جستجوی استاد راهنمای دانشگاهی

• Operations Research
• Public Transport Systems
• Digital Transformation
• +۳ مورد دیگر

Leif Sörensen is a researcher at the University of Hildesheim , affiliated with the Institute of Business Administration and Business Informatics within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science . His work focuses on operations research and digital transformation in public transport systems, particularly examining flexible transport models, agent-based simulations, and geoinformation system integration. Core Affiliations Researcher (since 2023) - HAWK Hochschule für Angewandte Wissenschaft und Kunst, Holzminden Doctoral Researcher - Institute of Business Administration and Business Informatics, University of Hildesheim Former Student Assistant - TU Dresden and Max Planck Institute for Self-Organization Research Focus: Sörensen's research bridges transport policy , agent-based modeling , and data analytics . He investigates how digital transformation can enhance rural mobility through flexible public transport systems , with emphasis on contractual incentives for drivers and stochastic behavior prediction . His work spans both developed and developing economies , particularly analyzing informal transit systems in South Africa. Publication Trends: Recent articles demonstrate a strong focus on operations research in transport contexts, with 7/9 publications involving agent-based simulations and DRT (Demand-Responsive Transport) . Keywords cluster around urban mobility , digital transformation , and transport resilience , while sub-fields reveal expertise in dynamic routing algorithms , passenger acceptance metrics , and GIS-integrated transport planning . Key Projects Co-author on European Commission-funded transport safety modeling Participant in Smart Hybrid research initiative (44 co-publications) Contributor to International Transportation Economics Association conferences Academic Background: Holds degrees in International Economics (University of Göttingen) and International Business and Development (University of Bayreuth). Completed internships at UNECE Geneva and KfW Entwicklungsbank Pretoria , with fieldwork in South Africa and Japan .

Daniel Kühbacherمشاهده پروفایل

مربی

• Environmental Monitoring
• Greenhouse Gas Monitoring
• Urban Emissions
• +۳ مورد دیگر

Daniel Kühbacher is a Tutor and researcher at the Chair of Environmental Sensing and Modeling at Technische Universität München (TUM). He specializes in developing high-resolution urban emission inventories for CO2, CH4, and co-emitted species, and leads the setup of a 100-sensor CO2 network in Munich to assess sector-specific emission factors. His work bridges environmental monitoring, sensor technology, and urban climate science. Teaching roles include tutoring the Environmental Sensing and Modeling lecture and advanced seminar, as well as the joint practical course Gemeinschaftspraktikum MST . Research focuses on integrating traffic simulation data, mobile measurement units, and flux footprint modeling to quantify urban greenhouse gas emissions. Education: M.Sc. in Environmental Engineering Affiliations: Member of the ICOS Cities project and contributor to the ICOS Science Network Publications emphasize urban GHG monitoring innovations, including sensor network optimization, flux measurement validation, and inventory intercomparison studies. His work supports policy-relevant insights into emission hotspots and mitigation strategies. Currently develops the SCOUT project for street-level carbon observatories and explores human respiration emissions using mobile network data.

• 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.

Jochen Linßenمشاهده پروفایل

استاد

• Energy Systems
• Renewable Energy
• Hydrogen Technology
• +۳ مورد دیگر

Prof. Dr.-Ing. Jochen Linßen is a Professor at the Institute of Climate and Energy Systems (ICE) , specifically within the Jülich Systems Analysis (ICE-2) group at the Research Center Jülich GmbH. His work focuses on advancing energy systems modeling, renewable energy integration, and hydrogen technology. Key research areas include the techno-economic analysis of energy transition pathways, hydrogen cost-potentials, and the interplay between energy systems and transportation networks. His expertise spans climate policy, infrastructure planning, and the development of frameworks for long-term mobility demand modeling. He has contributed to high-resolution assessments of renewable energy resources and the evaluation of green hydrogen’s role in decarbonizing industries. Recent work emphasizes AI-driven risk mitigation in energy systems and the impact of natural hazards on energy infrastructure resilience. Research Interests: Modeling of energy systems and hydrogen networks Renewables integration and curtailment challenges Transportation electrification and hydrogen fueling infrastructure Climate policy and decarbonization strategies Key Projects: ETHOS. MODE. regional framework for mobility demand analysis Global assessments of wind and solar power potentials Hydrogen supply chain optimization for Germany and Europe His collaborative approach bridges engineering, economics, and environmental science to address systemic challenges in energy transitions. Ongoing efforts include participatory mapping of green hydrogen potentials in sub-Saharan Africa and analysis of automated driving’s impact on road networks.

