Find Academic Supervisors

Bashir KazimiView profile

Researcher

• Deep Learning
• Computer Vision
• Materials Science
• +3 more

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.

Ana LucicView profile

Assistant Professor

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +3 more

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.

• Telecommunications
• 5G/6G Networks
• Network Slicing
• +7 more

Christian Wietfeld is a Professor at TU Dortmund, Germany, specializing in telecommunications, 5G/6G networks, and robotics. His research focuses on network slicing, machine learning for communications, vehicular networks, and intelligent reflecting surfaces. Affiliations: TU Dortmund Key Collaborations: Stefan Böcker, Benjamin Sliwa, Manuel Patchou His work spans 6G multi-X communications, private industrial networks, and disaster response robotics. Recent projects include mmWave reflector systems, predictive uplink slicing, and AI-driven network planning. 2024-2025 publications highlight advancements in 6G IRS, energy-efficient 5G, and vehicular connectivity. Sub-fields include beam management, digital twins, and non-terrestrial networks. He contributes to experimental frameworks like Open RAN and ns-3 simulations, emphasizing scalable solutions for industrial and emergency applications.

Markus SchartauView profile

Researcher

• Marine Biogeochemical Modeling
• Plankton Dynamics
• Ocean Ecosystem Modeling
• +7 more

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.

• Hydrogeology
• Environmental Informatics
• Groundwater Systems
• +5 more

Dr. Thomas Kalbacher serves as Deputy Head of Department and Workgroup Leader of Hydroinformatics at the Department of Environmental Informatics, Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. With extensive experience in environmental modeling and software development, he leads critical research initiatives focused on groundwater systems and environmental informatics. His research spans Earth Science - Hydrogeology and Computer Science - Simulator & Workflows, with particular emphasis on the development of OpenGeoSys (OGS) software and related analysis tools. Kalbacher's work addresses sustainable use and protection of natural groundwater systems, examining their quantitative and qualitative impact on surface waters. He investigates how digital twins and near real-world models can help tackle water management challenges in the face of climate change, with a focus on creating robust future projections to preserve landscape multifunctionality. Analysis of his recent publications reveals strong trends in hydrogeological modeling, spectral analysis of groundwater level fluctuations, and development of digital tools for environmental monitoring. His work increasingly focuses on large-scale modeling of groundwater systems, particularly in the Danube catchment area, while also advancing data infrastructure through projects like Digital Earth. A significant portion of his recent research examines methodological approaches to hydrologic modeling, particularly comparative assessments of Richards equation versus infiltration capacity approaches. As leader of the Hydroinformatics workgroup, Kalbacher plays a pivotal role in the OpenGeoSys open-source project, which has become a significant platform for computational hydrology and environmental modeling. His team develops software solutions for regional groundwater quantity modeling (LandTrans) in cooperation with the Research Software Engineering and Visualization group. The Hydroinformatics group under his leadership contributes to advancing environmental modeling capabilities through robust software development practices and integration of high-resolution observation data from modern monitoring stations and satellite observations.

Mitra BaratchiView profile

Associate Professor

• Spatio-Temporal Data Analysis
• Machine Learning
• Mobility Data Modeling
• +5 more

Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.

• Computational Mathematics
• Partial Differential Equations
• Finite Element Methods
• +4 more

Jichun Li is a Professor in the Department of Mathematical Sciences at the University of Nevada Las Vegas, with a prolific research career spanning computational mathematics, image processing, and computer vision. His work bridges theoretical mathematics with practical applications in medical imaging, environmental science, and biometrics. Li's research interests center on computational mathematics with particular focus on partial differential equations and finite element methods, alongside significant contributions to image processing and computer vision. His work demonstrates a unique integration of mathematical theory with practical applications, particularly in medical imaging where his techniques enable improved early cancer diagnosis through advanced lesion segmentation. In environmental science, his research on land surface albedo dynamics provides critical insights into climate change impacts in sensitive regions like the Tibetan Plateau. His recent work on face recognition with synthetic data addresses contemporary challenges in biometric security systems. Analysis of Li's publication trends reveals a strategic evolution from foundational mathematical research toward interdisciplinary applications. While maintaining strong theoretical contributions in computational mathematics, particularly in PDEs and finite element methods, he has increasingly focused on medical imaging applications since 2020, developing novel techniques for cancer diagnosis and ultrasmall object detection in CT scans. His 2022-2025 publications show growing emphasis on synthetic data applications, particularly in face recognition challenges, demonstrating adaptability to emerging AI trends. Professor Li maintains an extensive collaborative network, with frequent co-authorship patterns suggesting mentorship relationships with researchers like Bo Yan, Weimin Tan, Guannan Chen, and Encai Zhang across multiple publications. His work appears in leading journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Multimedia, and Computational Mathematics and Applications, reflecting both the theoretical depth and practical relevance of his research.

