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Jens LausenView profile

Researcher

• Market Microstructure
• Regulation in Financial Markets
• Financial Intermediation
• +1 more

Dr. Jens Lausen is a Researcher at the Department of Business Information Systems within the Faculty of Economics and Business at Goethe University Frankfurt. Affiliated with the Chair of e-Finance under Prof. Dr. Peter Gomber since February 2017, he completed his doctorate in October 2021 on digital financial markets and investor protection. His research bridges finance, information systems, and regulatory technology with a focus on empirical market analysis. His academic credentials include: Master's degree in Economics and Management with Finance specialization from Johannes Gutenberg University Mainz (January 2017), graduating as top student in his cohort Doctorate (Dr. rer. pol.) from Goethe University Frankfurt (October 2021) on "Market Organization and Investor Protection in Digital Financial Markets" Lausen's research centers on Market Microstructure, Financial Regulation, and Decision Support Systems, employing computational methods like agent-based modeling and machine learning. His work analyzes market fragmentation, crowdfunding dynamics, and misconduct detection in financial intermediation, frequently published in leading finance and information systems journals. Analysis of his 15 most recent publications (2018-2024) reveals three dominant research trajectories: (1) Regulatory impact assessment using textual analysis and machine learning, (2) Market microstructure evolution in fragmented securities markets, and (3) Behavioral dynamics in crowdfunding platforms. His work consistently applies computational techniques to regulatory challenges in digital finance. As core member of Prof. Gomber's e-Finance research group, Lausen contributes to Frankfurt's financial technology research ecosystem. The team maintains strong industry connections with European financial institutions and regulatory bodies, focusing on practical applications of market microstructure theory and regulatory technology solutions.

Axel KrönerView profile

Professor

• Optimal Control
• Optimization with partial differential equations
• Numerical analysis
• +3 more

Prof. Dr. Axel Kröner is a Professor of Optimization at the Institute of Mathematics, Martin-Luther-Universität Halle-Wittenberg, within the Faculty of Natural Sciences II. His research focuses on optimal control and numerical methods for partial differential equations (PDEs), stochastic control, and feedback mechanisms in complex systems. Academic Background: Ph.D. in Mathematics, Technische Universität München (2011) Diploma in Mathematics, Ruprecht-Karls-Universität Heidelberg (2007) Axel Kröner's research spans PDE-constrained optimization, numerical analysis, and control theory. Key areas include feedback control for hyperbolic and parabolic equations, stochastic optimal control for infinite horizon problems, and adaptive finite element methods for PDE discretization. His work bridges theoretical and computational aspects, with applications in elasticity, fluid dynamics, and quantum mechanics. Recent publications highlight advancements in Hamilton-Jacobi-Bellman equations for feedback control, bilevel optimization for parameter learning, and Runge-Kutta-based neural networks . Methodologies often integrate dynamic programming , Galerkin approximations , and error estimation for efficient solutions. Advising and Projects: He has supervised numerous Master's and Bachelor's theses on topics like wave equation control, inverse problems, and neural network optimization. Funding includes support from the German Academic Exchange Service (DAAD) and the Gaspard Monge Program for Optimization and Operations Research (2015-2018). Professional Activities: Kröner has organized major conferences such as the International Workshop on PDE Constrained Optimization and contributed to teaching at institutions including Humboldt-Universität zu Berlin and University Paris-Saclay.

• Applied Machine Learning
• Econometrics
• Statistics
• +7 more

Arne Jonas Warnke is a data and analytics specialist currently serving as a team lead at DataCamp (a VC-funded U.S. scale-up) and as a guest lecturer at Heidelberg University. At DataCamp, he designs and manages learning content in machine learning, data engineering, and MLOps to build practical data capabilities for organizations. His academic role at Heidelberg involves teaching data science, machine learning, and applied statistics courses, including a 2024/25 seminar on Generative AI and LLMs for economics students. His expertise spans applied machine learning, econometrics, and statistics; data engineering production workflows; automation of data processes; organizational change through data analysis; and reproducible research using Python, R, and SQL. He emphasizes translating technical complexity into business/policy solutions and bridges technical teams with domain experts. With over a decade of programming experience, he focuses on practical applications where data solves societal challenges and drives educational innovation in digital topics.

