جست‌وجوی استاد و پژوهشگر

• Corporate Finance
• SMEs
• Family Businesses
• +۴ مورد دیگر

Elisabete Gomes Santana Felix is an Associate Professor at the University of Évora. She holds a PhD in Management from ISCTE (2008), a Master's in Business Sciences from INDEG/ISCTE (1999), and a Bachelor's in Business Management from the University of Évora (1993). Her research focuses on Corporate Finance, SMEs, Family Businesses, Venture Capital, and Social Entrepreneurship. Her educational background includes: PhD in Management, ISCTE (2008) Master in Business Sciences, INDEG/ISCTE (1999) Degree in Business Management, University of Évora (1993) Felix's research explores diverse aspects of financial ecosystems, including venture capital dynamics, financial distress modeling, and socioeconomic impacts of microcredit. Her work frequently examines how organizational structures and market conditions influence business sustainability, with a strong emphasis on empirical analysis across global emerging and developed markets. Her publications demonstrate consistent focus on venture capital mechanisms, SME financing, and corporate governance, with recent work expanding into gender impacts in family businesses and sustainable development frameworks. Research frequently employs comparative methodologies across European and Asian economic contexts. Felix actively advises graduate researchers, having supervised 35+ Master's students and 3 PhD candidates. Major research projects include: Corporate Governance in Medium Income Countries: The Case of Portugal (2007-2009) Programa para a Valorização, Inovação e Transferência de Tecnologia no Alentejo (2021-2023)

• Schema discovery for Semantic Web data
• Data mining
• Recommendation systems
• +۵ مورد دیگر

Kenza Kellou-Menouer is a researcher affiliated with the ETIS Laboratory at ENSEA, France, and part of the MIDI research group . Her work focuses on schema discovery for Semantic Web data, data mining, and big data optimization. Research: Semantic schema discovery, clustering/classification algorithms, and association rules. Teaching: Semantic Web technologies, database design, algorithms, and programming languages (Java, C++, C#, C). Research Interests center on Semantic Web data integration, RDF schema inference, and hybrid machine learning approaches. She has contributed to scalable schema discovery systems and real-time profiling techniques for large datasets. Publications include work on schema inference tools (SchemaDecrypt++, HInT) and methodological frameworks presented at top-tier venues like VLDB (A*), ICDE (A*), SSDBM (A), and ISWC . Her research bridges theoretical advancements with practical implementations for RDF datasets. Community Contributions include organizing tutorials at the International Semantic Web Conference (ISWC) 2022 and developing educational materials for database and programming courses.

Alessandro Lameiras Koerichمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Pattern Recognition
• +۶ مورد دیگر

Alessandro Lameiras Koerich is a Professor in the Department of Software Engineering and IT at École de technologie supérieure (ÉTS), part of the Université du Québec network in Montreal, Canada. He is affiliated with the LIVIA (Imaging, Vision and Artificial Intelligence Laboratory), where he conducts research in machine learning, computer vision, and pattern recognition. His work focuses on developing AI solutions for real-world applications in healthcare, security, and multimedia analysis. His research interests span multiple domains of artificial intelligence, with particular emphasis on multimodal machine learning, affective computing, and trusted machine learning systems. Dr. Koerich's work explores how AI can interpret human emotions through facial expressions and audio analysis, develop robust models resistant to adversarial attacks, and create efficient learning systems that can adapt to changing data streams. His research bridges theoretical advances with practical applications in healthcare monitoring, security systems, and multimedia content analysis. Analysis of his recent publications reveals a strong focus on multimodal emotion recognition, with increasing emphasis on transformer architectures, knowledge distillation techniques, and addressing challenges in real-world settings (in-the-wild recognition). His work consistently combines computer vision with audio processing, demonstrating expertise in both visual and auditory modalities for affective computing applications. Recent trends show growing interest in large language models for audio classification and addressing challenges of concept drift in text stream mining. Dr. Koerich actively supervises numerous graduate students across various research projects, with a particular focus on multimodal expression recognition, music genre classification, and robust machine learning systems. His collaborations span multiple institutions and research groups, reflecting the interdisciplinary nature of his work in artificial intelligence. He is a key member of the LIVIA laboratory, which has established ÉTS as a leading institution in computer vision research in Canada (ranked 6th according to CSRankings). The laboratory focuses on solving real-life problems through AI engineering, particularly in developing complex deep learning models with massive amounts of data that have incomplete annotations.

