جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Data Mining
• High-Performance Computing
• +۵ مورد دیگر

Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.

Gaël Kermarrecمشاهده پروفایل

پژوهشگر

• Boundary Layer Meteorology
• Atmospheric Turbulence
• GNSS Applications
• +۳ مورد دیگر

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections

• Business Administration
• Operations Research
• Supply Chain Management
• +۶ مورد دیگر

Prof. Dr. Julia Rieck is a Full Professor of Business Administration at the University of Hildesheim , leading the Department of Business Administration and Operations Research within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science. As Dean of the Faculty , she oversees academic programs, quality management, and research initiatives. Her roles include academic advising for the Business Information Systems (B.Sc./M.Sc.) programs and active participation in examination boards and quality committees. Education: PhD in Political Science (Dr. rer. pol.) with summa cum laude (2008), Habilitation at Clausthal University of Technology (2014), and studies in Business Mathematics (Diploma, University of Hamburg, 2003) and Mathematics (Georg-August-University Göttingen, 2000). Research: Focuses on Operations Research , Supply Chain Management , Project Planning , and Logistics . Her work integrates mathematical modeling , machine learning , and real-world applications , particularly in disaster response , dynamic transportation , and sustainable e-commerce . Projects: Leads third-party funded initiatives like "IT für die sorgende Gesellschaft" (AI in healthcare/social sectors) and contributes to the HULLS real-lab (AI in aging societies). Collaborates with regional companies (e.g., Youco, ADITUS) and institutions (HAWK, University of Hannover). Teaching: Emphasizes practical application through case studies, industry partnerships, and the IT-Speed Dating event for student-company connections. Her courses cover project resource planning , logistics , and digital transformation . Labs & Teams: Active in the Institute of Business Administration & Business Information Systems , contributing to the KET Kompetenzwerkstatt (entrepreneurship support) and interdisciplinary teams in AI and sustainability research.

• Databases
• Spatial Data Management
• Geospatial Systems
• +۳ مورد دیگر

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

David Bermbach is a Full Professor at Technische Universität Berlin , leading the Scalable Software Systems group since 2023. His research focuses on distributed systems, serverless computing, and benchmarking, with significant work on edge and fog computing architectures. He is affiliated with the Einstein Center Digital Future and co-chairs interdisciplinary projects like SimRa for bicycle traffic safety. Full Professor, Scalable Software Systems (2023–present) ECDF-Professor, Mobile Cloud Computing (2017–2023) Postdoctoral Researcher (2014–2017) Education : Diploma in Business Engineering (2010) – Karlsruhe Institute of Technology (KIT) PhD in Computer Science (2014, summa cum laude) – KIT Research Interests span distributed systems with emphasis on cloud, edge, and fog computing, serverless architectures, IoT platforms, and benchmarking frameworks. His work addresses consistency-performance trade-offs, resource placement, and interdisciplinary applications in urban mobility and satellite edge computing. Article Trends show a focus on serverless computing (12/15), edge-cloud integration (9/15), and benchmarking methodologies (7/15). Key themes include optimizing function placement, federated learning architectures, and low-earth orbit computing systems. Scientific Awards Best Paper Award – ShutPub (2024) Best Workshop Paper – A Research Perspective on Fog Computing (2017) Best Paper Runner Up – Benchmarking Eventual Consistency (2014) Summa Cum Laude PhD Thesis (2014) Advising & Grants include mentoring students like Tobias Pfandzelter and Trever Schirmer, leading funded projects through the Einstein Center Digital Future, and contributing to 6G network research. His team works on cloud federation, serverless optimization, and real-world IoT applications.