• Electrochemical Process Engineering
• Fuel Cell Technology
• Synthetic Fuels
• +6 more

Prof. Dr. Ralf Peters is the acting director of the Institute of Energy and Climate Research - Electrochemical Process Engineering (IEK-14) at Forschungszentrum Jülich and Professor for Process Engineering for Synthetic Fuels at Ruhr University Bochum. Previously, he held professorships at Aachen University of Applied Sciences (2006-2023) and taught in the Master's program 'Energy Systems' at Aachen University of Applied Sciences since 2001. His career spans over three decades in energy research, with significant contributions to fuel cell technology and synthetic fuel production. Dr. Peters received his Dipl.-Ing. in Mechanical Engineering with specialization in Chemical Engineering from RWTH Aachen University in 1990 and completed his doctorate (Dr.-Ing.) at the University of Siegen in 1995 with research on 'Vapor-liquid phase equilibria in the ammonia-water-lithium bromide material system.' His academic journey includes positions as scientific staff member at the Institute for Fluid and Thermal Engineering at the University of Siegen (1990-1995) and at Forschungszentrum Jülich since 1996. Prof. Peters' research focuses on electrochemical process engineering , particularly in the areas of fuel cell technologies for mobile applications , synthetic fuel production , and power-to-fuel systems . His work bridges fundamental thermodynamic modeling with practical reactor development and system integration. The research group under his leadership has made significant advances in catalyst development for carbon-neutral fuel synthesis, particularly for iso-butanol production, and in the optimization of electrolysis systems for hydrogen production. His team has also contributed extensively to the understanding of CO 2 utilization pathways and the life cycle assessment of alternative fuel production processes. Analysis of Prof. Peters' recent publications reveals a strong focus on catalyst development for carbon-neutral fuel synthesis, particularly using bimetallic systems and dilute alloys. His work spans multiple technical domains including electrochemical systems (electrolyzers and fuel cells), thermodynamic process modeling, and life cycle assessment of alternative fuel pathways. The research demonstrates a clear progression from fundamental catalyst characterization to system-level integration of power-to-fuel technologies, with increasing emphasis on renewable energy integration and carbon-neutral fuel production. Prof. Peters has supervised more than 75 student theses throughout his career and has been actively involved in numerous collaborative research initiatives. He has served on advisory boards including Hydrogen Europe Research Association, Processnet, and Springer Vieweg Verlag. His leadership extends to organizing international forums such as the 'TRENDS: Transition to Renewable Energy Devices and Systems' discussion forum. At Forschungszentrum Jülich, Prof. Peters leads the Electrochemical Process Engineering department (IET-4), which focuses on low-temperature electrolysis, fuel synthesis, and related competencies in modeling and simulation. The department works closely with both academic and industrial partners to advance technologies for sustainable energy systems.

Günter BitschView profile

Adjunct Professor

• Industrial Engineering
• Artificial Intelligence
• Machine Learning
• +5 more

Prof. Dr. Günter Bitsch is a faculty member at ESB Business School of Reutlingen University, Germany. He serves as Deputy Dean of the Faculty NXT Sustainability and Technology and Pro-Dean of Finance. His academic expertise lies in digital transformation in industrial contexts, particularly focusing on hybrid decision support systems, cyber-physical production systems, and digital shopfloor management. PhD in Business Intelligence and Manufacturing Execution Systems from University of Siegen (2010) His research integrates artificial intelligence, machine learning, and Industry 4.0 technologies into production systems. He leads projects like accelerateKI and FTSsharing, and his work emphasizes explainable AI applications in manufacturing, human-robot collaboration, and adaptive control systems. Recent publications address: Explainable AI frameworks for root cause analysis Image segmentation for gear tooth alignment Economic evaluation of AI projects in CNC manufacturing Human-centric workforce dispatching models Dynamic adaptation in cyber-physical systems Prof. Bitsch's work bridges academic research with industrial practice through his co-founded company becos GmbH, where he applies his expertise in smart manufacturing solutions.