Michel Dumontierمشاهده پروفایل

استاد

• FAIR data
• generative artificial intelligence
• machine learning
• +۹ مورد دیگر

Dr. Michel Dumontier is a Distinguished Professor of Data Science at Maastricht University, where he serves as the founder and Director of the Institute of Data Science. He is internationally recognized as a co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles, which have transformed scientific data management globally. His academic background includes: BSc in Biochemistry from the University of Manitoba (1999) PhD in Bioinformatics from the University of Toronto (2004) Assistant/Associate Professor at Carleton University (2005-2013) Associate Professor at Stanford University (2013-2016) Distinguished Professor at Maastricht University (2017-present) Dr. Dumontier's research focuses on unlocking data potential for scientific discovery, with expertise in knowledge graphs for drug discovery and personalized medicine. His work spans FAIR data principles, generative AI, machine learning, semantic technologies, ontology, and data integration. His recent publications reveal a strong trend toward applying generative AI to healthcare data, with increasing focus on synthetic health data generation, privacy-preserving techniques, and knowledge graph applications in drug repurposing. His work bridges computer science, biomedical informatics, and clinical applications across multiple medical domains. Dr. Dumontier has secured significant research funding as a principal investigator: NWO (Dutch Research Council) Horizon Europe MCSA NIH/NCATS ARPA-H He coordinates the AIDAVA and REALM projects, leads the NCATS Biomedical Data Translator, and directs the GENIUS AI lab. As editor-in-chief of the journal Data Science, he shapes discourse in the field. Dr. Dumontier maintains active industry connections through: Minderheidsaandeelhouder at Data2Discovery Inc Scientific advisor and minority shareholder at OntoForce NV Scientific advisor, board member, and minority shareholder at Comunicare Editor-in-chief of Data Science Journal at Sage Publishing

• Water Resources Engineering
• Integrated Water Resources Management
• Rainwater Harvesting
• +۷ مورد دیگر

Jean-Marc Mwenge Kahinda is an Associate Professor of Water Engineering at the University of the Witwatersrand's School of Civil and Environmental Engineering in Johannesburg, South Africa. With over 20 years of experience in the water sector, he has established himself as a leading expert in water resources engineering and management, particularly in the Southern African Development Community (SADC) region. His educational background includes: PhD from University of the Witwatersrand MSc (Eng) in Water Resources Engineering and Management from University of Zimbabwe BSc (Eng) in Mining Engineering from University of Lubumbashi (DRC) Professor Mwenge Kahinda's research focuses on the critical intersection of land and water management, with particular emphasis on sustainable practices that address land degradation in semi-arid regions. His work integrates hydrological processes, ecosystem management, and climate change adaptation strategies to develop practical solutions for water security challenges. He has pioneered research on rainwater harvesting systems, developing GIS-based decision support tools that have contributed to nationwide implementation of these practices in South Africa. Analysis of his recent publications reveals a strong focus on land degradation assessment in the Greater Sekhukhune District Municipality, utilizing advanced geospatial techniques and social-ecological systems approaches. His research increasingly incorporates remote sensing technologies (particularly Sentinel-2 satellite imagery) and multi-criteria analysis to understand the complex interactions between human activities, climate change, and land degradation processes in semi-arid environments. As an active academic, he serves as guest editor for the Elsevier Journal of Physics and Chemistry of the Earth and as an academic editor for PLOS Water. He regularly reviews for accredited journals and research reports in his field. Professor Mwenge Kahinda supervises PhD and MSc students across multiple universities and has extensive experience lecturing on water-related subjects throughout the SADC region. He organizes and conducts specialized training and short courses on water management topics. His leadership in multidisciplinary research projects focuses on securing and managing water resources through integrated approaches that consider both technical and social dimensions of water management challenges.

Chia-Wen Chenمشاهده پروفایل

پژوهشگر

• Item Response Theory
• Forced-Choice Items
• Computerized Adaptive Testing
• +۳ مورد دیگر

Dr. Chia-Wen Chen is a Psychometrician at the Psychometrics Centre within the Cambridge Judge Business School , specializing in Executive Education. Originally from Taiwan, he holds a PhD in Psychometrics from the Education University of Hong Kong (2018), preceded by a master's degree in Psychology from National Chung Cheng University (2012). His career spans institutions in Taiwan, Hong Kong, Norway, and the UK, including postdoctoral research at the University of Oslo's Centre for Educational Measurement (2019-2023). PhD in Psychometrics (2018), Education University of Hong Kong MSc in Psychology (2012), National Chung Cheng University Postdoctoral Researcher (2019-2023), University of Oslo Chia-Wen's research focuses on Item Response Theory (IRT) models for forced-choice and compositional items , with applications in computerized adaptive testing (CAT) , differential item functioning (DIF) , and multilevel modeling . His work includes developing novel IRT methods for ranking items and Most-Least formats, addressing reliability/validity in educational scales like the Principal Instructional Management Rating Scale (PIMRS), and analyzing large-scale datasets (PISA) for cross-national educational insights. Recent publications demonstrate expertise in ipsative testing , online parameter estimation , and factor mixture modeling for educational diagnostics. He has also contributed to methodologies for CAT algorithms in educational evaluation and fault line analysis in school leadership teams.

