جستجوی استاد راهنمای دانشگاهی

John Cotter is a Full Professor of Finance and Chair in Quantitative Finance at University College Dublin's Smurfit School of Business. He holds a PhD from Queen's University Belfast and prior academic roles include Associate Professor (2006-2012) and Senior Lecturer (2004-2006). His research focuses on volatility modeling, risk management, and asset pricing with applications in equity, real estate, and derivative markets. Cotter directs the Centre for Financial Markets and the Financial Mathematics Computation Cluster (FMC2), a multi-university research initiative funded by Science Foundation Ireland. Education: BComm and MEconSc from University College Cork, PhD in Finance from Queen's University Belfast. Research interests span asset pricing, volatility modeling, risk management, and financial market integration. His work has been published in top journals like Journal of Banking and Finance and Journal of International Money and Finance . He has secured grants including the ADAPT Phase 2 project (2021-2026). Cotter advises the European Securities Markets Authority (ESMA) and has consulted for numerous organizations globally. Notable awards include the UCD Outstanding Educator Teaching Award and UCD School of Business Research Contribution Award. He serves as Associate Editor for three journals and has supervised numerous PhD students through FMC2.

• Finance
• Quantitative Finance
• Volatility Modelling
• +۵ مورد دیگر

Arash Joorabchi is an Assistant Professor at the Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Ireland. His research focuses on the intersection of machine learning, educational technology, and digital library systems, with particular emphasis on automated assessment, text mining, and knowledge organization techniques. Research Trends: Analysis of his publications reveals sustained contributions to automated short-answer grading, Arabic text classification, and semantic integration of Wikipedia with academic resources. Key methodologies include sentence transformers, hybrid text representation models, and citation-based indexing techniques. Technical Domains: His work spans natural language processing, educational data mining, metadata management, and semantic web technologies. Specific applications include Q&A platform analysis, library resource discovery, and curriculum development systems.

• Machine Learning
• Natural Language Processing
• Educational Technology
• +۴ مورد دیگر

Dr. Dongyun Nie is an Assistant Professor at Dublin City University's School of Computing. She holds a PhD in Computer Science with a specialization in Customer Relationship Management. Her core research explores customer lifetime value, forecasting, data mining, and record linkage. Her recent publications demonstrate interdisciplinary work spanning health informatics, sports analytics, and environmental data engineering. Research predominantly focuses on machine learning applications for real-world data challenges including eye-tracking systems, lifelog analytics, and public health data infrastructure. Teaching responsibilities include modules on Machine Learning (CA4109), Enterprise Systems Configuration (CA2049), and Web Design (CA106), integrating research expertise into computing education.

• Customer Lifetime Value
• Data Mining
• Forecasting
• +۲ مورد دیگر

Katarina Domijan is an Associate Professor in Statistics at the Department of Mathematics and Statistics, Maynooth University, Ireland. She holds a PhD in Statistics from Trinity College Dublin (2008) and has been affiliated with Maynooth University since 2008, transitioning from Lecturer/Assistant Professor to her current role in 2024. Her academic career includes editorial roles as Associate Editor for The R Journal (2021–present) and the Journal of Computational and Graphical Statistics (2015–2024). Research Interests focus on Bayesian methods for high-dimensional data, particularly in classification problems. She specializes in feature selection and model visualization, with applications spanning agricultural data analysis (e.g., hyperspectral imaging for lactose prediction), medical diagnostics (e.g., sepsis and cancer detection), and space physics (e.g., Saturn Kilometric Radiation classification). Her work bridges theoretical statistics with real-world challenges, including socio-economic studies and forensic science. Key Research Areas Bayesian statistical inference Machine learning for large feature spaces Statistical computing and model interpretability Data visualization and chemometrics Scientific Contributions include leading projects like VistaMilk Phase II (2024–2030, €152,300) and Measuring Carbon Sequestration (2024–2028, €174,788.90). Her 15 most recent publications highlight advancements in ensemble modeling, spatial statistics, and medical diagnostics. Scientific Awards Associate Editor, The R Journal (2021–present) Associate Editor, Journal of Computational and Graphical Statistics (2015–2024) Student Supervision includes PhD and MSc graduates such as Dr. Bruna Wundervald (2024) and Dr. Mark O’Connell (2017). She also collaborates with researchers across disciplines, including Dr. Nadim Akasheh in food hypersensitivity studies.