Anika Großمشاهده پروفایل

استاد

• Database Systems
• Knowledge Graphs
• Ontologies
• +۷ مورد دیگر

Prof. Anika Groß is a Professor for Database Systems and Programming at the Department of Computer Science and Languages at Anhalt University of Applied Sciences in Köthen, Germany. She holds a PhD from the University of Leipzig (2014) and has held roles as a PostDoc in strategic research at Daimler AG and as a research assistant at the Interdisciplinary Centre for Bioinformatics (IZBI). She is actively involved in academic governance, serving on the faculty council, board of examiners, and various sustainability and research data management committees. Her research focuses on database systems, knowledge graphs, and ontology engineering, with applications in environmental science, medicine, and material sciences. Education - PhD (Dr. rer. nat.) in Computer Science, University of Leipzig (2014) - Diploma in Bioinformatics, MLU Halle-Wittenberg (2007) - Postdoctoral research at University of Leipzig's Database Group (2008–2013) Research Interests Her work emphasizes temporal graphs, ontology evolution, and semantic data integration. She develops tools like Region Evolution eXplorer (REX) and contributes to projects such as AgriRestore (ecosystem restoration) and MeineWaldKI (AI-based forest monitoring). Her recent focus includes wearable device applications in healthcare and digital twins for lithium value chains. Recent Research Trends Recent publications highlight interdisciplinary applications of knowledge graphs in agriculture and environmental science, leveraging temporal analysis and machine learning for tasks like chemical transformation prediction and bladder monitoring. She also explores cross-lingual semantic annotations in medical forms and holistic clustering of linked data. Professional Roles - Executive Committee Member, GI Database Systems Group - Fellow, Institute for Technologies and Economics of Lithium (ITEL) - Co-Chair of multiple workshops including FGDB-Workshop @ LWDA 2018 and BigDS@BTW 2017 Labs & Collaborations She leads research teams in projects funded by DFG, European Union (EFRE), and ITEL, collaborating with organizations like Daimler AG and ANU. Her lab focuses on advancing database systems and interdisciplinary data science applications.

Ingo Heidbüchelمشاهده پروفایل

پژوهشگر

• Hydrology
• Catchment Hydrology
• Hydrogeology
• +۳ مورد دیگر

Dr. Ingo Heidbüchel is a researcher in the Department of Hydrogeology at the Helmholtz Centre for Environmental Research - UFZ (Leipzig, Germany). His work focuses on understanding hydrologic processes, catchment hydrology, and transit time distributions. Previously, he held postdoctoral positions at the University of Bayreuth (2021), UFZ (2017–2020), and the GFZ in Potsdam (2013–2016). He earned his PhD from the University of Arizona (2008–2012) and a Diploma (Master equivalent) in Hydrology from Albert-Ludwigs-University Freiburg (2000–2007). Research interests include hydrologic transit times, catchment response dynamics, and hydrochemical interactions. His studies often employ tracer techniques and advanced modeling to explore processes like nitrogen cycling, groundwater-surface water mixing, and environmental change impacts on water systems. Key contributions include developing methods for analyzing transit time distributions in low-order catchments and investigating the effects of topography and seasonality on water chemistry. His work is supported by grants such as the DFG project on transit time dynamics in real-world catchments. Dr. Heidbüchel collaborates widely, contributing to initiatives like CAMELS-DE, a dataset of hydrometeorological time series for German catchments.

Jochen E. Gebauerمشاهده پروفایل

استاد

• Social Psychology
• Personality Psychology
• Cross-Cultural Psychology
• +۳ مورد دیگر

Prof. Jochen E. Gebauer leads the Chair of Cross-Cultural Social and Personality Psychology at the School of Social Sciences. His research focuses on self-concept from social and personality psychology perspectives, emphasizing cross-cultural approaches to identify universal processes. Key interests include agency, communion, religiosity, and their interplay with personality traits and societal contexts. His work explores topics such as obesity’s societal impacts, religiosity’s psychological benefits, and cultural influences on personality. Publications span over two decades, addressing global challenges like pandemics and segregation. Though no specific awards or grants are noted, his contributions highlight interdisciplinary collaborations across psychology, sociology, and public health. His team investigates themes like leadership dynamics, secularization trends, and the psychological effects of social stratification. While no student advisees are listed here, his research lab’s activities emphasize empirical studies combining large-scale data with cultural theory.

Markus Schartauمشاهده پروفایل

پژوهشگر

• Marine Biogeochemical Modeling
• Plankton Dynamics
• Ocean Ecosystem Modeling
• +۷ مورد دیگر

