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Senthold AssengView profile

Professor

• Digital Agriculture
• Climate Change Impacts
• Sustainable Crop Production
• +5 more

Prof. Senthold Asseng is a Professor of Digital Agriculture at the Technical University of Munich (TUM), leading the Hans Eisenmann Forum for Agricultural Sciences since 2021. His research focuses on climate-plant-soil systems modeling, addressing global food security, sustainable agriculture, and digital technologies like vertical farming and autonomous robotics. He holds a PhD from Humboldt University Berlin and habilitation from TUM, with prior roles at CSIRO Australia and the University of Florida (as Full Professor and Director of the Florida Climate Institute). His honors include AAAS Fellow, Web of Science Highly Cited Researcher, and multiple teaching/mentorship awards. Education: BSc/MSc in Agronomy & Horticulture, Humboldt University Berlin (1989-1990) PhD in Agronomy, Humboldt University Berlin (1994) Habilitation in Agronomy, TUM (2004) Research Interests: Climate change impacts on crops, digital agriculture technologies, systems analysis for autonomous farming, and vertical farming sustainability. His work integrates modeling, robotics, and environmental control to enhance agricultural resilience and productivity. Awards: AAAS Fellow (2019) Highly Cited Researcher (2019) UF Research Foundation Professor (2016-2019) Grants & Leadership: Led interdisciplinary projects like AgMIP-Wheat, SECC, and FAO advisory roles. Active in editorial roles for journals like Global Change Biology and Environmental Research Letters . Labs/Teams: Chair of Digital Agriculture at TUM, collaborating on projects like Smartfield and Proteins4Singapore , focusing on automation, controlled environment agriculture, and climate-smart solutions.

• Lithium-Ion Battery Production
• Energy Storage Systems
• Production Engineering
• +3 more

Prof. Dr.-Ing. Rüdiger Daub serves as Professor and Chair of Production Engineering and Energy Storage Systems at the Technical University of Munich (TUM), operating within the Department of Mechanical Engineering. His leadership encompasses research direction, academic supervision, and strategic development of battery production technologies at TUM's Garching campus (Boltzmannstr. 15), with active industry collaborations driving innovation in sustainable manufacturing. Daub's research program pioneers advanced production methodologies for lithium-ion and solid-state batteries, focusing on electrode manufacturing, electrolyte filling, and cell assembly processes. His work investigates critical parameter interdependencies affecting battery safety and performance, developing inline monitoring systems and digital twin technologies for real-time process optimization. Key contributions include moisture control in electrode production, electrochemo-mechanical characterization of solid-state systems, and robotics solutions for deformable object assembly, all integrated with machine learning for quality assurance in industrial settings. Analysis of his 2023-2025 publications reveals a dominant research trajectory toward solving production bottlenecks in next-generation energy storage. The work demonstrates increasing integration of computational modeling with empirical validation, particularly in solid-state battery manufacturing and high-voltage electrolyte systems. A notable trend is the cross-pollination of robotics, computer vision, and uncertainty quantification techniques to address complex assembly challenges and distribution shifts in quality monitoring, reflecting industry's urgent need for adaptable, data-driven production systems. Leading TUM's specialized laboratories for battery cell production, Daub's team maintains comprehensive facilities for electrode calendering, electrolyte filling, and cell assembly with integrated tracking and tracing capabilities. The research infrastructure supports collaborative projects with automotive OEMs and battery manufacturers to develop scalable production processes, emphasizing environmental sustainability through water-based electrode production and footprint optimization. Current initiatives focus on digital factory modeling and prelithiation technologies for next-generation battery systems.

Stefan LadwigView profile

Researcher

• Automated Driving
• Human-Machine Interaction (HMI)
• Traffic Psychology
• +5 more

Dr. phil. Stefan Ladwig is a researcher at the Institute for Automotive Engineering (Institut für Kraftfahrzeuge) at RWTH Aachen University, specializing in traffic psychology, user acceptance, and human-machine interaction for automated driving systems. His work addresses psychological and ergonomic aspects of emerging mobility technologies, including seating ergonomics, trust-building mechanisms, and communication strategies for autonomous delivery robots. Research Focus: Automated driving, interior design psychology, driver/passenger behavior, and safety requirements. Projects: Lead roles in UrbANT (autonomous delivery robot) and SteeringBow (dynamic driving experience). Collaborations: Affiliated with Aldenhoven Testing Center, Future Mobility Partnership e.V., and innocam.NRW. Ladwig’s recent publications examine rotated seating positions, visual dominance in speed adaptation, and trust-relevant driving scenarios, reflecting his interdisciplinary approach to optimizing human-vehicle interactions. His work spans ergonomics, energy management, and behavioral interventions to enhance safety and usability in next-generation mobility solutions.