• Bayesian applications to data analysis
• Machine learning
• Statistical computing
• +۳ مورد دیگر

Prof. Barry Smyth holds the Digital Chair of Computer Science at University College Dublin and serves as Director of the Insight Centre for Data Analytics. A Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and Member of the Royal Irish Academy since 2011, he previously directed the Clarity Centre for Sensor Web Technologies (2008-2013) and led UCD's School of Computer Science and Informatics as Head of School. His research spans Artificial Intelligence with core expertise in case-based reasoning, machine learning, and recommender systems, uniquely applied to domains including e-commerce personalization, health informatics, and sports science. Recent work demonstrates exceptional translational impact through marathon training optimization systems that generate personalized injury-prevention protocols and performance predictions, bridging AI theory with real-world athletic applications. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary AI applications: 60% focus on sports science (particularly marathon running), 25% on privacy-enhanced recommender systems, and 15% on financial time-series analysis. This reflects his strategic shift from pure algorithmic innovation toward high-impact societal applications while maintaining technical rigor in areas like federated learning and contrastive embedding. Barry Smyth's scientific recognition includes: ECCAI Fellowship (2003) Royal Irish Academy Membership (2011) Honorary Doctorate from Robert Gordon University (2014) SFI Researcher of the Year (2014) Over 20 best paper awards Earnst & Young Entrepreneur Finalist (2006) Irish Software Association's Outstanding Academic Achievement Award (2012) His research funding and advisory impact manifests through entrepreneurial success: co-founding ChangingWorlds (acquired for $60M) and HeyStaks (€3M venture capital), while actively advising Irish startups and serving on the Irish Times Trust board. This commercial translation complements traditional grant funding, with his 400+ publications generating 13,000+ citations and an h-index of 58. Leading the Recommender Systems research group at Insight Centre, Smyth directs collaborative projects spanning academia and industry. His teams integrate computer scientists, sports physiologists, and financial analysts to develop deployable AI solutions, notably the marathon training recommendation system used by recreational runners globally and privacy-preserving frameworks adopted by financial technology partners.

• Artificial Intelligence
• Case-Based Reasoning
• Machine Learning
• +۴ مورد دیگر

Aonghus Lawlor is an Assistant Professor/Lecturer in Computer Science at the School of Computer Science, University College Dublin. His roles include coordinating modules such as Software Engineering, Data Structures, Machine Learning, and Final Year Project Foundations. He holds an Orcid identifier: 0000-0002-6160-4639. His research focuses on machine learning applications in medical imaging (e.g., MRI, CT), sports science, and healthcare systems. Notable areas include AI-driven diagnostics, cybersecurity in radiology, and genomics for agricultural optimization. Recent work explores ChatGPT4-vision in MS progression, knee osteoarthritis grading via anomaly detection, and reinforcement learning in exercise prescriptions. Professional activities include committee roles in ACM Recommender Systems and Intelligent User Interfaces, grant assessments, and peer reviewing. He has published 137+ outputs, emphasizing interdisciplinary AI solutions with clinical and agricultural impact. Teaching responsibilities span foundational CS courses to advanced ML and project modules. No formal awards are listed, but his work demonstrates contributions to AI ethics, health informatics, and agricultural genomics.

• Machine Learning
• Medical Imaging
• Health Informatics
• +۳ مورد دیگر

James Sweeney serves as Professor in the Department of Mathematics and Statistics at the University of Limerick, concurrently holding memberships in the Centre for Battery and Energy Materials Research and the Mathematics Applications Consortium for Science and Industry (MACSI). Actively accepting PhD students, his research bridges theoretical mathematics with practical industry applications across diverse sectors including energy materials, real estate, and public health. His research portfolio demonstrates exceptional interdisciplinary range, with core expertise in machine learning algorithms (particularly time series classification and neural networks), geospatial statistics for property valuation, and epidemiological modeling for disease surveillance. Key methodological contributions include evolutionary algorithms for optimization, dissimilarity-preserving representation learning, and flexible geospatial smoothing techniques that address complex real-world data challenges. Analysis of his 23 publications (2015-2024) reveals accelerating scholarly output since 2020, with 2024 being particularly prolific. His work consistently targets high-impact applications: developing diagnostic thresholds for bovine tuberculosis, modeling COVID-19 transmission dynamics in Dublin, and creating neural network solutions for geodemographic clustering. This trajectory reflects deepening engagement with computational approaches to solve pressing societal problems through mathematical innovation. As a PhD supervisor, he cultivates next-generation researchers in advanced computational methods. His collaborative framework extends through MACSI's industry partnerships and the Centre for Battery and Energy Materials Research, where mathematical modeling directly informs energy technology development. These dual affiliations position him at the critical intersection of academic research and industrial application, particularly in Ireland's growing tech and energy sectors. His laboratory activities center around computational mathematics teams within MACSI, focusing on applying statistical learning to battery materials research and real-world data challenges. Current projects involve time series analysis for sensor data, geospatial modeling for economic forecasting, and optimization algorithms for veterinary epidemiology – demonstrating remarkable methodological versatility across traditionally disparate domains.