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +۳ مورد دیگر

Prof. Dr. Frank Kirchner is a leading expert in robotics and artificial intelligence, serving as Executive Director of DFKI Bremen and Professor of Robotics at the University of Bremen since 2002. He founded the Robotics Innovation Center (DFKI Bremen) and co-founded the Brazilian Institute of Robotics (2013) and Institute for Maritime Technologies (MarTech, 2010). Education: Doctorate in Informatics (Dr. rer. nat, Bonn, 1999); Diploma in Informatics and Neurobiology (Bonn, 1994) Research Interests focus on biologically inspired robotic motion, highly redundant systems, AI-driven autonomous robotics, and cross-domain applications in space, maritime, and industrial contexts. His work bridges theoretical and applied robotics, emphasizing simulation-reality gaps and adaptive control frameworks. Recent Publications highlight advancements in co-design optimization for manipulators, planetary exploration robotics, and sensor-based industrial assembly learning, reflecting interdisciplinary trends in AI, mechatronics, and environmental adaptation. Scientific Awards: Honorary Doctorate (2017, SENAI CIMATEC, Brazil) BBAW Membership (2015–) Advising includes supervising 65 doctoral theses (38 completed, 27 in progress) and mentoring over 250 bachelor’s/master’s students at the University of Bremen. He leads major projects like AI-REEFSHIELD and FieldCoBots , with funding from EU, DFG, BMBF, and DLR.

Constantin Grigoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Uncertainty Quantification
• Stochastic Differential Equ://grigo.userweb.mwn.de/ueber-mich/
• +۲ مورد دیگر

Constantin Grigo is a PhD researcher at the Technical University of Munich (TU Munich), actively engaged in the Continuum Mechanics group. His work focuses on Uncertainty Quantification (UQ) and Machine Learning (ML), particularly for applications in maritime safety, bicycle traffic modeling, and stochastic systems. He has presented his research at major conferences like SIAM UQ and WCCM, and has been recognized with Student Travel Awards from SIAM UQ 2018 and SIAM CSE 2019. Education: Master of Science in Physics, LMU Munich (2015) Bachelor of Science in Physics, LMU Munich (2012) Year abroad at Grenoble INP (2010-2011) Research Interests: Probabilistic machine learning for coarse-graining high-dimensional systems Bayesian model and dimension reduction Stochastic differential equations in heterogeneous media Microscopic traffic simulation for bicycles and autonomous vehicles Digital twin applications for maritime and urban mobility Reduced-order modeling of random materials Selected Awards: SIAM UQ 2018: Student Travel Award Winner SIAM CSE 2019: Student Travel Award Winner His publications span topics such as data-driven scenario specification for autonomous vehicles, bicycle maneuver prediction using neural networks, and physics-constrained surrogates for UQ. He also contributes to open-source simulation tools like SUMO for traffic modeling.

Stefan Julichمشاهده پروفایل

استاد

• Soil physics and soil hydrology
• Influence of land use on soil hydraulic properties
• Dynamics of material and water cycles in soils and landscapes
• +۵ مورد دیگر

