جست‌وجوی استاد و پژوهشگر

Ming Liمشاهده پروفایل

دانشیار

• Networking
• Mobile Systems
• Cybersecurity
• +۵ مورد دیگر

Ming Li is an Associate Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington. Her research focuses on networking, mobile systems (e.g., VR/XR), and cybersecurity, with an emphasis on applied AI, sensing, and computing. She holds a PhD from Mississippi State University and has held prior faculty positions at the University of Nevada Reno. Her work has been funded by NSF, DoT, and NIH, and she has received prestigious awards including NSF CRII and CAREER grants, as well as the N2Women Rising Star in Networking and Communications honor (2023). Education: PhD in Electrical and Computer Engineering, Mississippi State University, 2014 M.E. in Electrical Engineering, Beijing University of Posts and Telecommunications, 2010 B.E. in Electrical Engineering, Sun Yat-sen University, 2007 Research Interests: Dr. Li’s work spans IoT security, mobile computing, privacy-preserving computing, and human-centric systems. Her recent projects include developing VR-based authentication methods, detecting GPS trackers via EMR, and creating tools for cyclist safety using acoustic sensing. She has published over 35 papers in top venues like ACM CCS, INFOCOM, and IEEE journals. Recent Publications Trends: Her recent work emphasizes hardware security (e.g., EMR-based attacks), VR/XR system improvements, and smart city applications. Key areas include user authentication via novel sensing modalities, adversarial attack detection, and privacy-preserving techniques in IoT and crowdsourcing systems. Awards & Recognition: NSF CAREER Award (2020) N2Women Rising Star (2023) 40 Under 40 by Arlington City (2023) Best Paper Awards at IEEE Globecom’15 and DASC’17 Advising & Grants: Dr. Li has advised numerous graduate students, many of whom have gone on to academic and industry positions. Her grants include an NSF-funded project on privacy-aware VR QoE assessment and a DoT grant for cyclist safety. She also serves on editorial boards (e.g., IEEE Transactions on Vehicular Technology) and TPC roles for conferences like INFOCOM, NDSS, and CCS. Labs & Teams: Her research group, MobiSec Lab, focuses on mobile and network security, with active projects in wearable computing, edge AI, and smart infrastructure. Collaborations include work with the National Institute of Standards and Technology (NIST) on privacy metrics and with transportation agencies on vulnerable road user safety.

Yi Qiangمشاهده پروفایل

دانشیار

• GIScience
• Spatial Data Science
• Disaster Resilience
• +۴ مورد دیگر

Dr. Yi Qiang is an Associate Professor in the School of Geosciences at the University of South Florida, where he leads the Laboratory of Computational Spatial Science for Sustainability (COMPASS Lab). His research integrates GIScience, spatial data science, and geocomputation to address disaster resilience, environmental justice, and community vulnerability. He holds a Ph.D. from Ghent University, an M.S. from the University of Edinburgh, and a B.S. from Beijing Normal University. Research interests focus on multi-scale spatio-temporal modeling, disaster management using geospatial analytics, and analyzing social disparities related to natural hazards. Key areas include: Developing spatial data models for resilience assessment AI/machine learning applications in geocomputation Nighttime light remote sensing for economic disturbance analysis His publications demonstrate strong emphasis on geospatial analysis techniques applied to flood exposure, infrastructure resilience, and community recovery patterns following disasters. Recent work utilizes crowdsourced data and machine learning for urban flood mapping and transportation network analysis. Dr. Qiang advises multiple graduate students and has secured significant research funding including NSF grants for projects on cross-scale spatiotemporal modeling and flood damage assessment. He directs the COMPASS Lab, focusing on computational approaches to sustainability challenges.