Martin BrehmView profile

Professor

• Theoretical Spectroscopy
• Molecular Dynamics Simulations
• Ionic Liquids
• +4 more

Martin Brehm is a Heisenberg Professor (W2) for Theoretical Spectroscopy at the University of Paderborn since May 2023. His research focuses on predicting vibrational spectra of condensed phase systems through advanced ab initio molecular dynamics simulations and software development, including contributions to the TRAVIS and CP2k packages. Academic Record : PhD (summa cum laude, University of Leipzig 2014), Habilitation (University of Halle–Wittenberg 2023) Research Highlights : Developing Voronoi integration methods for electromagnetic moments, creating force fields for bio-polymer solubility in ionic liquids, applying machine learning to atomistic simulations His work spans ionic liquids , vibrational spectroscopy , and computational chemistry applications in solubility prediction of complex systems. The TRAVIS software he co-develops has been cited over 500 times. Scientific accolades include the Hellmann Award 2021 (AGTC) and the BuildMoNa Award 'Outstanding Scientific Results' (2011). Current DFG-funded projects investigate atomistic simulation of complex liquids (€783,000) and spatial correlation effects on vibrational spectra (€194,000).

• Animal Breeding
• Livestock Husbandry
• Machine Learning
• +3 more

Christian Post is a research associate at the Institute of Animal Breeding and Animal Husbandry , part of the Christian-Albrechts-University of Kiel . His work focuses on applying machine learning and sensor systems to monitor and predict health disorders in dairy cows and swine, particularly through automated detection of diseases and precision livestock farming . He is also involved in environmental sustainability projects like pigNplay , which simulates nitrogen emission reduction strategies in swine farming. Doctorate in Agriculture from University of Bonn Master's and Bachelor's in Agriculture and Food Management from Osnabrück University of Applied Sciences His research interests span machine learning applications , sensor technology , and informatics in livestock systems . Recent work includes: Deep convolutional neural networks for calf disease detection Benchmark datasets for automated pig detection Comparative modeling of lameness and mastitis prediction

Marco MunderlohView profile

Research Fellow

• Video Coding
• Motion Detection
• Wave Field Synthesis
• +6 more

Marco Munderloh is a research fellow at the Leibniz University Hannover under the Institute for Information Processing . His career spans advanced video coding, predictive maintenance, and photonic design, with a focus on low-bitrate aerial video compression and motion detection. Dipl.-Ing. in Computer Engineering (2004), Technical University of Ilmenau PhD (2015), Leibniz University Hannover His research interests include: Wave Field Synthesis (WFS) with patented applications in cinema sound systems Predictive maintenance using deep learning and Bayesian neural networks Inverse design for photonic integrated circuits and 3D nanostructures Region-of-interest (ROI) video coding and motion compensation Optical verification of construction materials Marco's recent publications (2025–2018) cover predictive maintenance, video compression, and photonic design. Key trends involve applying machine learning to industrial reliability, optimizing video codecs for aerial surveillance, and computational inverse design for optical devices. Scientific achievements include: Patent for WFS technology in sound reproduction Collaborations span institutions like IEEE, CIRP, EUSPEN, and journals such as APSIPA Transactions and Bautechnik . Contact: Marco.Munderloh@tnt.uni-hannover.de , Marco.Munderloh@web.de .

Daniel GritznerView profile

Researcher

• Deep Learning
• Transfer Learning
• Semantic Segmentation
• +7 more

Daniel Gritzner is a researcher at the Institute for Information Processing (Leibniz Universität Hannover) , specializing in computer vision, remote sensing, and scenario-based software engineering. His work bridges academic research with real-world applications in renewable energy, geospatial analysis, and automated code generation. Studied Computer Science (B.Sc. 2010, Diploma 2014) at the University of Mannheim Focus areas: Deep Learning, Semantic Segmentation, Remote Sensing, Formal Specifications His research integrates computer vision with remote sensing , applying techniques like transfer learning and domain adaptation to aerial/satellite imagery. Key projects include SegForestNet for segmentation and WindGISKI for wind turbine site selection. Recent publications highlight advancements in semantic segmentation, hyperspectral band optimization, and scenario-based controller synthesis. Collaborative work with Jörn Ostermann and others demonstrates interdisciplinary approaches across IEEE, Springer, and arXiv platforms. Technical contributions include the open-source SegForestNet framework, implementing binary space partitioning trees for geospatial analysis. This toolchain combines Python/Rust with PyTorch, emphasizing reproducibility and practical deployment in industrial/energy domains.