Markus Schartau is a Researcher in the Biogeochemical Modelling Research Unit at GEOMAR Helmholtz Centre for Ocean Research Kiel, where he has worked since 2013. His research focuses on marine biogeochemical modeling, plankton dynamics, and parameter optimization in ecosystem models. With over 69 publications spanning two decades, Schartau has established himself as a leading expert in marine biogeochemical modeling, particularly in the areas of model calibration, mesocosm experiment simulation, and plankton community dynamics. His research interests encompass uncertainty and trustworthiness of biogeochemical model results, species composition and size frequency structures of plankton, methods of parameter optimization and model selection, simulations of mesocosm experiments, physiological and ecological variations of plankton, and temporal and spatial variations of organic substances in coastal ocean areas and estuaries. Schartau's work often bridges theoretical modeling with experimental observations to improve our understanding of marine biogeochemical processes. The analysis of his recent publications (2020-2024) reveals a strong focus on marine biogeochemical modeling, with particular emphasis on plankton dynamics, carbon cycling, and the impacts of environmental change. His work spans multiple spatial scales from coastal systems to the global ocean and integrates approaches from data science, biogeochemistry, and ecosystem modeling. Key themes include model calibration techniques, mesocosm experiment analysis, marine particle dynamics, and the effects of ocean acidification on biogeochemical processes. Schartau actively participates in major research projects including OceanNETs (EU project), BASS (funded by DFG), ICEBERG (EU project), and μARC (funded by BMBF), demonstrating his integration into the international marine science community. His collaborative approach is evident in his extensive co-authorship network across multiple institutions and countries. His career trajectory shows progression from postdoctoral positions at Stony Brook University and the Alfred Wegener Institute to permanent research scientist roles at Helmholtz-Zentrum Geesthacht (now HEREON) and finally at GEOMAR. This path reflects his growing expertise and recognition in the field of marine biogeochemical modeling.

• Process Mining
• Business Process Management
• Data Analytics
• +۳ مورد دیگر

Lukas Pfahlsberger is a scientific collaborator at the Institute of Computer Science , Humboldt University of Berlin, within the Faculty of Mathematics and Natural Sciences. His work focuses on process mining and business process management. Research interests include: Causal process mining and knowledge integration Business process analytics and organizational capability development Big data governance and alignment methods Spatiotemporal analysis in process mining Recent publications explore temporal, multi-perspective, and causal approaches to process mining, with applications in IT demand management and digital transformation. His work bridges technical process mining methods with organizational theory and data governance frameworks. Contact: lukas.pfahlsberger@hu-berlin.de

• Database Systems
• Data Mining
• Social Network Analysis
• +۳ مورد دیگر

Eric Leclercq is a researcher at the University of Burgundy, affiliated with the LE2I Lab in Dijon, France. His work spans database systems, social network analysis, and biomedical data integration. He has contributed extensively to polystore systems, tensor decompositions, and category theory applications in data modeling. Fields of Interest : Database Systems, Data Mining, Social Network Analysis, Big Data Analytics, Semantic Web Leclercq's recent research focuses on formal frameworks for data lakes using category theory, multi-level tensor decomposition for social network stratification, and schema migration in multi-model systems. He has published in venues like CAiSE, IDEAS, and RCIS. His collaborations include Annabelle Gillet, Marinette Savonnet, and Nadine Cullot. Notable works include Lambda+ architecture for data processing, polarization analysis in social networks, and tools for tweet collection and biomedical data integration.

• Database Systems
• Web Data Extraction
• Information Integration
• +۴ مورد دیگر

Jayant Madhavan is a researcher at Google specializing in database systems, web data extraction, and information integration. His work primarily focuses on extracting structured data from the web, schema matching, and developing techniques for managing and visualizing large datasets, particularly through projects like Google Fusion Tables and WebTables. Madhavan's research interests center around the challenges of working with web data. His work explores methods for extracting structured information from unstructured web content, particularly focusing on tables and lists. He has made significant contributions to the field of schema matching, developing techniques that enable integration of data from diverse sources. His research also extends to geospatial data processing and visualization, where he has developed algorithms for efficiently handling large geographical datasets for map visualization. His publication record shows a consistent focus on practical applications of database research to web-scale problems. The evolution of his work demonstrates a progression from foundational research on schema matching and data integration to applied work on Google products like Fusion Tables, which enable non-experts to work with structured data. His most recent work examines the ecosystem of structured data on the web and how to effectively extract and utilize this information. Madhavan has collaborated extensively with Alon Y. Halevy (43 co-authored papers) and other researchers at Google, forming a core group that has advanced the state of the art in web data management. His work bridges theoretical database research with practical applications, making significant contributions to both academic literature and real-world data management systems.