Antonietta Lanzaمشاهده پروفایل

استادیار

• Man-Machine Interaction
• Machine Learning
• Knowledge Acquisition
• +۴ مورد دیگر

Antonietta Lanza is an Assistant Professor of Computer Science at the Department of Informatics, University of Bari, Italy. She earned her degree in Information Science from the same university in 1978 and has been a faculty member since 1984. Her research spans man-machine interaction, machine learning, and knowledge acquisition with applications in technical document processing and geospatial data interpretation. Education : Degree in Information Science (University of Bari, 1978) Affiliations : Department of Informatics, University of Bari Her research focuses on integrating machine learning with geospatial analysis, particularly for wind speed forecasting, topographic map interpretation, and cadastral data processing. She has developed algorithms for spatio-temporal modeling and spatial clustering of structured data. Recent publications primarily address wind power prediction, spatio-temporal lazy learning, and cluster-based time series modeling. Applications span renewable energy, environmental planning, and maritime AIS data analysis. She actively contributes to academic associations AI*IA (Italian Association for Artificial Intelligence) IAPR-IC (International Association of Pattern Recognition - Italian Chapter) and has created software like PS_package , a tool for line-drawing segmentation.

Sujata Murtyمشاهده پروفایل

استادیار

• Climate Change
• Oceanography
• Paleoclimatology
• +۳ مورد دیگر

Sujata Murty is an Assistant Professor in the Department of Atmospheric & Environmental Sciences at the University at Albany (SUNY), since Spring 2021. Her research focuses on reconstructing past ocean and climate systems using coral proxies to understand long-term hydrological and temperature changes. She holds a PhD in Earth Sciences from Nanyang Technological University (Singapore) and completed a postdoctoral fellowship at the Woods Hole Oceanographic Institution. Key research interests include Indo-Pacific oceanography, paleoclimatology, and climate variability analysis. Her work integrates coral geochemical records (δ18O, Sr/Ca, Ba/Ca, and Δ14C) with ocean models to study monsoon dynamics, Indonesian throughflow variability, and decadal-scale climate drivers. Notable projects include the CoralHydro2k database—a global compilation of coral proxy records to reconstruct tropical hydroclimate. Her publications emphasize interdisciplinary approaches to decode historical climate patterns, such as reconstructing Red Sea surface hydrology, Australian monsoon sensitivity to volcanic eruptions, and interhemispheric rainfall linkages. Collaborative efforts with international teams highlight her role in advancing proxy-based climate reconstructions and improving calibration methods for coral-derived data. While no formal awards are listed, her contributions to paleoceanography and climate science are evident through her extensive publication record and active participation in global initiatives like the PAGES CoralHydro2k Project.

Michael Grossniklausمشاهده پروفایل

استاد

• Data Management
• Information Systems
• Stream Processing
• +۳ مورد دیگر

Michael Grossniklaus is Professor for Databases and Information Systems at University of Konstanz's Department of Computer and Information Science. His research develops data management and processing technologies supporting data science applications, with emphasis on stream processing, context-aware systems, and web engineering solutions. Key research areas: Continuous query processing for streaming data Context-aware mobile data management Machine learning for database optimization Multi-channel web information systems Semantic web and RDF stream processing Publications demonstrate sustained contributions to database query processing, with recent work focusing on cardinality estimation using geometric deep learning and cloud database optimization. Developed C-SPARQL language extension for continuous RDF stream queries.

Emiris Ioannisمشاهده پروفایل

استاد

• Computer Algebra
• Computational Geometry
• Computer-Aided Geometric Design
• +۹ مورد دیگر

Emiris Ioannis is a Professor in the Department of Informatics & Telecommunications at the University of Athens. He holds a BScEng from Princeton University, MSc and PhD from UC Berkeley, and a Habilitation from the University of Nice Sophia Antipolis. His research focuses on computer algebra, computational geometry, robotics, bioinformatics, and fairness in AI. He has held tenured positions at INRIA, France, and has collaborated with institutions like ENS-Ulm, IHES, and the Ontario Research Center for Computer Algebra. His work bridges theoretical and applied areas, including structural bioinformatics, algorithm development, and machine learning. Notable contributions include tools for protein structure analysis (e.g., Hydraprot), explainable AI frameworks (GLANCE), and predictive models for renewable energy (wind speed forecasting). His research often intersects with interdisciplinary challenges in robotics, finance, and environmental sustainability. Emiris has organized international workshops and contributed to conferences like ISSAC. His publications span high-impact journals in computer science, mathematics, and bioinformatics. Despite no explicitly listed awards, his extensive academic contributions and leadership roles reflect his impact in theoretical informatics. His advising and grants focus on collaborative projects involving geometric algorithms, AI ethics, and data-driven decision systems. Research teams under his leadership often involve cross-disciplinary collaborations, addressing both fundamental and applied problems in computational science.