• Computer Science
• Software Engineering
• Artificial Intelligence
• +5 more

Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.

Vera MeyerView profile

Professor

• Biotechnology
• Microbial Genetics
• Fungal Biology
• +5 more

Prof. Vera Meyer is a Professor in the Department of Applied and Molecular Microbiology at Technische Universität Berlin’s Faculty III - Process Sciences. Her research focuses on optimizing fungal production systems for bioactive compounds, antifungal agents, and secondary metabolites. She leads projects such as the MY-CO SPACE exhibition, exploring fungal-based materials in architecture and sustainability. Notable contributions include work on Aspergillus niger morphology, pyomelanin synthesis for radiation shielding, and co-authoring over 150 publications. Meyer holds patents on fungal biosynthetic processes and collaborates internationally on interdisciplinary bioeconomy initiatives. Contact: vera.meyer@tu-berlin.de . Education details are not explicitly listed, but her career includes leadership roles in TU Berlin’s biotechnology programs and UniCat collaborations. Research emphasizes systems biology approaches to microbial engineering, combining microbiology, mathematics, and engineering for predictive models of cellular processes. Recent projects include the MY-CO SPACE exhibition at Bundeskunsthalle Bonn (2025-2026), showcasing fungal-based construction materials.

Christian KrupitzerView profile

Professor

• Food Informatics
• Self-adaptive Software Systems
• Machine Learning
• +4 more

Jun.-Prof. Dr. Christian Krupitzer is a Tenure Track Professor in Food Informatics at the University of Hohenheim's Institute of Food Science and Biotechnology, part of the Faculty of Natural Sciences. He leads the Department of Food Informatics and is a member of the Computational Science Hub (CSH). His research focuses on self-adaptive software systems, machine learning (especially edge computing), IoT technologies, and software engineering applied to food processing and agricultural systems. Education: PhD in Business Information Systems (Dr. rer. pol.), University of Mannheim (2018) M.Sc. and B.Sc. in Business Information Systems, University of Mannheim (2010–2012) High School Diploma (Abitur) from Wilhelmi-Gymnasium Sinsheim (2007) Research Interests: Krupitzer’s work integrates computational methods with food science, emphasizing adaptive systems for food quality monitoring, IoT in agriculture, and machine learning for predictive analytics. He explores edge computing’s role in real-time decision-making and secure group communication schemes for IoT networks. Publications: His recent work spans predictive maintenance in Industry 4.0, digital twins in food systems, and blockchain applications in supply chain authentication. The articles highlight trends in interdisciplinary approaches combining AI, IoT, and domain-specific challenges in food production and logistics. Awards: No scientific awards explicitly listed in the provided materials. Grants & Advising: While specific grants are unmentioned, his roles as department head and tenure-track professor suggest involvement in research funding. No formal advisee list provided, though his team includes postgraduate researchers like Dana Jox, Daniel Einsiedel, and others. Labs & Teams: Leads the Food Informatics department and collaborates with the Computational Science Hub. His team focuses on developing innovative solutions for food systems through computational methods.

• Spatial Statistics
• Spatiotemporal Modeling
• Environmetrics
• +3 more

Philipp Otto is a Professor of Statistics and Data Science at the University of Glasgow. Previously, he was a Reader in Statistics and Data Analytics (2023–2024) and held a Junior Professorship in Big Geospatial Data at Leibniz University Hannover (2018–2023). He earned his PhD in Statistics (summa cum laude) from European University Viadrina in 2016 and a B.Sc. in International Economics, with study visits to Saint Petersburg State University. His research focuses on spatial and spatiotemporal statistics, environmetrics, network modeling, and machine learning applications. Education: PhD in Statistics (2016), European University Viadrina, Frankfurt (Oder) B.Sc. in International Economics (with study visits to Saint Petersburg) Research Interests: Philipp’s work centers on spatial statistics, spatiotemporal volatility modeling, environmental data analysis, and network processes. He develops statistical methods for geo-referenced and network data, with applications in climatology, finance, and environmental risk assessment. His contributions include advancements in GARCH models, spatiotemporal clustering detection, and statistical process monitoring for AI systems. Grants & Projects: He has secured €1,038,847 in research grants, leading projects on historical map time series analysis, agricultural air quality impacts, and high-dimensional spatial dependence structures. Industry collaborations include survival analysis for building information models. Awards: 2017 Fellowship to attend the Lindau Nobel Laureate Meeting (Economic Sciences) 2017 Best Presentation Award (Data Science, Statistics, and Visualisation) Teaching: He teaches statistics and data science across disciplines, including economics, engineering, and mathematics, at both undergraduate and postgraduate levels. Professional Activities: Editorial Boards: Environmetrics (2021), AStA Advances in Statistical Analysis (2020) Member of German Statistical Society (Treasurer, 2013)