Nick JanßenView profile

Researcher

• Machine Learning
• Computer Science
• Signal Processing
• +3 more

Nick Janßen is a researcher at the Institute for Information Processing, Leibniz University Hannover, working towards his PhD since July 2024 in Dr. Melanie Schaller’s group. His research focuses on state space models and audio monitoring applications in biomedical contexts. Education: Master’s Degree in Computer Engineering (2024), Leibniz University Hannover Bachelor’s studies in Computer Engineering (2018–2024) His research interests include machine learning , signal processing , and probabilistic modeling , particularly applied to medical engineering and data segmentation . Recent work explores audio monitoring for surgical applications and conditional normalizing flows for error detection in segmentation data. Notable publications include a preprint on state space models for bone cement disintegration monitoring (2025) and a dataset on erosion depth analysis (2024). His methodological work bridges computer science and biomedical engineering .

Orkun SensebatView profile

Researcher

• Quantum Computing
• Quantum Machine Learning
• Computational Physics
• +3 more

Orkun Sensebat is a Doctoral Researcher at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Germany, since 2024. He holds a Master of Science degree in Physics from RWTH Aachen University (2024) and focuses on quantum computing and machine learning applications. Education: M.Sc. in Physics, RWTH Aachen University (2024) Research Interests: Specializing in cross-platform benchmarking and co-design of quantum systems, Sensebat explores quantum annealing techniques for solving partial differential equations and identifies quantum machine learning applications. His work bridges theoretical physics, mathematics, and high-performance computing. Projects: Currently contributes to the QSolid project, a German national initiative for quantum computing advancements. His master's thesis, "Quantum Annealers and Partial Differential Equations: The Gate-Based Encoding Approach," demonstrates expertise in quantum simulation methodologies.

Markus FidlerView profile

Professor

• Performance of Communication Systems
• Network Calculus
• Effective Bandwidths
• +13 more

Prof. Dr.-Ing. Markus Fidler is a Professor of Communications Networks at the Institute of Communications Technology , affiliated with the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover since 2009. His academic journey began with a doctoral degree in Computer Engineering from RWTH Aachen University (2004), followed by post-doctoral fellowships at Institute Mittag-Leffler (2004), NTNU Trondheim (2005), and University of Toronto (2006). He led the Emmy Noether Research Group at Technische University of Darmstadt (2007-2008), where he also earned his habilitation (2008). His research interests span Network Calculus , Effective Bandwidths , Available Bandwidth Estimation , and Parallel Systems (e.g., Multi-path Protocols, Synchronization Constraints). He explores Future Internet architectures, Wireless Communication (including Cognitive Radio and Car-2-X systems), and Congestion Control for Cooperative ADAS and Platooning. His work integrates Machine Learning and Stochastic Modeling for network performance analysis. Recent publication trends emphasize Age-of-Information (AoI) in tandem queues, Statistical Bounds for parallel systems, and Granularity Trade-Offs in multi-server environments. He applies Min-plus Algebra to AoI modeling and investigates Hybrid Time-Event Triggered Systems for resource-efficient communication. His projects include ADINeMo (2024) on Deviation-of-Information for sensor sampling and VaMoS 2 (2024) on validated models for MapReduce scaling. Scientific Awards ERC Starting Grant (2012) Advising includes current doctoral students Sami Akin , Brenton Walker , and Mahsa Noroozi , alongside numerous past advisees now holding academic positions globally. He leads DFG-funded projects such as FeelMaTyC (2017-2020) and GRK SocialCars (2014-2023), focusing on IoT, cooperative mobility, and network calculus applications.

