Michael Vynnycky. Previously, he held an Assistant Professor position in Mechanical Engineering at Aligarh Muslim University since August 2016. Education: PhD in Mechanical Engineering, Aligarh Muslim University (awarded March 7, 2018) Husain's research integrates Computational Fluid Dynamics with Machine Learning to solve complex thermal-fluid problems, specializing in nanofluid heat transfer, natural convection systems, and renewable energy applications. His work on multiphase flow modeling directly addresses UN Sustainable Development Goals through innovations in solar energy systems and pharmaceutical delivery mechanisms. Recent projects include developing ML-based models for microalgal bioreactors and aerosol transport in human airways. His 2022-2024 publications reveal a strategic shift toward interdisciplinary applications, combining CFD with machine learning for biomedical and sustainable energy solutions. Key trends include nanofluid optimization in solar walls, phase change material integration, and DEM-ML hybrid modeling for particle transport - demonstrating growing emphasis on practical engineering implementations. Scientific Awards: Marie Skłodowska-Curie postdoctoral fellowship (2022) EuroTechPostdoc2 Marie-Curie Cofund fellowship (2022) [declined] University Medal for First position in masters Erasmus Mundus Scholarship [declined] Maulana Azad National Fellowship Husain's primary research funding comes from his €195,000 Marie Curie fellowship supporting the 'Development of a Novel Machine Learning-based model for Multiphase flows' project. Though no formal advisees are listed, his 39 publications (including 22 in 2022-2024) reflect extensive collaboration across 15+ international institutions, with significant co-authorship from Eindhoven University of Technology and Aligarh Muslim University researchers. He operates within Professor Vynnycky's computational modeling group at UL, leveraging high-performance computing resources for fluid dynamics simulations. His current work bridges mechanical engineering and data science, with emerging collaborations in biomedical engineering for respiratory drug delivery systems and sustainable energy applications through UL's Sustainable Energy Research Group.

• Applied Mathematics
• Mathematical Modeling
• Industrial Mathematics
• +۲ مورد دیگر

Dr. Maryna Kasianiuk is a Lecturer in the Department of Mathematics and Statistics at the University of Limerick. She is affiliated with the Mathematics Applications Consortium for Science and Industry (MACSI), focusing on applied mathematical modeling and industrial mathematics. Email: Maryna.Kasianiuk@ul.ie

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.

Doireann O'Kielyمشاهده پروفایل

دانشیار

• Fluid Mechanics
• Solid Mechanics
• Mathematical Modeling
• +۱۱ مورد دیگر

Doireann O'Kiely is an Associate Professor in Applied Mathematics at the University of Limerick, where she conducts research at the intersection of mathematics and real-world physical problems. She serves as Co-Director of MACSI (Mathematics Applications Consortium for Science and Industry) and holds memberships in the Centre for Battery and Energy Materials Research, the Centre for Research Training in Foundations of Data Science, and the SSPC (Research Ireland Centre for Pharmaceuticals). Her educational background includes a BSc in Theoretical Physics from University College Dublin, an MSc in Mathematical Modelling and Scientific Computing from Oxford, and a PhD in Mathematics from the University of Oxford. O'Kiely specializes in applying mathematical modeling, particularly differential equations, to understand physical systems. Her primary research areas include fluid mechanics (viscous flow, capillary flows, fluid-structure interaction), solid mechanics (elasticity, plasticity, wrinkle mechanics), and industrial applications (glass manufacture, metal forming). She has developed significant expertise in modeling thin viscous, elastic, and plastic sheets, with applications ranging from smartphone screens to car bodies. Her work often focuses on counter-intuitive physical phenomena that can be explained and harnessed through mathematical analysis. Analysis of her recent publications reveals a strong focus on mathematical modeling of industrial processes, particularly cold rolling and metal forming, as well as fluid dynamics in porous media and crystallization processes. Her work demonstrates a consistent pattern of applying asymptotic methods and numerical techniques to solve complex physical problems with practical industrial applications, especially in manufacturing and materials science. Dr. O'Kiely actively mentors PhD students and has secured research funding through the SFI Frontiers for the Future programme. Her work with MACSI connects academic research with industrial partners across multiple sectors, translating mathematical insights into practical solutions for real-world challenges. She leads collaborative projects including the Varda (MACSI/SSPC) initiative on polymorphism in crystallization for pharmaceutical manufacturing in space, and has contributed to numerous industry case studies with companies like Medtronic, Boston Scientific, and Vistakon, addressing problems ranging from contact lens manufacturing to stent production.