Gudrun KlinkerView profile

Professor

• Industrial Augmented Reality
• Sensing
• Ubiquitous Tracking (Sensor Fusion)
• +4 more

Overview Prof. Gudrun Klinker is a Professor at Technische Universität München (TUM), leading the Chair of Computer Science Applications in Medicine & Augmented Reality. She holds a PhD from Carnegie-Mellon University and has extensive experience in academia and industry, including roles at Digital Equipment Corporation and Fraunhofer Institute. Affiliations Professor (C3/W2) since 2000 Head of the FAR (Augmented Reality) research group Member of the CAMP (Computer Aided Medical Procedures) interdisciplinary team Research Dr. Klinker specializes in augmented reality (AR) with focuses on industrial applications, sensor fusion, 3D interaction, and healthcare integration. Her work bridges computer science with medical and industrial challenges, emphasizing user-centric design and real-world deployment. Key projects include: AR-based maintenance systems for complex machinery Healthcare applications like serious games for nutrition education VR/AR training environments for medical and aerospace domains Awards and Recognition Robert Sauer Prize (2010) for contributions to Bavarian science ISMAR Lasting Impact Award (2014) for influential AR research Teaching and Mentorship Prof. Klinker teaches courses on 3D user interfaces, AR fundamentals, and game design. She advises students in thesis projects, focusing on AR applications, HCI, and medical computing. Labs and Collaborations Her research is supported by partnerships with industry and institutions, leveraging TUM's interdisciplinary environment to advance AR/VR systems for real-world impact.

André BorrmannView profile

Professor

• Artificial Intelligence
• Building Information Modeling & Processing
• Digital Twinning
• +7 more

Prof. Dr.-Ing. André Borrmann is an academic leader at the Technical University of Munich (TUM) , where he has headed the Chair of Computing in Civil and Building Engineering since 2011 (formerly Computational Modeling and Simulation). He serves as Director of the TUM Georg Nemetschek Institute - AI for the Built World since 2025 and Spokesperson for the Leonhard Obermeyer Center since 2013. Research Interests Artificial Intelligence in Civil Engineering Digital Twinning Building Information Modeling (BIM) Pedestrian Dynamics Knowledge Representation Construction Simulation His work focuses on AI application across the built environment lifecycle - from generative design to maintenance prediction - with significant contributions to BIM standardization and buildingSMART International IFC extensions. He co-authored the German Ministry of Transport BIM Roadmap and led the BIM4INFRA2020 project. Awards include the 2024 Konrad Zuse Medal and multiple best paper awards at international conferences.

Thomas StetzView profile

Professor

• Smart Grids
• Energy Storage
• Photovoltaic Systems
• +3 more

Prof. Dr.-Ing. Thomas Stetz is a Professor for Electrical Power Engineering at the Technical University of Mittelhessen (Gießen, Germany), specializing in Smart Grids and Energy Storage . He is a Scientific Director of the Institute for Transformation Tasks in Energy Economics and Energy Technology (ITEE) and Project Group Leader at TransMIT GmbH, Gießen. PhD in Electrical Power Engineering (University of Kassel, 2013) Former Research Group Leader at Fraunhofer IWES (Kassel, Germany) His research focuses on technical and economic aspects of voltage control , grid integration of photovoltaics , and smart grid technologies . He has led international projects with partners like the University of Costa Rica and Ingolstadt University of Applied Sciences, funded by BMBF , BMWi , and DFG . Key article trends include reactive power optimization , automated grid planning , and storage system integration for renewable-heavy grids. His scientific awards highlight his contributions to global PV grid integration research. Best Presentation Award (CIER 2017, Havana) Science Award (German Chamber of Industry and Commerce Kassel, 2014) Prof. Stetz supervises PhD and undergraduate students and contributes to industry-standard guidelines through collaborations with Stadtwerke Gießen AG and Bayernwerk. His work bridges technical innovation and policy development for the energy transition.

Nina KüpperView profile

Researcher

• Satellite Remote Sensing
• Mining Engineering
• Machine Learning Applications in Geosciences
• +3 more

Nina Küpper is a Senior Engineer at the Institute of Mineral Engineering, RWTH Aachen University, specializing in satellite remote sensing for mining residue characterization and digital education in mining. She leads the SatMine research project, integrating satellite imaging with environmental monitoring. Research Focus: Machine learning applications in mine waste identification, VR-based technical training, and copper sediment distribution analysis. Projects: SatMine, VR-Mine, MyScore mobility initiative. Technologies: Remote sensing, geospatial AI, holographic tools, and virtual mineralogical showrooms. Her work bridges environmental sustainability with mining education, emphasizing digital innovation and international collaboration.