Nicky Wrightمشاهده پروفایل

پژوهشگر ارشد

• Marine Geoscience
• Palaeoclimatology
• Plate Tectonics
• +۲ مورد دیگر

Nicky Wright is a Research Fellow in the School of Geosciences at the University of Sydney and a member of the EarthByte Group. She holds an ARC Early Career Industry Fellowship and was a 2023–2024 Superstar of STEM. Her research focuses on marine geoscience, palaeoclimate, and paleogeography reconstruction, with expertise in plate tectonics and ocean basin evolution. Wright completed her PhD at the University of Sydney (2018) and was a postdoctoral fellow at the Australian National University (2018–2021). Education: PhD in Geosciences, University of Sydney, 2018 Postdoctoral Research, Australian National University, 2018–2021 (ARC Centre of Excellence for Climate Extremes) Research Interests: Nicky’s work integrates computational geoscience with field data to address questions about Earth’s tectonic history and climate dynamics. Key areas include reconstructing ancient topography, analyzing subduction zone processes, and modeling ocean basin evolution. Her projects often involve open-source tools like pyGPlates and GPlately for spatio-temporal data analysis. Publications: Her recent work includes studies on Australian drought variability, kimberlite eruption mechanisms, and the application of computational tools for plate tectonic reconstructions. These contributions highlight her interdisciplinary approach to geoscience challenges. Awards & Grants: ARC Early Career Industry Fellowship (2024): 'Hunting high and low: mapping ancient topography to find copper' Superstar of STEM (2023–2024) Advising & Collaboration: Wright leads projects such as the STELLAR initiative with BHP, focusing on resource exploration through spatio-temporal data analysis. She collaborates widely within EarthByte and international groups, contributing to global paleogeographic datasets and climate modeling efforts. Labs/Teams: EarthByte Group (University of Sydney), part of the Net Zero Institute. Active member of the GPlates community and contributor to open-source geoscience software.

Alan Woodleyمشاهده پروفایل

مدرس ارشد

• Data Science
• Environmental Management
• Computational Modeling
• +۳ مورد دیگر

Dr. Alan Woodley is a Senior Lecturer in the School of Computer Science at Queensland University of Technology (QUT) and leads the Spatial Informatics Laboratory. He holds a PhD in Computer Science from QUT and a Certificate in New Ventures Leadership from MIT. His roles include Associate Investigator at the Centre for Agriculture and the Bioeconomy, Australian Data Science Centre, and ARC Centre of Excellence for Mathematical & Statistical Frontiers. Education: PhD (2008), B.IT (Hons) (2003), B.IT (2003) from QUT; MIT Innovation Bootcamp (2018). Research focuses on applying big data solutions to environmental and mining domains, emphasizing data analytics, computational modeling, and visualization. Key areas include industrial sustainability, social-technical integration, and flood monitoring using remote sensing and social media. His work has attracted over $4M in funding and produced over 70 publications. Recent articles highlight innovations like flood mapping via spectral indices, social media-based disaster detection, and parallel clustering algorithms. Notable grants include projects with the US Department of Energy, Food Agility CRC, and CRC for Spatial Information. Awards: Vice Chancellor's Research Fellowship (2018), Bonneville Power Administration grants for salmon monitoring and fish density tools, and Food Agility CRC funding for circular economy frameworks. Teaching: Courses include Programming Principles, Web Development, and Large Scale Data Mining (encompassing 100–600 students annually). Collaborations: Partnerships with Lendlease, Bonneville Power Association, Queensland Government, and mining industry representatives to develop tools like community compost apps and land change systems. His Spatial Informatics Lab drives interdisciplinary projects in environmental informatics and geospatial data science.

