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Anna-Carolina HaenschView profile

Assistant Professor

• Missing Data Analysis
• Synthetic Data Generation
• Big Data in Social Sciences
• +6 more

Anna-Carolina Haensch is a Lecturer at the University of Munich in the Chair of Statistics and Data Science in Social Sciences and the Humanities and an Assistant Professor at the University of Maryland in the International Program in Survey and Data Science. Her interdisciplinary work bridges statistics, computational social science, and natural language processing, with a focus on methodological innovation in social research. Education: PhD in Sociology, University of Mannheim (2017-2021) M.Sc. in Survey Statistics, University of Bamberg (2014-2017) B.A. in Political Science/Sociology, Ludwig-Maximilians-Universität München (2011-2014) Dr. Haensch's research centers on missing data, synthetic data, and big data applications in social sciences. She develops advanced statistical methods for survey data harmonization, multiple imputation techniques, and leverages natural language processing to analyze complex social phenomena. Her work consistently addresses methodological challenges in social research with practical applications for data collection and analysis. Her recent publications reveal a significant trend toward integrating large language models with traditional survey methodology, exploring how AI can enhance data collection, analysis, and interpretation in social science research. She has made substantial contributions to understanding missing data patterns, developing synthetic data approaches, and examining the societal implications of AI tools across multiple domains including mental health, political science, and housing policy. Scientific Awards: 2022 AAPOR Burns "Bud" Roper Fellow Award 2022 AAPOR Warren J. Mitofsky Innovators Award (as part of CTIS team) 2022 AAPOR Policy Impact Award (as part of CTIS team) 2011-2017 Max-Weber-Programm (undergraduate and graduate stipend) Dr. Haensch actively mentors the next generation of researchers, currently supervising 3 PhD theses on statistical education, machine learning applications in social sciences, and synthetic data generation with LLMs. She has guided approximately 6 master's theses and 15 bachelor's theses at the University of Munich since 2022, with topics primarily related to synthetic data, multiple imputation, and LLM applications. Her teaching spans statistical methods, data science techniques for survey researchers, and specialized topics in big data analysis. She has secured teaching grants including a €20,000 promotion for the RAINER project (R Assistant IN Error Resolution) for 2024-2025 and a €10,000 LMU-NYU Scholarship in 2023. She serves on several important boards including the Eurostat EMOS Board (2024-2026), the Ethics Commission of Faculty 16 at LMU (2023-2025), and as Women's Representative at the Institute for Statistics, LMU (2024-2026). Her collaborative work with the University of Maryland Social Data Science Center and involvement in the Global COVID-19 Trends and Impact Survey demonstrates her commitment to large-scale data collection initiatives and real-time social research.

Marco GüntherView profile

Professor

• Mathematical Modeling
• Computational Fluid Dynamics (CFD)
• Multiphase Flows
• +3 more

Prof. Dr. Marco Günther is a faculty member in the Faculty of Engineering at Saarland University of Applied Sciences, specializing in Mathematics and Fluid Mechanics. His academic role includes teaching and research leadership in computational fluid dynamics and simulation. Research Focus: Mathematical modeling of flow processes, multiphase flows, and CFD applications Lectures: Mathematics for engineering, numerical simulation, technical fluid mechanics Projects: Numerical simulation of laser welding, flow behavior in wastewater treatment, and particle-laden flows External Affiliation: Scientific consultant for Fraunhofer-ITWM's Transport Processes division His research emphasizes industrial applications of fluid mechanics, with projects spanning from wind turbine optimization to urban water management. The Linux-based simulation tool 'gm.linux' was developed as a companion to his widely adopted textbook on mathematical modeling.

• Highway Engineering
• Asphalt Materials
• Pavement Design
• +5 more

Markus Oeser is a full professor at the Chair and Institute of Highway Engineering, affiliated with the Federal Highway Research Institute (BASt). His research advances pavement technology, smart infrastructure, and sustainable materials through computational modeling and experimental characterization. Research Focus His work spans: Advanced asphalt materials (self-healing, inductive, nano-enhanced) Digital twins for real-time pavement monitoring Traffic flow modeling and AI-driven infrastructure management Sustainable recycling of construction waste Piezoresistive sensors for smart roads Publication Trends (2024-2025) Recent articles emphasize multi-scale modeling (atomic to structural levels), integration of AI/ML in pavement systems, and sustainability-driven material innovations. Dominant themes include nanomaterial-enhanced composites, digital twins for infrastructure, and traffic microsimulation. Affiliations & Infrastructure Leads research at BASt's highway engineering facilities, leveraging laboratories for materials testing, computational modeling, and full-scale pavement validation.