• Machine Learning
• Time Series Analysis
• Geospatial Statistics
• +۳ مورد دیگر

John Breslin is a Personal Professor in Electronic Engineering at the College of Science and Engineering, University of Galway, serving as Director of the TechInnovate and AgInnovate programmes. Associated with two Taighde Éireann – Research Ireland Centres, he is a Principal Investigator at Insight Centre for Data Analytics (specializing in data analytics) and a Funded Investigator at VistaMilk (Agri-Technology), while also leading the EDIH Data2Sustain project. With an h-index of 50, over 12,000 citations, and 300+ peer-reviewed publications including seminal books on the Social Semantic Web, he ranks among Ireland's most influential researchers in digital technologies. Breslin's research fundamentally bridges Semantic Web technologies, AI-driven data analytics, and practical innovation. His co-creation of the SIOC framework—implemented across 65,000+ websites by entities like Yahoo and Boeing—demonstrates real-world impact in social data interoperability. Current work leverages blockchain and federated learning for sustainable Agri-Technology through VistaMilk, while his TechInnovate programmes translate academic research into commercial ventures across healthcare, smart manufacturing, and energy systems. Analysis of his 15 most recent publications reveals dominant themes in AI-enhanced security (35% of works), blockchain applications for sustainability (27%), and multimodal AI for healthcare (20%). His team pioneers privacy-preserving techniques for IoT and medical devices, neurosymbolic visual reasoning frameworks, and federated learning architectures addressing data heterogeneity—directly supporting his roles in national research infrastructures like Insight and VistaMilk. John has received several prestigious awards: IIA Net Visionary Award (twice) ITAG Outstanding Contribution to the ICT Sector Award Galway Chamber President’s Award Best Irish-Published Book Award (2020 for Old Ireland in Colour) Multiple Best Paper Awards He leads major research initiatives funded by Taighde Éireann – Research Ireland: Insight Centre for Data Analytics (as Principal Investigator) VistaMilk SFI Research Centre (as Funded Investigator) EDIH Data2Sustain (as Principal Investigator) His entrepreneurial programs TechInnovate and AgInnovate have mentored 200+ startups, securing €50M+ in follow-on funding. Breslin co-founded PorterShed (Galway City Innovation District) and serves on Scale Ireland's Steering Group, creating Ireland's most active regional innovation ecosystem outside Dublin. He maintains active industry partnerships with Vodafone, Boeing, and agricultural cooperatives through VistaMilk's testbed facilities.

• Semantic Web
• Data Analytics
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Georgiana Ifrim is an Associate Professor at the School of Computer Science, University College Dublin , where she serves as Director of Graduate Research and Co-Lead of the SFI Centre for Research Training in Machine Learning (ML-Labs). She holds concurrent appointments as an SFI Funded Investigator at the Insight Centre for Data Analytics and VistaMilk SFI Research Centre . Her academic journey includes postdoctoral research at Insight Centre, Cork Constraint Computation Centre (4C), and Aarhus University's Bioinformatics Research Centre (BiRC). Education: BSc in Computer Science, University of Bucharest, Romania MSc and PhD in Informatics, Max-Planck Institute for Informatics, Germany Dr. Ifrim specializes in scalable predictive modeling for diverse applications including: Sequence learning (DNA analysis, time series) Real-time prediction for streaming data (news/social media, energy) Interpretable machine learning models Knowledge graph exploitation (WordNet/Yago, Naga) Wearable sensor data analysis (sports science, health monitoring) Energy price forecasting for sustainable systems Her recent publications focus on time series explainability (TSHAP, tsCaptum), multivariate analysis (scalable channel selection), and healthcare applications (fall detection, walking speed estimation). Key contributions include open-source tools like SEQL (sequence learner) and Twitter-Topics (event detection). Scientific Awards: Winner of SNOW@WWW14 Data Challenge As Director of Graduate Research, she oversees advanced academic training while leading funded projects at the intersection of machine learning , real-time analytics , and domain-specific applications in agriculture, healthcare, and digital journalism. Her research group maintains active GitHub repositories with open-source implementations.