Thomas Marlowمشاهده پروفایل

استادیار

• Environmental Sociology
• Urban Sociology
• Computational Social Science
• +۳ مورد دیگر

Thomas Marlow is an Assistant Professor of Sociology at Texas A&M University, specializing in environmental and urban sociology with a focus on computational methods. His research addresses environmental inequalities, climate policy barriers, and sustainable urban transitions. He holds a Ph.D. from Brown University and conducted postdoctoral research at NYU Abu Dhabi’s Center for Interacting Urban Networks (CITIES). Education Ph.D. in Sociology, Brown University (2020) Postdoctoral Fellowship, NYU Abu Dhabi (2016–2020) Research Interests Environmental Justice and Climate Policy Urban Mobility Networks and Spatial Inequality Computational Social Science and Big Data Analysis Legacy Industrial Pollution and Public Health His work has been featured in Socius , Environmental Research Letters , and media outlets like The Guardian and BBC . Courses taught include SOCI 420: Advanced Methods of Social Research. Media Coverage Highlights Ars Technica (2022): Sea-level rise and industrial toxins Boston Globe (2021): Mobility segregation analysis The Guardian (2020): Climate discourse bots Marlow’s research bridges social theory and empirical data science, with a focus on actionable policy insights for equitable urban futures.

Nikoleta Anicicمشاهده پروفایل

پژوهشگر

• Entomology
• Vector Ecology
• Invasive Species Monitoring
• +۴ مورد دیگر

Nikoleta Anicic is a Scientific Collaborator in Vector Ecology at the Department of Environment Constructions and Design (DACD) at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), where she focuses on monitoring and researching invasive mosquito species and their ecological impacts. Education: PhD in Evolutionary Ecology, University of Namur (Belgium), 2017 Master of Science in Biology with specialization in environmental microbiology and ecology, University of Zurich, 2017 Bachelor of Science in Biology, University of Neuchâtel, 2014 Anicic's research centers on vector ecology with particular expertise in mosquito monitoring and control. Her work combines molecular biology, entomology (specializing in mosquitoes), data management, ArcGIS, and programming in R to address public health challenges posed by invasive species. She has developed specialized skills in high-resolution optical identification of mosquito eggs and spatio-temporal modeling of invasive species dynamics. Her research has direct applications for arboviral disease surveillance and prevention, particularly for diseases like Dengue, Chikungunya, and Zika transmitted by invasive Aedes species. Analysis of her publication record reveals a strong interdisciplinary approach spanning entomology, environmental microbiology, and computational ecology. Her research trajectory shows progression from fundamental ecological studies on subterranean biodiversity and freshwater zooplankton to increasingly applied work on invasive mosquito surveillance. Recent publications demonstrate sophisticated integration of machine learning techniques with traditional ecological monitoring to predict seasonal mosquito population dynamics across multiple European countries. Nikoleta Anicic leads and participates in numerous research projects focused on invasive mosquito surveillance across Swiss cantons (including Zurich, Uri, Glarus, Schwyz, and the Romandy region) and Liechtenstein. These projects involve close collaboration with cantonal environmental offices and national authorities, with SUPSI serving as the national coordination center for invasive species monitoring designated by the Swiss Federal Office for the Environment. Her work directly informs Swiss public health strategies for controlling invasive mosquito species including Ae. albopictus, Ae. japonicus, and Ae. koreicus. At SUPSI, Anicic is a core member of the Vector Ecology Sector (SECOVETT) within the Institute of Microbiology. She contributes significantly to the Swiss mosquito network, providing scientific expertise for developing monitoring protocols, analyzing field samples, and interpreting spatial patterns of mosquito invasion. Her recent presentation at the 26th International Conference on Subterranean Biology demonstrates her continued engagement with broader ecological questions beyond her primary mosquito research focus.