Helen Purtillمشاهده پروفایل

دانشیار

• Biostatistics
• Longitudinal Studies
• Latent Class Analysis
• +۵ مورد دیگر

Dr. Helen Purtill is an Associate Professor in Statistics and Data Science at the Department of Mathematics and Statistics, University of Limerick. Her research spans biostatistics, clinical trials, and health data science, with affiliations to the Ageing Research Centre, Centre for Research Training in Foundations of Data Science, and Mathematics Applications Consortium for Science and Industry (MACSI). Research Interests: Latent class analysis, longitudinal study design, observational bias, sports injury modeling, RCT methodology, meta-analysis, validity/reliability studies Teaching Focus: Statistics education through research applications, technology-enhanced assessment, cross-disciplinary knowledge transfer between health science and business Collaborations: Interdisciplinary work in geriatrics (TILDA), sports medicine (rugby injury studies), and physiotherapy Her recent publications focus on injury epidemiology in athletes, systematic reviews in low back pain, and clinical decision-making in vascular surgery. Over 20 years, she has contributed to 99 research outputs, including articles, reviews, and conference works. Professional Activities: Member of Health Research Institute Member of Institute for Numerical Computation and Analysis Member of Irish Statistical Association

• 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).

Pepijn Van De Venمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• eHealth
• +۷ مورد دیگر

Pepijn Van De Ven is a Professor in the Department of Electronic and Computer Engineering at the University of Limerick, with affiliations to multiple research centers including the Centre for Research Training in Foundations of Data Science, Data-Driven Computer Engineering Research Centre, and Lero – the Research Ireland Centre for Software. His work bridges artificial intelligence with healthcare applications, particularly focusing on mental health interventions, mobile app development, and ambient assisted living technologies. PhD in AI Techniques for Marine Vehicles (UL, 2005) MSc in Electronic Engineering (Eindhoven University of Technology, 2000) His research interests center on leveraging machine learning and mobile ICT to enhance mental health care, with a specific emphasis on eMental Health, treatment prediction models, and context-aware intervention systems. He also explores computer vision applications in waste classification, medical imaging, and Industry 4.0 safety systems. The trends in his recent publications highlight interdisciplinary work combining computer vision (e.g., instance segmentation, contamination detection), mental health (e.g., depression, diabetes comorbidity), and AI-driven clinical tools (e.g., interpretable models, Bayesian analysis). Subfields like ecological momentary assessment, graph-based learning, and human-AI trust mechanisms are recurrent. Pepijn has contributed to teaching through modules on Android programming and has held leadership roles such as Course Leader for the National MSc in Artificial Intelligence at UL and Past-President of the International Society for Research on Internet Interventions (ISRII). He leads significant research projects including the PROACTIVE (UK-MRC), PERSONAE (Innovation Fund Denmark), and BINSPECT (Science Foundation Ireland) grants. Current research group: D2iCE (Design, Intelligence, and Computer Engineering) Collaborations: King’s College London, University of São Paulo, University of Bristol

• String Theory
• Algebraic Geometry
• Modular Forms
• +۳ مورد دیگر

Jan Manschot is an Associate Professor in the School of Mathematics at Trinity College Dublin, specializing in the intersection of mathematics and theoretical physics. His work bridges advanced mathematical frameworks with fundamental questions in quantum gravity and particle physics. His core research spans String Theory, Algebraic Geometry, Modular Forms, and Black Hole Physics, with emphasis on mathematical structures underlying supersymmetric gauge theories and Calabi-Yau compactifications. He investigates Donaldson-Witten invariants, BPS state counting, and modular properties of partition functions, revealing deep connections between automorphic forms and quantum field theory dynamics. Analysis of his 2021-2024 publications shows consistent focus on string amplitudes via modular integrals, black hole microstate statistics through indefinite theta functions, and topological twists in supersymmetric QCD. His methodology combines path integral techniques, algebraic geometry of moduli spaces, and computational approaches to solve non-perturbative problems in quantum gravity. No scientific awards were documented in the provided sources. Information regarding academic advising, grant funding, or collaborative teams remains unspecified in available materials, though his research implicitly involves cross-disciplinary engagement with theoretical physics communities.