• Geology
• Reservoir Engineering
• Microbial Activity in Rocks
• +3 more

Saeed Khajooie is a Researcher in the Teaching and Research Area Applied Structural Geology at RWTH Aachen University. His work focuses on subsurface geological systems, particularly in the context of energy storage and reservoir characterization. Research interests include: Underground hydrogen storage mechanisms Microbial activity impacts on reservoir rocks Reservoir compartmentalization analysis Geochemical-reservoir engineering integration Fluid dynamics in hydrocarbon systems Recent research trends demonstrate expertise in: Hydrogen-rock interaction studies Microbial permeability alteration Reservoir continuity evaluation Formation testing data interpretation Capillary pressure applications Fluid property modeling

• Bioinformatics
• Time Series Analysis
• Machine Learning
• +3 more

Patrick Schäfer is a Researcher at the Institute of Computer Science, Humboldt University of Berlin. His work focuses on time series analytics, bioinformatics, and data series management. He contributes to unsupervised learning algorithms and temporal data mining. Affiliation: Institute of Computer Science, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin Email: Patrick.Schaefer@hu-berlin.de Phone: 030 2093-41287 Address: Unter den Linden 6, 10099 Berlin Research Interests His research spans time series classification , segmentation algorithms , and bioinformatics data management . He develops tools for unsupervised analysis of multidimensional temporal data, focusing on motif detection, similarity search, and efficient indexing. Key Themes : Parameter-free temporal data analysis High-dimensional time series similarity search Unsupervised motif discovery Human activity segmentation benchmarks Integration of time series analytics with molecular simulations

• Neuroscience
• Neuroimaging
• Magnetic Resonance Spectroscopy (MRS)
• +6 more

Theresia Ziegs is a Research Associate at the Tübingen Center for Digital Education (TüCeDE) and serves as the link to the AI Makerspace , an extracurricular learning center for innovative technologies. She focuses on developing AI-supported methods for adaptive teaching within the MINT-ProNeD project and coordinates courses on robotics and AI for students, alongside designing teacher training programs in these fields. Education : Dr. rer. nat. in Neuroscience (2017–2023), Max Planck Institute for Biological Cybernetics, Tübingen M.Sc. in 2016, University of Rostock B.Sc. in Physics (2011–2014), University of Rostock Her research centers on neuroscience , particularly using 1H/13C FID-MRSI at ultra-high magnetic fields (9.4T) to study brain metabolism, including glutamate and glucose dynamics . Her work emphasizes methodological optimization for improved reproducibility and spatial resolution in metabolic imaging. Her publications highlight expertise in metabolic mapping , signal processing , and machine learning-enhanced imaging techniques , with applications in human brain metabolism and neuroimaging . She has received the ISMRM Magna Cum Laude Merit Award (2022) for her contributions. Scientific Awards : ISMRM Magna Cum Laude Merit Award (2022) Theresia actively collaborates on AI integration in education and teacher training , bridging advanced neuroimaging research with pedagogical innovation at the University of Tübingen.

• Radiative Heat Transfer
• Ultrafast Laser-Tissue Interactions
• Nanoscale Thermal Transport
• +4 more

Professor Zhixiong Guo is a distinguished faculty member in the Department of Mechanical and Aerospace Engineering at Rutgers, The State University of New Jersey. He serves as Editor-in-Chief for the Journal of Enhanced Heat Transfer and Heat Transfer Research, and has made significant contributions to thermal sciences and engineering. His research spans multiple domains including radiative heat transfer, ultrafast laser-tissue interactions, and nanoscale thermal phenomena. Professor Guo received his educational foundation at Tsinghua University in Beijing, China, where he earned a B.S., M.S., and Dr. Eng. in Engineering Physics. His academic excellence was evident as he graduated first in his class (1/28) for his B.S. and completed his M.S. one year ahead of schedule. He then pursued and completed his Ph.D. in Mechanical Engineering from Polytechnic University (now Polytechnic School of Engineering, New York University) in just two years. Professor Guo's research interests center on advanced heat transfer phenomena, with particular expertise in radiative transfer in participating media, ultrafast laser-tissue interactions, and nanoscale thermal transport. His work bridges theoretical modeling with practical applications in energy systems, biomedical engineering, and materials science. He has pioneered computational methods for solving complex radiation transfer problems and developed innovative optical sensing techniques using whispering-gallery mode resonators. Analysis of Professor Guo's recent publications reveals a strong focus on emerging thermal technologies, including machine learning applications in heat transfer prediction, nanofluid thermal properties, and advanced materials for thermal management. His work demonstrates a clear trajectory from fundamental radiative transfer research toward practical applications in energy efficiency, biomedical diagnostics, and advanced manufacturing. Professor Guo has received numerous prestigious honors including: Fellow of the American Society of Mechanical Engineers (ASME), 2011 Fellow of the American Society of Thermal and Fluids Engineers (ASTFE), 2021 Rutgers, The Board of Trustees Award for Excellence in Research, 2018 As Editor-in-Chief of two leading journals in the field, Professor Guo has significantly shaped the direction of thermal sciences research. His laboratory at Rutgers focuses on cutting-edge research in optical thermal sensing, ultrafast radiation phenomena, and advanced computational methods for heat transfer analysis. Current research directions include machine learning applications in thermal systems and novel approaches to energy conversion and storage.