Chao Huangمشاهده پروفایل

استادیار

• Large Language Models
• Graph Learning
• Recommender Systems
• +۳ مورد دیگر

Chao Huang is an Assistant Professor at the Department of Computer Science and Institute of Data Science at the University of Hong Kong (HKU). As the director of the Data Intelligence Lab@HKU, his research focuses on Large Language Models (LLMs), LLM Agents, Graph Learning, Recommender Systems, and AI for Smart Cities. He holds a PhD from the University of Notre Dame. Education: PhD in Computer Science from University of Notre Dame (USA). Research Interests: His work bridges machine learning with practical applications, including: Developing advanced LLM frameworks like GraphGPT and UrbanGPT Creating automated research tools (AutoAgent, AI-Researcher) Designing recommendation systems with LLM integration (RLMRec, LLMRec) Exploring spatio-temporal AI for urban challenges Notable Achievements: Over 11,000 Google Scholar citations (h-index 55), multiple top conference awards (WWW/SIGIR/KDD), and open-source projects with thousands of GitHub stars. His work has been recognized as most influential/pioneering in major AI conferences. Labs/Teams: Leads the Data Intelligence Lab, collaborating on projects like LightRAG, MiniRAG, and VideoRAG. The lab emphasizes open-source contributions with repositories on GitHub. Grants/Advising: Supervises PhD/MPhil students and offers research internships. Active in recruiting motivated researchers and students through HKU's programs.

• Database Systems
• Data Mining
• Machine Learning
• +۷ مورد دیگر

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.

Raul Castro Fernandezمشاهده پروفایل

استادیار

• Data Ecology
• Data Discovery
• Data Markets
• +۵ مورد دیگر

Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago and co-founder and Chief Research Officer at invocate. He originated the concept of 'data ecology,' which frames his research on how data moves through and transforms technological, economic, and social systems—and how we can design interventions to make those ecosystems more valuable, equitable, and resilient. He co-leads the Data Ecology research initiative at the Data Science Institute and co-runs Chicago Data Night, a forum that brings together industry and academia in Chicago. Dr. Castro Fernandez's research centers on data ecology, data discovery, data markets, and data integration. His work develops both theoretical frameworks and practical systems that help organizations find, evaluate, and use data effectively. He approaches data as a socio-technical phenomenon, examining how data shapes our world and how we can shape it back through technical, economic, and social interventions. His research bridges computer science, economics, and social science to address fundamental challenges in data ecosystems. His recent publications reveal a strong focus on applying large language models to data management challenges, particularly for tabular data discovery and integration. He has developed innovative systems like Pneuma for LLM-based tabular data navigation, Solo for natural language data discovery, and Nexus for correlation discovery in spatio-temporal data. His work also addresses critical challenges in data valuation, data markets, and responsible data sharing, with applications across industry and research contexts. SIGMOD Test-of-Time Award (2023) NSF CAREER Award (2024) Sloan Research Fellowship (2025) Dr. Castro Fernandez actively mentors students across multiple levels, advising PhD students including Qiming Wang, Yue Gong, and Zhiru Zhu, as well as numerous master's and undergraduate students. His group has developed influential systems including Data Station (for trustworthy data sharing), Ver (for view discovery), Metam (for goal-oriented data discovery), and Solo (for natural language data discovery). These systems address fundamental challenges in data discovery, sharing, and integration, with applications across various domains. He leads the Data Ecology research group at the University of Chicago, which focuses on developing technical, economic, and social interventions to make data ecosystems more valuable, equitable, and resilient. His team works at the intersection of database systems, machine learning, and economics to build practical systems that address real-world data challenges faced by organizations and individuals, with a particular emphasis on the socio-technical aspects of data sharing and discovery.