Melanie Krauseمشاهده پروفایل

استاد

• Urban Economics
• Regional Development
• Sustainable Urban Growth
• +۷ مورد دیگر

Melanie Krause is a Professor of Sustainable Real Estate and Urban Development at Leipzig University, appointed in 2023. Previously, she served as a Junior Professor of Economics at Hamburg University (2015–2023) and held a Visiting Professorship at Leipzig University (2022–2023). She holds a Ph.D. in Economics from Goethe University Frankfurt (awarded summa cum laude ), a Master's from ESCP-EAP European School of Management (with Distinction), and undergraduate credentials from Bonn University. Her research explores urban systems through an economic lens, emphasizing sustainability challenges like air quality, housing markets, and income inequality. She employs quantitative methodologies including machine learning and geospatial data analysis (e.g., satellite-derived nighttime lights) to study global urban growth patterns, CO₂ emissions, and transportation efficiency. Interdisciplinary collaboration with natural scientists is central to her data-driven approach. In teaching, she blends theory with applied data analysis. Courses include Big Data and Smart Cities (machine learning for urban predictions), Introduction to Urban Economics , and Socio-Economic Issues of Urban Development . She emphasizes project-based learning using real-world datasets. Academic Honors: Ph.D. summa cum laude (Goethe University Frankfurt) Master of Science in Management (Distinction) Diplôme de Grande Ecole (Félicitations du Jury)

Jukka Matthias Krispمشاهده پروفایل

استاد

• Location Based Services (LBS)
• Geographic Visualization
• Visual Analytics
• +۷ مورد دیگر

Prof. Dr. Jukka Matthias Krisp is a Professor of Applied Geoinformatics at the Institute of Geography, Faculty of Applied Computer Science, University of Augsburg. He leads the Applied Geoinformatics research team focusing on Location Based Services, Geographic Visualization, Spatial Modeling, and GIS applications in ecological network planning. His research interests span multiple domains within geoinformatics: Location Based Services (LBS) including context modeling, navigation systems, and mobile applications Geographic Visualization and Visual Analytics for complex spatial data representation Spatial Modeling techniques for urban environments and transportation systems Geographic Information Systems applications in ecological network planning and environmental monitoring Indoor navigation systems and 3D spatial representation Prof. Krisp's recent publications demonstrate a strong focus on bicycle routing optimization, spatial analysis of social networks, and the integration of AI technologies like ChatGPT for geospatial data generation. His work often combines traditional GIS methodologies with emerging technologies to address urban mobility challenges, traffic congestion analysis, and sustainable transportation planning. He has made significant contributions to the field of Location Based Services, particularly in context-aware computing and spatial data processing. Among his notable scientific contributions: Development of surface roughness-centric approaches to bicycle routing Innovative methods for estimating night populations using mobile network data Integration of big data and cartographic techniques for understanding urban mobility patterns Application of fuzzy inference systems for traffic congestion analysis Narrative approaches to indoor navigation using 360-degree camera documentation Prof. Krisp actively supervises students and collaborates with researchers internationally. His team includes Pablo Löw, Zulfa Nur'aini Afifah, and former members like Lika Zhvania. He teaches courses such as Advanced Spatial Analysis, Geoinformation Systems and Cartography, and Visual Geodata Mining at the University of Augsburg.

D. Frantzمشاهده پروفایل

استادیار

• Earth Observation
• Remote Sensing
• Geoinformatics
• +۷ مورد دیگر

D. Frantz is an Assistant Professor at Trier University , leading the Geoinformatics - Spatial Data Science lab. Their research focuses on transforming Earth Observation (EO) data into actionable environmental insights through preprocessing, data cubes, and machine learning. Current Position : Assistant Professor for Geoinformatics - Spatial Data Science, Trier University (2021–today) Former Role : PostDoctoral Researcher at Earth Observation Lab, Humboldt-Universität zu Berlin (2017–2021) Education : PhD in Environmental Remote Sensing and Geoinformatics, Trier University (2013–2017) Frantz's research emphasizes open science , with all software and methods published as open source ( FORCE ). Key research areas include: EO data preprocessing to analysis-ready data Multi-sensor data integration (Sentinel, Landsat) Land cover fraction mapping and time series analysis Material stock quantification in buildings/infrastructure Climate change mitigation through urban-rural gradient analysis Recent publications highlight work on global material stock mapping , tree species classification in temperate forests, and temporally transferable crop mapping using deep learning. While no explicit awards are listed, their open-source contributions and leadership in the Geoinformatics lab underscore significant academic impact.