Ahmed ElbeltagiView profile

Assistant Professor

• Hydrology
• Agricultural Water Management
• Climate Change Adaptation
• +6 more

Ahmed Elbeltagi is an Assistant Professor in the Agricultural Engineering Department at Mansoura University's Faculty of Agriculture. His work focuses on hydrology, agricultural water management, and climate change adaptation. Specializes in data-driven modeling for water resource optimization Integrates machine learning with traditional hydrological models Active in climate change impact assessments on agricultural systems Recent research trends include: Developing open-source tools like Aqua-MC for irrigation simulation Applying hybrid deep learning models for evaporation prediction Advancing water quality assessment through multivariate analysis Exploring economic applications of wetlands in arid regions He collaborates with institutions across Egypt, India, China, and Saudi Arabia, with a focus on sustainable water management solutions.

• Ecological Epidemiology
• Wildlife Disease Dynamics
• Veterinary Epidemiology
• +4 more

Dr. Martin Lange is part of the Project Group Ecological Epidemiology within the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research (UFZ). His research focuses on computational epidemiology, particularly in wildlife and livestock systems. Key areas include disease transmission dynamics, surveillance strategies, and the development of predictive models for infectious diseases like African Swine Fever (ASF) and Bovine Viral Diarrhea Virus (BVDV). Affiliations: UFZ, Department of Ecological Modelling; EcoEpi research group. Education: PhD in Veterinary Epidemiology (2013, TU Bergakademie Freiberg). Research interests span ecological epidemiology, wildlife disease control, and the application of agent-based and individual-based models to understand pathogen spread. Notable contributions include modeling ASF in wild boar populations and BVDV eradication strategies in Ireland. Awards: Received the 2018 UFZ-Wissenstransferpreis (shared with H.H. Thulke) and the Konrad-Bögel-Nachwuchsförderpreis for his doctoral work on eco-epidemiology of wild boar diseases. Also recognized at the DACh Epidemiologietagung 2011. Publications: Over 50 peer-reviewed articles since 2006, emphasizing interdisciplinary approaches to disease modeling and environmental systems. Recent work includes digital twin frameworks for biodiversity monitoring and Python-based model coupling tools like FINAM. Labs/Teams: Active in the EcoEpi group and contributes to projects like BioDT (Biodiversity Digital Twin) and CauSES (Causation in Social-Ecological Systems).

Stefano NoventaView profile

Research Fellow

• Mathematical Psychology
• Psychometrics
• Psychophysics
• +7 more

Stefano Noventa is a Research Fellow at the Methods Center, Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has held multiple postdoctoral positions at the University of Tübingen and previously at the University of Verona and the University of Padova. Education: Ph.D. in Cognitive Psychology, University of Padova (2011) M.Sc. in Physics, University of Padova (2006) Studies in Physics, University of Padova (1999–2006) International Visiting Graduate Student, University of Toronto (2009, 2010) Dr. Noventa's research lies at the intersection of mathematical psychology, psychometrics, and psychophysics, with a focus on developing and unifying quantitative models of human cognition and assessment. His work integrates Item Response Theory (IRT) and Knowledge Space Theory (KST) to create more robust frameworks for educational and psychological measurement. He investigates latent variable models, probabilistic knowledge structures, and the identifiability of complex psychometric models, often applying these to domains such as education, organizational psychology, and entrepreneurship. His recent publications (2020–2024) demonstrate a strong trend toward theoretical integration, particularly in bridging cognitive diagnosis models with traditional psychometric frameworks. The articles emphasize mathematical rigor, model generalization, and empirical validation, with applications in both cognitive science and applied psychology. Topics include the unification of assessment models, parameter estimation under local dependence, and the modeling of intuitive physical reasoning. Scientific Awards: No awards or honors listed in the provided text. Dr. Noventa has not been explicitly mentioned as an advisor to students, but he has served as a corresponding author and collaborator on multiple research projects, indicating a leadership role in research teams. He has been involved in a DFG-funded project (GLI NON-NORM) since 2019, suggesting active grant participation. His work is highly collaborative, involving researchers from Germany, Italy, Austria, and Canada. Labs and Research Groups: Methods Center, University of Tübingen Hector Institute of Education Science and Psychology, University of Tübingen Center of Assessment, University of Verona Department of General Psychology, University of Padova