• Population Genetics
• Evolutionary Biology
• Aquaculture Genetics
• +۴ مورد دیگر

Professor Theodore Abatzopoulos is a leading faculty member in the Department of Biology, School of Sciences, Aristotle University of Thessaloniki, Greece. Internationally recognised for his integrative research in population genetics, evolutionary biology and aquaculture genetics, he has authored or co-authored more than one hundred peer-reviewed papers and conference contributions focused primarily on crustaceans (Artemia, Brachionus, crayfish), marine and freshwater fish, and other aquatic organisms of the Mediterranean and Balkan regions. Education & Academic Home Department of Biology, School of Sciences, Aristotle University of Thessaloniki (current Professor) Research Interests Professor Abatzopoulos employs molecular tools (microsatellites, mitochondrial DNA, SNPs, genomics) to address fundamental and applied questions in population genetics , phylogeography , speciation and conservation genetics . His work spans: Genetic structure of Artemia (brine shrimp) populations world-wide Cryptic species delineation in rotifers of the Brachionus plicatilis complex Impacts of aquaculture practices on the genetic integrity of gilthead sea bream ( Sparus aurata ), European sea bass ( Dicentrarchus labrax ) and Mediterranean mussel ( Mytilus galloprovincialis ) Phylogeography and conservation of freshwater crayfish ( Astacus astacus , Austropotamobius torrentium ) Post-glacial recolonisation patterns of red deer ( Cervus elaphus ) and wild boar ( Sus scrofa ) in the Balkans Evolutionary responses to environmental stress and the role of molecular chaperones Publication Trends (2014-2020) Between 2014 and 2020 his most recent 15 articles reveal a clear emphasis on integrative taxonomy (combining morphological and molecular data to resolve cryptic species), population connectivity in marine and hypersaline ecosystems, and the genetic consequences of aquaculture escapes . Molecular markers (microsatellites, mtDNA control region, 16S, COI) are routinely used to assess temporal stability, panmixia and conservation units across a diverse set of taxa. Scientific Awards & Recognition No specific awards are listed in the provided metadata. Advising & Collaborative Networks Professor Abatzopoulos has supervised numerous graduate students and post-docs whose names recur as co-authors (e.g., Alexandros Triantafyllidis, Athanasios Baxevanis, Ilias Kappas, Nikoletta Karaiskou, Spiros Papakostas, Stefania Maniatsi). He is the driving force behind the Hellenic Evolutionary Society (HEVOS), promoting evolutionary studies and science education across Greece. Laboratories & Teams He heads the Aquaculture & Population Genetics Laboratory at AUTH, a facility equipped for molecular ecology, genomics and cryopreservation of aquatic genetic resources. The lab maintains extensive reference collections of Artemia and Brachionus strains and collaborates with universities, hatcheries and conservation agencies throughout Europe, Asia and Africa.

• Geoinformatics
• Geographic Information Systems
• Spatial Databases
• +۵ مورد دیگر

Assoc. Prof. Renata Ďuračiová, PhD., serves as Associate Professor and Head of the Department of Global Geodesy and Geoinformatics at the Faculty of Civil Engineering, Slovak University of Technology in Bratislava. Her academic role centers on advancing geoinformatics through rigorous research in spatial data quality, uncertainty modeling, and mathematical GIS methodologies while leading departmental operations and curriculum development. Her research spans spatial databases, geospatial analyses, and environmental applications including Lidar processing, building footprint detection, and solar radiation modeling. Key contributions address bark beetle infestation prediction using satellite data and machine learning, alongside innovations in fuzzy logic for spatial database querying and heterogeneous data integration. Her work bridges theoretical geoinformatics with practical solutions for forest management and urban planning challenges. Analysis of her 2017-2025 publications reveals consistent focus on spatial data quality assessment, terrain parameter effects on environmental modeling, and algorithmic development for Lidar/satellite data processing. Recurring themes include uncertainty quantification in spatial analyses, cross-source data integration (e.g., OpenStreetMap/INSPIRE), and climate-driven ecological modeling. Her technical expertise manifests in specialized tools like point-cloud solar radiation calculators and predictive models for forest disturbances. Prof. Ďuračiová actively contributes to the geospatial community through leadership roles in the Cartographic Society of the Slovak Republic (Executive Committee since 2011) and Slovak Association for Geoinformatics (Presidency since 2011). Her professional engagement emphasizes advancing geoinformatics standards and applications within Central European contexts while mentoring through courses like Database Systems in GIS and Geodata Quality Analysis.