Stefan Julich is a Professor of Landscape Studies at Eberswalde University for Sustainable Development, where he has held a professorship since 2022. He is affiliated with the Faculty of Landscape Management and Nature Conservation, contributing to both teaching and research activities. His academic career spans multiple prestigious institutions across Europe, including TU Dresden, Justus-Liebig University Giessen, and the Potsdam Institute for Climate Impact Research. Dr. Julich earned his Diploma in Geography with minors in Geology and Soil Science from Martin-Luther University Halle-Wittenberg (1998-2004), followed by a Dr. rer. nat. (magna cum laude) from Justus-Liebig University Giessen in 2010. His dissertation focused on uncertainties in modeling water and nitrogen cycles in micro- and mesoscale river catchments. His research primarily investigates soil physics and hydrology, with particular emphasis on the influence of land use on soil hydraulic properties, dynamics of water and material cycles in landscapes, and ecosystem services of soils. He employs advanced modeling techniques to simulate water and material flows from soil to landscape scales and develops innovative monitoring concepts and laboratory methods for environmental assessment. Analysis of his recent publications reveals a strong focus on tropical and temperate hydrology, with particular attention to land use change impacts, soil erosion processes, and water resource management in data-scarce regions. His work often bridges theoretical modeling with practical applications in environmental management, particularly in African and European contexts. Professor Julich actively contributes to long-term environmental observation systems, notably the Ecosystemic Environmental Observation (ÖUB) in Brandenburg's biosphere reserves, which monitors 136 sites across various ecosystem types. His research integrates abiotic (soil and water) and biotic (vegetation and fauna) components in comprehensive ecosystem assessments. He teaches courses in soil science, hydrology, and soil-water science within the Bachelor's programs in Landscape Use and Nature Conservation and Organic Agriculture and Marketing. His research projects focus on long-term ecosystem monitoring in biosphere reserves, examining how regional ecosystems evolve under changing environmental conditions.

• Spatial Modeling
• Temporal Modeling
• Semantic Modeling
• +۷ مورد دیگر

Prof. Thomas H. Kolbe serves as Chair of Geoinformatics at the Technical University of Munich (TUM), where he leads research in spatial, temporal, and semantic modeling of urban environments. His work focuses on developing foundational frameworks for 3D/4D city models, digital twins, and smart city applications through international standardization efforts including CityGML and IndoorGML. His research spans virtual city modeling, urban system simulation, and GIS integration with emerging technologies. Current projects emphasize AI-driven urban scenario generation, semantic streetspace modeling, and IoT integration in digital twin ecosystems. Recent publications demonstrate strong interdisciplinary connections between computer vision, urban planning, and geospatial data science, with particular emphasis on practical implementations of 3D city models for sustainability challenges. Prof. Kolbe actively contributes to professional organizations including the Round Table GIS eV (as Chairman since 2013) and the Munich Data Science Institute (as core member since 2021). His leadership extends to the Leonhard Obermeyer Center for digital methods in the built environment and the Hans Eisenmann Forum for agricultural sciences. His work bridges theoretical geoinformatics with practical urban applications through numerous collaborative projects with municipal governments and industry partners.

Masahiro Ryoمشاهده پروفایل

استاد

• Environmental Data Science
• Artificial Intelligence
• Biodiversity Science
• +۵ مورد دیگر

Masahiro Ryo is a Professor of Environmental Data Science at Brandenburg University of Technology (BTU) and leads the working group "Artificial Intelligence for Smart Agriculture" at the Leibniz Centre for Agricultural Landscape Research (ZALF). His research integrates machine learning with ecological systems to address global sustainability challenges, focusing on biodiversity, soil health, and AI-driven agricultural solutions. His work spans environmental data science, ecosystem services, and smart agriculture. Key themes include explainable AI for biodiversity monitoring, soil organic carbon prediction, and machine learning applications in ecological modeling. Recent publications highlight trends in environmental AI, with applications in yield mapping, fungal taxonomy, and global change ecology. He emphasizes the integration of ecological theory with deep learning to tackle small-data problems and improve model transferability. Contact: masahiroryo@gmail.com | Personal Website