Jalal EtesamiView profile

Assistant Professor

• Machine Learning
• Statistical Decision Making
• Causal Inference
• +4 more

Prof. Jalal Etesami is an Assistant Professor in the Department of Computer Science at Technical University of Munich (TUM), leading the Decision Sciences & Systems group. He holds a Ph.D. in Industrial and Systems Engineering from the University of Illinois at Urbana-Champaign and was a Postdoctoral Fellow at EPFL in Switzerland. His research focuses on machine learning, causal inference, multi-agent systems, and game theory, with applications to systemic risk modeling and market design. He teaches advanced courses such as Causal Inference in Time Series , Algorithmic Game Theory , and Optimization, Learning, and Market Design . Notable contributions include work on causal structure learning, stochastic optimization, and non-Gaussian causal models. Recent research explores causal effect identification under confounding, neural networks for market analysis, and optimal experiment design. Prof. Etesami’s work appears in top venues like NeurIPS, AAAI, and IEEE journals. He actively contributes to the academic community, organizing seminars and workshops on topics ranging from causal reasoning to computational social choice.

• Soil Biogeochemistry
• Carbon Cycling
• Mineral-Soil Interactions
• +3 more

Dr. Marion Schrumpf is a Group Leader in the Soil Biogeochemistry research group at the Max Planck Institute for Biogeochemistry, affiliated with the Department of Biogeochemical Processes. Her work focuses on soil carbon dynamics, mineral-organic matter interactions, and climate change impacts on soil systems. Research areas: Soil biogeochemistry, carbon cycling, mineral-soil interactions, nutrient stoichiometry, microbial ecology, and climate modeling. Email: mschrumpf@... Phone: +49 3641 57-6182 Office: B2.015 Her recent publications address themes like mineral control over soil carbon stabilization, drought effects on soil processes, microbial stoichiometric adaptation, and the Jena Soil Model's role in simulating carbon-nutrient interactions. She leads efforts to disentangle the complex relationships between land use, mineralogy, and soil organic matter turnover across diverse ecosystems.

Marcel GebhardtView profile

Professor

• Digital Marketing & Sales
• Marketing & Sales Analytics
• Marketing & Sales Biases
• +3 more

Marcel Gebhardt (born 1987) is a Professor in the Department of General Business Administration at the Faculty of Industrial Engineering, Mannheim University of Applied Sciences since April 2022. His expertise spans B2B Marketing, Technical Sales, and Data-driven Marketing & Sales, with a focus on Digital Sales and Business Performance Methods. University: Mannheim University of Applied Sciences School: Faculty of Industrial Engineering Rank: Professor Research Interests: Digital Marketing & Sales Marketing & Sales Analytics Data-driven Marketing & Sales Cost Management in Industrial Engineering Publications Trends: His research focuses on data-driven approaches in B2B marketing, lead scoring models, cost analysis for engineering changes, and optimization of production systems. His work bridges academic theory with industrial applications, particularly in machine building and laser technologies. Scientific Awards: Dissertationspreis der Péter Horváth-Stiftung (2018) Other Contributions: Gebhardt has served as a researcher and consultant in public and industry-funded projects, including roles at the International Performance Research Institute (IPRI).

Jochen GarckeView profile

Professor

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +5 more

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.

Constantinos AntoniouView profile

Professor

• Transportation Systems Engineering
• Intelligent Transport Systems (ITS)
• Behavioral Economics in Transport
• +5 more

Prof. Constantinos Antoniou is a Full Professor in the Chair of Transportation Systems Engineering at the Technical University of Munich (TUM), part of the TUM School of Engineering and Design. He holds a Diploma in Civil Engineering from the National Technical University of Athens (1995), an MS in Transportation (1997), and a PhD in Transportation Systems (2004) from MIT. His research focuses on transportation systems engineering, including modeling/simulation, intelligent transport systems (ITS), behavioral economics in transport, road safety, and sustainable transport systems. He has authored over 500 scientific publications, including over 210 peer-reviewed journal papers, and serves as Co-Editor-in-Chief of Transportation Research Part A . Awards include the Francquis Chair (2023), IEEE ITS Outstanding Application Award (2011), and Marie Curie Fellowship (2006–2008). He has secured over €4M in third-party funding, leading projects such as SCUDO, CulturalRoad, and PHOEBE. Currently supervising 17 PhD students, he has mentored seven graduates who now hold academic positions at institutions like UCL and UPC. Research interests span autonomous vehicles, traffic resilience, and policy-driven transport solutions. Key projects include analyzing urban air mobility, MaaS potential, and pandemic impact on transport networks. His work contributes to UN Sustainable Development Goals, emphasizing sustainable cities and communities.