Alina Premrovمشاهده پروفایل

پژوهشگر ارشد

• Biogeochemical Processes
• Ecosystem Modelling
• Climate Change Mitigation
• +۳ مورد دیگر

Dr. Alina Premrov is a Research Fellow at the Discipline of Botany, Trinity College Dublin, the University of Dublin. Her research focuses on biogeochemical processes in terrestrial ecosystems, particularly peatlands, with an emphasis on greenhouse gas emissions, soil organic carbon dynamics, and climate change mitigation. She leads the EPA-funded CO 2 PEAT project, aiming to refine methodologies for reporting CO 2 removals and emissions from Irish peatlands. Prior roles include lecturing in Geography at Maynooth University and postdoctoral research on soil carbon and pesticide risk modelling. Education: PhD in Geology from Trinity College Dublin (2011), focused on nitrate leaching reduction measures in tillage farming. Postdoctoral work included contributions to the CForRep, AUGER, and SOLUM projects, advancing soil carbon stock estimation and biogeochemical modelling. Research Interests: Peatland restoration, GHG flux modelling, pesticide risk assessment, and policy-driven environmental science. Her work integrates advanced models like ECOSSE with observational data to address land use and climate impacts on ecosystems. Recent efforts focus on enhancing IPCC Tier methodologies for peatland reporting and developing tools like the miniRECgap package for CO 2 flux data gap-filling. Grants & Roles: Principal Investigator of the CO 2 PEAT project (EPA grant 2022-CE-1100). Collaborated on DAFM's PROTECTS project and contributed to ECOSSE model enhancements for drained peatlands. Her research has been published in journals like Science of the Total Environment and Geoderma. Labs & Teams: CO 2 PEAT project team at Trinity, ICARUS Department at Maynooth University. Active in interdisciplinary collaborations on ecosystem modelling and climate policy.

• Mathematics
• Computer Science
• Environmental Science
• +۴ مورد دیگر

Dr. Aoife Hennessy is a Lecturer in the Department of Computing and Mathematics at South-East Technological University (SETU). Her research spans interdisciplinary fields including algebraic combinatorics, environmental microbiology, and educational technology. She leads the 'Pathways to Increase Retention through Early at Risk Prediction Modelling' and 'Applications of Riordan Arrays in the Analysis of Algorithms and Structures' projects. Her work focuses on topics like orthogonal polynomials, Riordan arrays, and the application of anaerobic digestates in agriculture. Notable contributions include developing the StatsSkills web app for statistics education and analyzing biofertilizer impacts on soil and plant growth. Dr. Hennessy has collaborated on studies involving microbial quantification methods and contributed to 17 peer-reviewed publications, with over 250 citations and an h-index of 7.

• Genomics
• Biomaterials
• Genetic Architecture
• +۱ مورد دیگر

Mark Daly is a Senior Lecturer in the Department of Computer and Software Engineering within the Faculty of Engineering and Informatics. His research explores genetics, biomaterials, and genomic technologies, with contributions to autism genetics and polygenic risk scoring. Recent publications focus on genomic analysis across biobanks, thermoresponsive hydrogels for medical applications, and high-resolution genotyping methodologies. Collaborative projects involve international consortia like FinnGen and Autism Sequencing Consortium. Media coverage highlights innovations in biomaterials, particularly hydrogel applications for minimally invasive surgery.