Sascha KochView profile

Professor

• Geoinformatics
• Machine Learning
• Municipal Heat Planning
• +3 more

Prof. Dr. Sascha Koch is a Professor of Computer Science at Jade University of Applied Sciences, specializing in geoinformatics, machine learning, and thermal energy planning. He leads the Institute Board of the IAPG (Institute for Applied Photogrammetry and Geoinformatics) and directs key projects like DiViAS (Digitization, Visualization, and Analysis of Collection Items) and ReStEP (Regional Strategic Energy Planning). His work focuses on AI-driven geospatial analysis for sustainable energy systems, including heat network suitability, geothermal potential modeling, and participatory urban planning. Projects: DiViAS (2023–2026), ReStEP (2024–2026), Comprehensive Desealing Cadastre Funding: Federal Ministry of Education and Research, Zukunft.Niedersachsen Research Themes: AI for energy transition, GeoVisual Analytics, heat management in smart cities He supervises master’s and bachelor’s theses on topics like geothermal planning, AI in municipal heat systems, and data pipelines for thermal analysis. His collaborations include the Lower Saxony State Office for Geoinformation and Surveying (LGLN) and regional museums. While his email isn’t publicly listed, his publications reflect his expertise in geospatial data science and sustainable energy applications.

Roland PeschView profile

Professor

• Geoinformation Systems
• Marine Conservation
• Spatial Modeling
• +8 more

Prof. Dr. Roland Pesch is a Professor at the Institute for Applied Photogrammetry and Geoinformatics (IAPG) in Lower Saxony, Germany. He leads the professorship for Fundamentals and Applications of Geoinformation Systems , focusing on marine spatial protection, biodiversity modeling, and sustainable urban-rural planning through GIS and remote sensing. Projects : Protect Baltic (2023-2028), 4N: Geo-Toolbox (2022-2026) Cooperation Partners : Lower Saxony State Office for Geoinformation and Land Surveying (LGLN) His research interests span Marine Conservation , Spatial Modeling , and Environmental Science , with applications in Biodiversity Hotspots , Climate Scenarios , and Urban Green Spaces . Recent work includes systematic reviews of public green spaces, habitat suitability analysis for Ostrea edulis , and predictive modeling of marine habitats using convolutional meshes and sonar data. Publications trend toward interdisciplinary GIS applications in marine ecology and urban sustainability , often involving remote sensing and spatiotemporal analysis . Key subfields include habitat mapping , marine protected areas , and climate change impact assessment . He supervises theses on topics like reed bed monitoring , surface sealing analysis , and green space accessibility at institutions in Lower Saxony. His lab collaborates with the LGLN to optimize land management workflows using 3D metrology and environmental data systems .

Andreas WichmannView profile

Professor

• Cartography
• Geovisualization
• 3D Metrology
• +10 more

Prof. Dr.-Ing. Andreas Wichmann is a faculty member at the Carl von Ossietzky University of Oldenburg, holding the Chair of Cartography and Geovisualization. He contributes to the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and leads research in 3D metrology, GIS, and geospatial data systems. Academic Rank: Professor Department: Cartography and Geovisualization Labs: Laboratory for Optical 3D Metrology His research focuses on 3D building reconstruction, LiDAR data analysis, and digital mapping applications. Recent projects include deep learning for building segmentation, Scan2BIM optimization, and 3D visualization of cultural heritage sites like the Bachmann Museum in Unreal Engine. Key trends in his publications involve integrating photogrammetry with machine learning, improving GIS methodologies, and developing geospatial datasets for civil protection and heritage preservation. Scientific Awards ASPRS Esri Award for Best Scientific Paper in GIS (2020) ISPRS Best Paper Award (2018) AOLS Best Poster Award (2016) Prof. Wichmann supervises final theses across M.Sc. and B.Sc. programs in Geoinformatics and Applied Geodesy. His collaborations include the Lower Saxony State Office for Geoinformation and Surveying (LGLN) and private engineering firms.