Jens KortusView profile

Professor

• Materials Science
• Theoretical Physics
• Semiconductor Defects
• +5 more

Jens Kortus is a Professor of Theoretical Physics at the Institute of Theoretical Physics , Freiberg University of Mining and Technology. He has held leadership roles including Dean of the Faculty of Chemistry and Physics , Vice Dean of the Faculty of Chemistry and Physics , and Director of the Institute for Theoretical Physics . His research spans computational materials science, semiconductor physics, and superconductivity. Education Ph.D. in Theoretical Physics (magna cum laude), TU Bergakademie Freiberg (1995) Master in Physics (summa cum laude), University of Wroclaw (1991) Research areas include defects in semiconductors, ab-initio methods (density functional theory, molecular dynamics), vibrational spectra prediction (IR/Raman), electronic structure of molecules/clusters, superconductivity in MgB 2 , and magnetic anisotropy in single-molecule magnets. He utilizes advanced computational tools like the LAPW method (WIEN code) and NRLMOL . Scientific awards include the Julius Weisbach Prize, Fast Breaking Paper recognition by ISI, Alan Berman Publications Award, and Schloeßmann fellowships. His work has been cited as Red Hot Research Paper by ISI.

Michael KochView profile

Professor

• Robotics
• Additive Manufacturing
• Materials Science
• +3 more

Prof. Michael Koch is a Professor at Technische Hochschule Nürnberg, specializing in interdisciplinary research at the intersection of robotics, materials science, and industrial engineering. His work focuses on advancing additive manufacturing, robotics integration, and computational simulation in manufacturing processes. He holds a Dr.-Ing. and Dipl.-Wirt.-Ing., reflecting his expertise in engineering and applied sciences. Research interests include 3D printing optimization, cyber-physical systems, and safety analysis of materials under mechanical stress. He has contributed to innovations in robotics programming via augmented reality and developed frameworks for automated manufacturing processes. His studies on biomedical applications, such as ovarian cancer modeling, demonstrate a cross-disciplinary approach. Key projects involve robot-guided CT scanning for automotive industry 4.0, part orientation evaluation for additive manufacturing (Poeam), and safety protocols for explosives (PBX). His work emphasizes integrating real-world geometry data into simulations to improve accuracy and efficiency. Prof. Koch’s publications span over three decades, with a focus on manufacturing, robotics, and materials science. He has pioneered methods for energy-efficient CO2 capture systems and explored microstructural changes in materials under dynamic loading.

• Planetary Science
• Aeolian Geomorphology
• Mars Geology
• +4 more

Elena Favaro is a Research Fellow at the European Space Agency (ESA), specializing in planetary science with a focus on aeolian geomorphology. She utilizes geographic information systems and high-resolution imagery to study landforms (yardangs, periodic bedrock ridges) and bedforms (megaripples, dunes, transverse aeolian ridges) on Earth and Mars, with current emphasis on Oxia Planum—the designated 2030 landing site for ESA's ExoMars Rosalind Franklin rover mission. Her research reconstructs Martian climatic history through analysis of aeolian features using remote sensing, 3D image analysis, and GIS. Key interests include landscape evolution, sediment transport processes, and wind dynamics, with specific focus on how yardangs, periodic bedrock ridges, and dust devils record past and present environmental conditions. Current work integrates deep learning for terrain classification to support mission planning at Oxia Planum. Recent publications reveal concentrated research on Oxia Planum, featuring high-resolution geological mapping, wind regime modeling, and classification of aeolian bedforms. Studies emphasize periodic bedrock ridges as paleowind indicators, secondary cratering for stratigraphic dating, and megaripple architecture for sediment transport analysis. This work provides critical context for rover operations by linking surface features to ancient climate conditions and contemporary aeolian processes.

Daniel MüllerView profile

Professor

• Land use
• Sustainability
• Agricultural production
• +2 more

Prof. Dr. Daniel Müller is a leading academic at the Leibniz Institute of Agricultural Development in the Global South (IAMO) and serves as Deputy Head of the Department for Structural Development of Farms and Rural Areas. His research focuses on land-system change , sustainability , agricultural production , and climate change impacts, with fieldwork spanning Central and Eastern Europe , former Soviet Union , Southeast Asia , and Western China . He combines resource economics and natural sciences using multi-scalar empirical methods. Key research areas: Land use Sustainability Agricultural production Climate change Ecosystem services Recent publications reveal trends in agricultural land ownership , cropland abandonment , and snow hydrology in Central Asia. His work addresses agricultural resilience in conflict zones (e.g., Ukraine) and employs Bayesian networks and remote sensing for land-use analysis. He leads international research groups including the Land Systems Group , International China Research Group , and Central Asia Research Group , with collaborations on platforms like ResearchGate , ORCID , and Google Scholar .