• Optical Sensors
• Nanotechnology
• Surface Plasmon Resonance
• +۲ مورد دیگر

Dr. Cian Taylor is a Lecturer in Statistics within the Department of Environmental Science at Atlantic Technological University. His research focuses on developing advanced optical sensors using surface plasmon resonance technology, particularly for environmental hydrogen detection applications. Dr. Taylor's theoretical and experimental work involves nanomaterial synthesis, fiber optic design, and computational modeling of sensor performance. His publications demonstrate expertise in optimizing sensor architectures for industrial and environmental monitoring purposes.

• Group Theory
• Finite Group Theory
• Geometric Group Theory
• +۳ مورد دیگر

Robert Heffernan is a Lecturer in the Department of Mathematics at Munster Technological University (MTU), located in Bishopstown, Cork, Ireland. His research focuses primarily on group theory, with early work centered on finite group theory, including commuting probability, p-groups, automorphisms, and character theory. More recently, his interests have expanded into geometric group theory. He has presented at several conferences, including the IMS September Meeting 2020 and the WIT Mathematics and Physics Seminar. His research often involves collaborative efforts with colleagues such as D. MacHale and B. McCann, exploring topics like minimal embeddings of finite groups and commutativity ratios in finite groups. Dr. Heffernan's publications span a range of journals including the Mathematical Gazette, Bulletin of the Irish Mathematical Society, and International Journal of Group Theory. His work frequently addresses theoretical problems in group structure, representation, and commutativity, with recent contributions to prime-producing polynomials and conjecture analysis in number theory. Despite his extensive publication record, no scientific awards are explicitly mentioned in the provided texts. His research trends emphasize both foundational group theory and its intersections with number theory and geometric approaches. Advising and grant activities are not detailed here, but his involvement in academic seminars and collaborative projects highlights active engagement in the mathematical community.

Congcong Wangمشاهده پروفایل

مدرس

• Social Media Crisis Message Categorization
• Hierarchical Multi-Label Classification
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Congcong Wang is a Lecturer in Computer Science at Maynooth University’s Faculty of Science & Engineering, specifically affiliated with the Maynooth International Engineering College. He earned a PhD in Computer Science from University College Dublin in 2023, focusing on social media crisis message categorization with scarce data, followed by postdoctoral research on hierarchical classification for legal documents at the same institution. He previously completed a B.Sc. in Software Engineering at the Beijing-Dublin International College, a joint program between Beijing University of Technology and University College Dublin. Research Interests: Crisis response using NLP techniques Hierarchical classification for legal and social media data Multilingual harmful comment detection Teaching: He has taught courses like Computing for Engineering and Probability & Statistics at Fuzhou University and supervised undergraduate final-year projects.

Rachel Murphyمشاهده پروفایل

مدرس

• History of Landholding
• Urban History
• Family History
• +۷ مورد دیگر

Rachel Murphy is a Lecturer in the School of History and Geography at the University of Limerick since 2018, specializing in Irish history and digital humanities. She concurrently serves as Editorial and Mapping Assistant for the Royal Irish Academy's Irish Historic Towns Atlas program. Her academic roles include chairing the Irish Historic Towns Atlas Digital Working Group and membership on the National Library of Ireland's Genealogy and Heraldry committee. Education includes a PhD in History and Digital Humanities from University College Cork (2017), MA in History of Family from University of Limerick (2008), and BA from University of Oxford (1992). She holds a Higher Diploma in GIS from UCC and a Grad. Dip. Teaching, Learning and Scholarship from UL (2020). Research focuses on landholding history, urban history, family history, and digital tools like GIS. Notable projects include the 'Death and Burial Data: Ireland 1864-1922' (2020-2023) and 'Deep Maps: West Cork Coastal Cultures' (funded by IRC). She emphasizes interdisciplinary approaches, collaborating with computer scientists on data modeling and transcribathon initiatives. Teaching contributions include supervising 37 MA dissertations (2018-2024), directing the MA History of Family program, and lecturing in Irish history. Awards include 2023 UL Team Teaching Award and AHSS Award for collaborative pedagogy. She has held roles in international business prior to academia, including at eBay Ireland and Accenture. Publications span historical demography, digital humanities methodologies, and Irish social history. Current research explores intersections between historical data curation and emerging technologies, with a focus on open educational resources (OER) development.