• Medical Informatics
• Public Health Surveillance
• Patient Matching
• +5 more

Shaun J. Grannis is a leading Research Professor at the Regenstrief Institute and Indiana University School of Medicine , specializing in Medical Informatics . His work focuses on patient matching, health data interoperability, and public health surveillance. Research Interests : Patient identification strategies, privacy-preserving record linkage, synthetic data generation, and leveraging health information exchanges (HIE) for population health analytics. Articles : Over 86 publications (2002-2025) addressing real-time disease detection, data quality metrics, and EHR interoperability. Key trends include syndromic surveillance , social determinants of health , and machine learning in clinical data . Awards : Recognized for groundbreaking work in public health informatics and synthetic data applications. Collaborations : Frequent partnerships with institutions like AMIA , JAMIA , and BMC Medical Informatics on projects enhancing healthcare data systems. Grants : Involved in NIH-funded initiatives for national patient-centered research networks.

• Physical Oceanography
• Ocean Dynamics
• Climate Modeling
• +5 more

Franziska Schwarzkopf is a Researcher at the GEOMAR Helmholtz Centre for Ocean Research in Kiel, Germany, with a focus on Ocean Dynamics . She has been actively involved in high-resolution Ocean Modeling and Climate Dynamics since 2019, contributing to projects like WHIRLS (ocean whirls' impact on climate) and SPACES initiatives. Her work spans multiple disciplines, including Physical Oceanography , Climate Modeling , and Marine Ecosystems . Research Interests High-resolution modeling of ocean circulation Oxygen Minimum Zones (OMZs) in tropical oceans Atlantic Meridional Overturning Circulation (AMOC) Agulhas Current system dynamics Marine heatwaves and climate feedback Interaction between ocean currents and marine ecosystems Publication Trends reveal a focus on Modeling ocean-climate interactions Decadal variability of currents Deep-sea and benthic circulation Climate change implications in ocean dynamics Eddy-resolving simulations Marine ecosystem connectivity Projects include WHIRLS , SPACES , Nordatlantik , and R'EDDY , with collaborations across institutions in Germany, Africa, and beyond. Her Education features a Dr. rer. nat. (2016) and Diplom (2008) in Physical Oceanography from Kiel-based institutions.

Julian FäthView profile

Research Fellow

• Remote Sensing
• Forest Hydrology
• Soil Moisture Monitoring
• +4 more

Dr. Julian Fäth is a Research Fellow at the Chair of Remote Sensing within the Institute of Geography and Geology at the University of Würzburg. Since May 2024, he has been leading the main working package "Forestry" in the EO4CAM project. His academic career spans forest science, remote sensing, and environmental geophysics with a focus on forest hydrology and soil moisture dynamics across temperate forest ecosystems in Germany. His educational background includes: PhD in Forest Nutrition and Water Balance from the Technical University of Munich (2017-2021) Studies in Forestry Science at the Technical University of Munich (2011-2017) Dr. Fäth's research focuses on applying geophysical methods, particularly electrical resistivity tomography (ERT), to monitor soil moisture fluctuations in forest ecosystems. His work bridges remote sensing technologies with traditional forestry practices to understand how forests respond to climate change, particularly drought stress. He investigates the complex interactions between soil properties, water availability, and tree health in temperate forests across Germany, with particular attention to beech decline under recurrent drought conditions. His publication record shows a strong trend toward integrating multiple monitoring approaches for soil moisture and forest hydrology. Recent work emphasizes 2D and 3D ERT techniques combined with traditional hydrological measurements to create comprehensive understanding of water dynamics in forest soils. His research increasingly addresses climate change impacts on forest ecosystems, particularly beech decline under recurrent drought stress, examining how individual neighborhood structure and soil properties influence tree survival. Dr. Fäth has been involved in several significant research projects including the EO4CAM project where he leads the forestry component. His collaborative work spans multiple institutions including the University of Würzburg and the Technical University of Munich. His research has practical applications for sustainable forest management under changing climatic conditions, providing critical insights for forest managers facing increasing drought frequency and intensity.

• Urban Climate Research
• Thermal Comfort
• Urban Air Quality
• +6 more

Prof. Dr. Uwe Schlink is a leading Professor at the Institute of Meteorology, University of Leipzig, and Senior Researcher at the Department of Urban & Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ. His work focuses on urban climate research , thermal comfort , urban air quality , and statistical modelling with Bayesian inference. He leads the working group on urban climate and personal exposure, bridging environmental science with societal resilience. Affiliation: University of Leipzig (since 2009) and UFZ (since 2013) Research Themes: Urban heat islands, personal exposure to environmental stressors, statistical climate models, and health impacts of air pollution His research spans environmental health , urban climatology , and resilient city planning , with significant contributions to understanding thermodynamic interactions between urban structures and climate. He has pioneered methods for high-resolution land surface temperature analysis and green infrastructure performance in mitigating heat stress. Recent publications (2023-2025) highlight his work on PM2.5-bound PAH exposure , anthropogenic heat impacts in Beijing, and Asian plateau climate dynamics . Collaborative projects address urban heat stress , green roofs , and health-focused urban planning .