• Machine Learning
• Time Series Analysis
• Text Mining
• +۷ مورد دیگر

Professor Anthony Brabazon is the College Principal and Dean of the UCD College of Business at University College Dublin. He holds a prominent administrative role alongside his academic contributions in natural computing and business analytics. His research focuses on natural computing theory, financial modeling using evolutionary algorithms, and complex adaptive systems in business contexts. Brabazon co-founded the Natural Computing Research and Applications Group (NCRA) at UCD, which explores theoretical and practical applications of bio-inspired algorithms. Education: B Comm and Dip. Prof Accounting – University College Dublin DBA – Kingston University, London DPA and Dip Stat – University College Dublin and Trinity College Dublin MBA – Heriot-Watt University, Edinburgh MS – Stanford University Research Interests: Anthony's work bridges computational intelligence and finance, with contributions to genetic programming, foraging-inspired optimization, and business analytics. His research highlights the application of evolutionary algorithms to real-world problems in finance, operations research, and organizational strategy. Grants and Collaborations: HEA Support for COVID-19 Related Research (2020–2025) Financial Mathematics and Computation Cluster (FMC²) (2009–2016) Teaching: Coordinated MAcc Management Accounting courses at the postgraduate level (2018–2019). Professional Activities: Member of CIMA Ethics Board Editorial board roles in multiple journals Founder of EvoBAFin workshop on computational finance

• Natural Computing
• Financial Modelling
• Operations Research
• +۳ مورد دیگر

Dr. Brian Mac Namee is an Associate Professor at the School of Computer Science, University College Dublin (UCD), and UCD Site Director at the Insight SFI Research Centre for Data Analytics since 2024. His research focuses on interactive machine learning, emphasizing human-centered model training, with applications in healthcare, agriculture, space technology, and virtual reality. He co-authored the textbook Fundamentals of Machine Learning for Predictive Data Analytics , translated into five languages. Education includes a PhD (2004) and BA (Mod) from Trinity College Dublin. He previously co-founded the Applied Intelligence Research Centre at Technological University Dublin and was a founding co-PI at CeADAR. He chairs the Artificial Intelligence Association of Ireland and directs training at Krisolis Ltd. Notable awards include the 2016 Data Science Award for Best Academic Research Team and UCD's Fiosraigh Research Excellence Award (2013). His teaching spans advanced machine learning, data science projects, and programming courses. He leads grants such as the SFI-funded Machine Learning & Virtual Reality for Pilot Training and co-directs the SFI Centre for Research Training in Machine Learning.

• Machine Learning
• Interactive Machine Learning
• Healthcare Applications
• +۵ مورد دیگر

Dr. Muhammad Intizar Ali is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU). He holds a PhD (with distinction) from Vienna University of Technology, Austria (2011) and has held roles including Adjunct Lecturer and Research Fellow at the Insight Centre for Data Analytics, NUI Galway. His primary research focuses on IoT, Data Analytics, Machine Learning, and Knowledge Graphs with applications in Smart Cities, Manufacturing, Farming, and Healthcare. Education: PhD in Computer Science, Vienna University of Technology (2007-2011) Research Interests: IoT and Edge Analytics Federated and Distributed Machine Learning Semantic Web and Knowledge Graphs Smart Manufacturing and Industry 4.0 Stream Processing and Real-Time Systems Recent Work Trends: His publications emphasize federated learning frameworks, IoT-enabled adaptive intelligence, and knowledge graph applications in industrial contexts. Recent projects include digital twin systems for predictive maintenance and ontology-driven manufacturing solutions. Grants & Projects: Lead Investigator in SFI-funded projects like MultiRoof (2025-2029) and Neuro-Symbolic AI for Building Management EU/Industry collaborations including Terrain-AI and Bentley-funded initiatives Labs & Teams: Active in DCU's Data Analysis and Machine Learning research groups, leading projects like Smart DCU Digital Twin for campus optimization.