Christos Ouzounisمشاهده پروفایل

استاد

• Bioinformatics
• Digital Biology
• Biological Computation
• +۵ مورد دیگر

Christos Ouzounis is a Professor of Bioinformatics at the Department of Informatics, Aristotle University of Thessaloniki , with a career spanning institutions including the European Bioinformatics Institute , King's College London , and University of Toronto . His work bridges Computational Biology , Digital Biology , and Metagenomics , focusing on large-scale data analysis, machine learning applications, and functional annotation of proteins. Education : BSc in Biological Sciences (1986), MSc in Biological Computation (1987), and DPhil in Computational Chemistry (1993) Key Roles : Director of the Bioinformatics Centre at King's College London (2007-2010), Research Director at IDEP-EKETA (2014-2020) His research interests include low-complexity protein sequences , Covid-19 seasonality patterns linked to UV radiation, and metagenomic analysis of urban microbiomes in cultural heritage sites. Current projects involve machine learning models for microbial coexistence networks, ontological classification of biomedical literature, and bioinformatics tool development . Publications highlight trends in archaeal genomics , functional dark matter in metagenomics, and epidemiological modelling . Notable collaborations include work on BioTextQuest v2.0 for concept discovery and MjCyc for metabolic pathway analysis.

Charlotte Changمشاهده پروفایل

استادیار

• Conservation Biology
• Social-Ecological Systems
• Quantitative Ecology
• +۵ مورد دیگر

Charlotte Chang serves as Assistant Professor of Biology and Environmental Analysis at Pomona College, where she has been faculty since 2020. Her dual appointment bridges the Department of Biology and Environmental Analysis program, with laboratory space in Seaver South. She maintains active affiliations including a Visiting Science Fellowship with The Nature Conservancy's One Conservancy program and collaborations with Centre for Wildlife Studies India and Wildlife Conservation Society. Ph.D. in Ecology and Evolutionary Biology, Princeton University Master of Philosophy, University of Cambridge Bachelor of Arts, Pomona College Chang's research integrates social-ecological systems analysis to advance conservation science, specializing in digital conservation constituencies, illicit wildlife hunting dynamics, and agroforest management for habitat specialists. Her lab employs innovative methodologies combining data science, quantitative ecology, and field methods to examine human-environment interactions across tropical systems. Current projects include LLMs for natural climate solutions evidence mapping and environmental social media analysis for conservation outreach optimization. Her publication record demonstrates consistent high-impact output across conservation biology, ecological modeling, and social science journals. Trends show increasing focus on digital conservation mobilization since 2020, with notable work on Twitter environmental discourse and platform migration patterns. Earlier research established foundational work on hunting motivation in Southwest China and coffee agroforest biodiversity in India's Western Ghats. David H. Smith Conservation Research Fellow National Science Foundation NIMBioS Postdoctoral Fellow IUCN Critical Ecosystem Partnership Fund Research Grant NSF GROW with USAID research fellowship Fulbright Fellowship Chang actively mentors early-career researchers across North America and Asia, with notable success in placing mentees in conservation organizations and graduate programs. Her lab receives funding from multiple NSF programs and conservation NGOs, supporting fieldwork in China, India, and Southeast Asia. Current grants focus on social media analysis for conservation outreach and natural climate solutions mapping. The Chang Lab operates as an interdisciplinary hub combining computational ecology, conservation social science, and field research. Her team collaborates with environmental practitioners, community leaders, and academic colleagues globally, with recent projects spanning the Western Ghats, Southwest China, and digital conservation networks worldwide.