Yelda Turkanمشاهده پروفایل

دانشیار

• Digital twins
• Scan-to-BIM
• Machine learning
• +۷ مورد دیگر

Dr. Yelda Turkan is an Associate Professor in the School of Civil and Construction Engineering at Oregon State University and a Hans Fischer Fellow at the Technical University of Munich (TUM-IAS). Her research focuses on leveraging computer vision, machine learning, and digital twins to enhance sustainability and resilience in the built environment. She holds a Ph.D. in Civil Engineering from the University of Waterloo, Canada, and dual B.Sc. degrees in Civil Engineering and Geomatics Engineering from Istanbul Technical University, alongside an M.Sc. in Remote Sensing. Her work integrates advanced technologies like LiDAR and BIM to improve construction monitoring, structural safety, and fire protection. She has authored over 80 peer-reviewed publications and secured $4.5M in grants from agencies like the NSF. Dr. Turkan serves as Vice President of the International Association for Automation and Robotics in Construction and chairs the ASCE Computing Division Executive Committee. Her research spans digital twins for infrastructure performance, fire safety simulations, and automated progress tracking. Key contributions include frameworks for bridge construction monitoring, fire safety management in timber structures, and BIM-based energy fault detection. Awards include the Robert C. Wilson Faculty Scholar (2023–2025) and ASCE ExCEEd Teaching Fellow (2015).

• Geographic Information Systems (GIS)
• Map Generalization
• Spatial Data Mining
• +۳ مورد دیگر

Jan-Henrik Haunert is a Professor at the University of Bonn, affiliated with the Institute of Cartography and Geoinformatics within the Faculty of Civil Engineering and Geodetic Science. His research focuses on map generalization, geographic information systems (GIS), and optimization techniques in cartography. He has contributed to projects funded by the German Research Foundation (DFG), particularly in deriving scale-dependent representations of geographic data. His work emphasizes logical consistency, semantic accuracy, and quality assessment in geospatial data processing. Key research areas include land cover map generalization, spatial data integration (e.g., volunteered geographic information), and rule-based incremental generalization. He has developed methodologies leveraging mixed-integer programming and straight skeleton algorithms for cartographic automation. His publications span peer-reviewed journals like GeoInformatica and Photogrammetrie - Fernerkundung - Geoinformation , as well as conference proceedings such as AGILE and ACM-GIS. Haunert’s contributions address challenges in geospatial data quality, including polygon simplification and river dataset matching. He has also explored applications in vehicle localization and virtual reality systems like the GeoScope for urban planning. His work bridges theoretical GIScience with practical applications in cartography and geoinformatics.

Heidi Hastedtمشاهده پروفایل

پژوهشگر

• Photogrammetry
• Optical 3D Metrology
• Remote Sensing
• +۹ مورد دیگر

Heidi Hastedt is a Researcher at the Institute of Photogrammetry and GeoInformation (IAPG) , part of Jade University of Applied Sciences . Her work spans photogrammetry , optical 3D metrology , and remote sensing , with a focus on camera calibration , Structure from Motion (SfM) , and software development in C++/C# and ESRI ArcObjects . Education : Diploma in Surveying (Dipl.-Ing. (FH)) from University of Applied Sciences Oldenburg/Ostfriesland/Wilhelmshaven (2001). Master of Engineering (M.Eng.) in Geoinformation Systems from Anhalt University of Applied Sciences (2015). Research Interests : Heidi's expertise lies in optical 3D measurement systems for applications like cultural heritage preservation and dynamic process monitoring . She specializes in camera modeling , system development , and quality control in SFM workflows. Her 15 most recent articles (2005–2022) highlight advancements in 3D reconstruction , cultural heritage digitization , and high-dynamic process capture using photogrammetric methods. Projects & Grants : Heidi has led research on geometric monitoring of the Bremen Cog (2017–present, funded by the German Maritime Museum) and accuracy evaluation of SFM methods (2003–2005, AGIP). Earlier work (2000–2002) focused on optical 3D system verification, and she contributed to Lower Saxony Advance initiatives (2005–2010) for dynamic 3D metrology. Laboratory Involvement : Heidi co-manages the Laboratory for Optical 3D Metrology , where she develops and tests photogrammetric tools for large-volume measurements and real-time monitoring of historical and dynamic objects.