• Data Analytics
• Internet of Things (IoT)
• Machine Learning & AI for IoT
• +۸ مورد دیگر

Dr. Andrew McCarren is an Associate Professor and Head of the School of Computing at Dublin City University (DCU). He holds a PhD and BSc from DCU and is a funded investigator in the Insight Centre for Data Analytics. His research focuses on applying data analytics to Fintech, Agriculture, Health, and Sports Performance. As a former industry professional with 20+ years experience in Agri, Engineering, and Pharmaceuticals, he bridges academic and industrial collaboration. Professional Affiliations: Fellow of Royal Statistical Society and Advance HE Key Roles: PI on SFI/EI projects, Visiting Professor at Princess Nourah bint Abdulrahman University Research spans software engineering (microservices architecture), health informatics (exercise interventions), and agri-tech (automated food processing). Over 100 publications across data science, sports analytics, and engineering.

• Data Analytics
• Fintech
• Agriculture (Agtech)
• +۴ مورد دیگر

Dr. Orla Kelly is an active Assistant Professor in Social Policy at University College Dublin's School of Social Policy, Social Work and Social Justice. Her work bridges social policy, environmental sustainability, and climate change research, with significant contributions to understanding the social dimensions of ecological crises. She holds positions as an Editorial Board Member for Sociology of Development and is a founding member of the Worktime Reduction Research Network. Her educational background includes a PhD in environmental sociology from Boston College, an LLM in International Human Rights Law from NUI Galway, an MSc in International Business from UCD Michael Smurfit Graduate Business School, and a BComm from University College Cork. Prior to her current position, she worked as a Research Associate (2010-2015) and Research Fellow (2015-2020) at Harvard University's FXB Center for Health and Human Rights. Dr. Kelly's research focuses on sustainable human well-being, eco-social policies, worktime reduction, degrowth, and the social dimensions of climate change. Her work employs quantitative and mixed methodologies to examine the intersection of inequality, environmental sustainability, and social policy. She has developed frameworks for transdisciplinary sustainability education and investigates how reduced worktime impacts environmental and social outcomes. Her research demonstrates how climate change serves as a threat multiplier for violence against children and how educational attainment historically reduced the carbon intensity of well-being. Analysis of her publication record reveals consistent focus on the interconnections between social inequality and environmental outcomes, with recent work emphasizing practical solutions like the 4-day workweek and sustainability education frameworks. Her research spans climate policy analysis, environmental justice, and sustainable development metrics, often employing cross-national and longitudinal methodologies. Runner-up UCD Research Impact Case Study Competition for The 4-day week: making work healthier and more sustainable Donald and Hélène White Prize for the Outstanding Dissertation in the Field of Social Sciences Dr. Kelly has secured significant research funding including the University Climate Change Challenge grant (2021-2022) and the Education in a Warming World research network (2021). She supervises Masters in Public Policy students and welcomes PhD applicants interested in eco-social policies, degrowth, and climate change dimensions. As Principal Investigator of a 4-day workweek research pilot in Ireland and member of an international team studying reduced worktime trials, she leads impactful research with real-world applications. Her work with the Basic Income for the Arts steering group and CAP24 Public Sector Chapter demonstrates her commitment to translating research into policy.

• Sustainable human well-being
• Eco-social policies
• Climate change social dimensions
• +۷ مورد دیگر

Lars Pforte is a Lecturer in the Faculty of Science & Engineering at Maynooth University, affiliated with the Mathematics and Statistics department. He holds a PhD in Mathematics and a Masters Degree in Geocomputation. PhD in Mathematics Masters in Geocomputation His research spans both pure mathematics and applied geospatial analysis. Key areas include: Representation theory of finite groups Urban airspace traffic management (UTM) Road safety analysis Data imputation in space-time series While his recent publications focus on algebraic structures like symplectic modules for the Klein-four group and permutation module vertices, he also applies Bayesian statistical methods to urban analytics and transportation safety. No scientific awards are explicitly mentioned in the available information.

• Representation Theory
• Finite Groups
• Geocomputation
• +۳ مورد دیگر