• Self-regulated Learning
• Learning Analytics
• Online Education
• +۴ مورد دیگر

Dr. Jacqueline Wong is an Assistant Professor in the Department of Education and Pedagogy at the Faculty of Social and Behavioural Sciences, Utrecht University. Her academic work focuses on enhancing educational practices through evidence-based research in self-regulated learning and learning analytics, with particular applications in higher education contexts. Dr. Wong earned her Ph.D. (cum laude) from Erasmus University Rotterdam with a thesis entitled "Enhancing Self-Regulated Learning Through Instructional Supports and Learning Analytics in Online Higher Education," which received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. She completed her Master's degree (cum laude) in Educational Psychology, where she examined the use of movements to support text comprehension. Prior to joining Utrecht University, she was a postdoctoral researcher at Delft University of Technology (2020-2022) working with the Program of Innovation in Mathematics Education (PRIME). Dr. Wong's research program spans several interconnected areas: the application of learning analytics to support self-regulated learning, the design of instructional interventions that promote metacognitive awareness, and the investigation of how emerging technologies like generative AI can enhance educational experiences. Her work frequently examines how technology can be leveraged to support students' metacognitive processes and goal-setting behaviors in both traditional and online educational settings. She has made significant contributions to understanding how conversational agents and learning analytics can provide personalized support for students' self-regulated learning processes. Analysis of her recent publications reveals a clear trajectory toward integrating emerging technologies with established educational theories. Her 2024-2025 work increasingly focuses on the intersection of artificial intelligence and educational psychology, particularly examining how generative AI tools can enhance academic text accessibility and provide adaptive support for learning. She also continues to investigate cognitive aspects of learning in educational games and augmented reality environments, with implications for both educational practice and learning theory development. Among her notable achievements, Dr. Wong received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. This recognition highlights the significant contribution her doctoral work has made to the field of educational psychology and learning sciences. Her systematic reviews, including examinations of student engagement in mathematics education and gamification in MOOCs, have provided valuable syntheses of research in growing areas of educational technology. Dr. Wong has been an active research member of the Leiden-Delft-Erasmus Centre for Learning and Education (LDE-CEL) and the network for Self-Regulated Learning in Digitalised Schools (SeReLeDiS). Her collaborative approach to research is evident in her extensive co-authorship network across multiple institutions. Before joining Utrecht University, she worked with lecturers to promote student motivation and self-regulated learning in blended service mathematics courses and examined the effectiveness of technology tools for learning mathematics. Dr. Wong's work bridges theoretical frameworks with practical implementations, often involving close partnerships with educators to ensure research relevance to real-world classroom challenges. Her current research appears to be moving toward deeper integration of AI technologies with educational theory, particularly examining how generative AI can support metacognitive processes and self-regulated learning across diverse educational contexts.

Nicolai Fabianمشاهده پروفایل

پژوهشگر

• Digital Transformation
• Knowledge Engineering
• Artificial Intelligence
• +۴ مورد دیگر

Nicolai Fabian is a researcher at the Faculty of Economics and Business, University of Groningen, specializing in Digital Transformation and its intersections with organizational strategy, ethics, and technology. His work addresses critical challenges in digital governance, artificial intelligence applications, and business model innovation for SMEs. Key Research Areas: Digital Transformation, Knowledge Engineering, AI Ethics, and Digital Governance. Recent Collaborations: Frequent co-authorship with scholars like Dong, Broekhuizen, and Verhoef on topics spanning cybersecurity, digital trace data, and platform dynamics. Research Trends (2022–2025): Focuses on the dual-edged impacts of digitalization, from economic value creation in SMEs to dystopian risks in AI and cybersecurity. Explores how organizations adapt to digital disruption through strategic learning, ethical frameworks, and data-driven governance. Network & Visibility: Active in international collaborations and cited across multidisciplinary domains, reflecting his engagement with global research communities.

• Computational Linguistics
• Formal Grammar
• Minimalist Grammars
• +۴ مورد دیگر

Jens Michaelis is a Professor at the Faculty of Linguistics and Literary Studies, Bielefeld University, specializing in Computational Linguistics, Text Technology, and Linguistic Creativity. He serves as Deputy Head of the Department of Linguistics, Clinical Linguistics, Text Technology and Computational Linguistics, and provides academic advising for Computational Linguistics programs. His office is located at UHG U5-231, with contact details including telephone +49 521 106-6915 and email jens.michaelis@uni-bielefeld.de . Maintaining active research roles, he leads project B01 'Coercion as a creative mechanism in compositional interpretation' within the SFB 1646: Linguistic Creativity in Communication. His teaching responsibilities span modules like 23-CL-BaCL2.1 Selected methodological aspects 23-CL-BaCL5 Advanced Module 23-CL-BaCL6 Project Module 23-LIN-Ma3.1 Basics of Computational Linguistics 23-MeWi-HM3a_a Mathematical-linguistic language modeling across both undergraduate and graduate programs. His scholarly work focuses on formal grammar properties, syntactic mechanisms, and computational modeling of language. As an ordinary member of the Faculty Conference and Habilitation Committee, he contributes to academic governance while maintaining an extensive publication record in mathematical linguistics, minimalist grammars, and formal language theory.