• Photogrammetry
• 3D Metrology
• Laser Scanning
• +۳ مورد دیگر

Dr. Darius Popovas is a researcher at the Institute for Applied Photogrammetry and Geoinformatics (IAPG), Jade University of Applied Sciences, Oldenburg, Germany. He is a member of the Laboratory for Optical 3D Metrology, contributing to advancements in photogrammetry and 3D scanning technologies since 2020. His research focuses on photogrammetry , 3D metrology , and virtual simulation , with specific interests in developing laser scanner simulators for digital twin environments, educational applications, and robotic missions. His work integrates computer vision, geomatics, and educational technology to create innovative simulation tools that bridge theoretical research with practical industrial and academic implementations. Between 2020 and 2024, Popovas co-authored six publications demonstrating a progression from foundational simulator development to applications in dynamic scanning and geodetic network planning for robots. This trajectory highlights increasing complexity in virtual scanning technologies, with recent work emphasizing real-time processing, sensor simulation, and integration into digital twin frameworks for industrial and educational contexts. He is a key contributor to the DAAD-funded project "Virtual laser scanner simulator for digitizing the teaching environment" (2019-2024), which develops software for generating simulated 3D point cloud data to enhance teaching methodologies in photogrammetry and geoinformatics through virtual laboratories and digital training resources. Within the IAPG, Popovas collaborates in the Laboratory for Optical 3D Metrology under Prof. Dr. Thomas Luhmann, working alongside a multidisciplinary team including Dr. Maria Chizhova and Dr. Denys Gorkovchuk. The laboratory actively organizes the Oldenburg 3D Days conference series, serving as a platform for disseminating research in optical metrology and fostering industry-academia partnerships.

• Energy Systems Modeling
• Geothermal Energy
• Optimization Algorithms
• +۳ مورد دیگر

Smajil Halilovic is a Researcher at the Chair of Renewable and Sustainable Energy Systems at the Technical University of Munich . His work focuses on energy systems modeling and optimization, particularly for geothermal and renewable energy integration. Current projects include Geo.KW , a coupled hydrothermal and infrastructure model for urban-scale geothermal use Research emphasizes optimization techniques for groundwater heat pump systems Key contributions in PDE-constrained optimization and thermal resource assessment Halilovic has published extensively in Renewable Energy and Energy Conversion and Management , with recent work on spatial optimization of geothermal systems and urban heat planning. His collaborations with Prof. Thomas Hamacher and Kai Zosseder highlight his role in advancing sustainable energy infrastructure. He teaches Mathematical Modeling of Complex Systems in the Energy Field and contributes to interdisciplinary projects like the Interdisciplinary project internship: Concept development of a renewable energy system in a developing country . His publications demonstrate expertise in geothermal integration, optimization algorithms, and urban energy modeling.

Bashir Kazimiمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• Materials Science
• +۳ مورد دیگر

Dr.-Ing. Bashir Kazimi is a group leader at the Materials Data Science and Informatics (IAS-9) department within the Institute for Advanced Simulation at Forschungszentrum Jülich. His work focuses on advancing deep learning and computer vision techniques for electron microscopy data analysis, enabling efficient material characterization. Expertise: Deep Learning, Computer Vision, Image Analysis Collaboration: Works closely with the Ernst-Ruska-Center (ER-C) for electron microscopy expertise Research Interests: Bashir develops and applies deep learning methods for tasks such as denoising, super-resolution, semantic segmentation, and tracking in electron microscopy. His applications span nanomaterial characterization, crystallographic defect identification, and orientation mapping. Scientific Trends: His recent publications highlight advancements in self-supervised learning, semantic segmentation of TEM images, and applications of deep learning to both materials science and archaeological monument detection in geospatial data. Scientific Achievement: Admitted to the Young Excellent Scientist Program (YESP) in 2024, supporting leadership development and scientific visibility Advising: Supervises Shrindhi Bhat , a PhD student in his group. He is involved in projects like FAST-EMI (Deep-learning assisted fast in situ 4D electron microscope imaging), with a focus on enhancing materials analysis through AI.