Wayne Wobckeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.

• Soil and Groundwater Remediation
• Quantitative Risk Assessment
• Pollutant Migration
• +۳ مورد دیگر

Chen Mengfang is a Professor and doctoral supervisor at the Key Laboratory of Soil Environment and Pollution Remediation, Nanjing Institute of Soil Science, Chinese Academy of Sciences, holding this position since 2010. His expertise spans industrial site pollution remediation and groundwater resource management with global applications. Education: B.S. in Coal Sedimentology, China University of Geosciences, 1985 Ph.D. in Sedimentary Geochemistry, Newcastle University, UK, 1990 Chen's research centers on soil-groundwater pollutant migration dynamics and in-situ biochemical remediation technologies , coupled with advanced quantitative risk assessment methodologies . He specializes in numerical simulation of groundwater flow, geochemical evolution modeling, and human/ecological risk quantification for contaminated sites including brownfields, abandoned mines, and landfills. His work integrates field investigations with computational approaches to develop site-specific remediation frameworks. Publication analysis reveals consistent focus on risk assessment model development (CLEA integration, RBCA software) and high-impact case studies including London 2012 Olympic Park remediation, Shanghai chemical works assessment, and UK pesticide facility cleanup. His research bridges theoretical modeling with industrial implementation through 150+ technical reports. No scientific awards are documented in the source material. As a doctoral supervisor, Chen leads CAS Knowledge Innovation Projects on POPs-contaminated sites while partnering with GSI Environmental Group for RBCA software development. He conducts industry-wide technical training on quantitative risk assessment and has secured project funding through international collaborations including UK-based site remediation contracts. Based at the Key Laboratory of Soil Environment and Pollution Remediation, Chen operates within a multidisciplinary team conducting field investigations and numerical modeling. His international network spans UK environmental agencies, Chinese industrial sites, and US consulting firms, with recent work focusing on POPs-contaminated site remediation technologies.

Martin Raubalمشاهده پروفایل

استاد

• Spatial Decision-Making
• Mobile Geographic Information Systems (GIS)
• Location-Based Services (LBS)
• +۷ مورد دیگر

Prof. Dr. Martin Raubal is a Full Professor at the Institute for Cartography and Geoinformation, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich. He leads research on spatial decision-making, mobile GIS, and location-based services, with applications in transportation, energy, and aviation. His work integrates cognitive engineering and eye-tracking to analyze human mobility and interaction with geospatial systems. Doctorate in Geoinformation (with distinction), Vienna University of Technology (2001) MS in Spatial Information Science and Engineering, University of Maine (1997) Dipl.-Ing. in Surveying Engineering and Geoinformation, Vienna University of Technology (1998) His research focuses on spatiotemporal human mobility, cognitive models for GIS, and sustainable urban systems. Key projects include the Digital Underground initiative at the Singapore-ETH Centre and the Empirical Use and Impact Analysis of Mobility-as-a-Service (EIM). Awards include the UV-Helava Prize and multiple best paper recognitions. Recent publications emphasize 3D spatial analysis, AI-driven geospatial tools, and causal inference in mobility modeling. Collaborations span institutions like Lufthansa Systems, SBB, and ETH-IVT. His lab investigates applications such as electric vehicle charging networks, bikeability indexes, and augmented reality in transportation planning.

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

Berta María Guijarro Berdiñas is a Researcher in the Department of Computer Science and Artificial Intelligence at the University of A Coruña , Spain. She is affiliated with the Laboratory for Research and Development in Artificial Intelligence and teaches courses like Machine Learning , Development of Intelligent Systems , and Programming at both undergraduate and postgraduate levels. Research Focus: Her work lies at the intersection of Artificial Intelligence , Machine Learning , and Knowledge-Based Systems . Key contributions include frugal learning (limited data), anomaly explanation , and distributed learning for edge devices. She applies these to areas like health informatics , forest fire management , and human-robot interaction . Recent Publications span explainable AI , anomaly detection , multi-agent systems , and low-power machine learning . Her articles appear in top venues like Expert Systems with Applications and IEEE Transactions on Neural Networks and Learning Systems . Grants & Projects include EU-funded initiatives, Spanish Ministry of Science grants, and regional collaborations. She focuses on AI for healthcare , smart systems , and distributed learning .