Xiangbin TengView profile

Assistant Professor

• Neuroscience
• Cognitive Psychology
• Neurocognitive Science
• +3 more

Prof. Xiangbin Teng is an Assistant Professor in the Department of Psychology at the Chinese University of Hong Kong. Previously, he was a postdoctoral researcher at the Neurocognitive and Experimental Psychology lab in Frankfurt/Berlin (2021–2022), following his PhD in Cognition and Perception from New York University and M.Sc./B.Sc. degrees from Peking University and Shanghai Jiao Tong University. His research focuses on neurocognitive mechanisms underlying auditory perception, particularly speech and music processing. Key areas include neural oscillations, temporal dynamics, and multiscale auditory encoding. He investigates how rhythmic patterns in language (e.g., ancient Chinese poetry) and music influence perceptual and neural processes. Publications highlight contributions to understanding theta/gamma band neural coding, EEG modulation spectra, and phase precession mechanisms. His work bridges computational models (e.g., recurrent neural networks) with empirical neuroscience findings. ERC Consolidator Grant (2025): Leading the "TRANSFORM" project exploring visual system development from infancy to adulthood. Grants & Collaborations: Extensive research on auditory cortex fMRI methods and cochlear implant simulations. Labs/Teams: Active in CUHK's cognitive neuroscience group and previously at the Max Planck Institutes. His current lab investigates sensory-motor interactions and predictive coding in perceptual systems.

Christoph A. WeberView profile

Research Professor

• Biophysics
• Soft Matter Physics
• Active Matter
• +3 more

Christoph A. Weber is a Group Leader at the Max Planck Institute for the Physics of Complex Systems and the Center for Systems Biology Dresden (CSBD). His research focuses on mesoscopic physics of life , including phase transitions in biological systems, intra-cellular organization mechanisms, and the physical principles underlying the origin of life. He holds a PhD in physics from Ludwig Maximilian University (LMU) Munich, where he studied under Prof. Erwin Frey. Prior to his current position, he conducted postdoctoral research at Harvard University and the Max Planck Institute for the Physics of Complex Systems. His work bridges biophysics, soft matter physics, and active matter, with a strong emphasis on theoretical modeling and interdisciplinary collaboration. Education : PhD in Physics (summa cum laude), LMU Munich (2013) Postdoc at Harvard University (2016–2018) Postdoc at MPIPKS Dresden (2013–2016) Research Highlights : Active droplet dynamics and protocell modeling Phase separation in biological systems (coacervates, condensates) Non-equilibrium phenomena in soft matter Origin-of-life studies via synthetic cell systems Collaborators and Labs : Prof. Olivier Dauchot (ESPCI Paris) Prof. Hugues Chaté (CEA Paris) Argonne National Laboratory (collaboration with Igor Aronson) Advising : He has mentored over 15 PhD, Master's, and undergraduate students in Dresden, Cambridge, and Munich, focusing on computational and theoretical approaches to active matter and biological physics.

Alexander WeißeView profile

Researcher

• Condensed Matter Physics
• Mathematical Physics
• Integrable Systems
• +4 more

Alexander Weiße is a tenured researcher at the Max Planck Institute for Mathematics in Bonn, Germany, where he also serves as Head of IT. Previously, he was a junior professor for computational physics at the University of Greifswald (until 2009). He holds a PhD in physics from the University of Bayreuth (2002) and completed postdoctoral research at the Max Planck Institute for the Physics of Complex Systems, University of New South Wales (Sydney), and University of Greifswald. Research Interests : His work spans condensed matter physics, mathematical physics, and integrable systems. Recent focuses include spin systems (e.g., XXZ Heisenberg chain), disorder/randomness in quantum systems, and computational methods (e.g., Chebyshev expansion, kernel polynomial method). He also contributes to high-performance computing and programming tools (C, Form, Julia). Teaching & Leadership : He has taught courses on computational physics, group theory, numerical many-particle physics, and computer algebra. Notable collaborations involve the Bergische Universität Wuppertal group on spin model correlation functions. He co-edited the textbook Computational Many-Particle Physics (Springer, 2008). IT Responsibilities : As Head of IT, he manages IT infrastructure security and strategy, with interests in cybersecurity and computational resource optimization.

Lutz EcksteinView profile

Professor

• Automotive Engineering
• Automated Driving Systems
• Vehicle Dynamics
• +4 more

Univ.-Prof. Dr.-Ing. Lutz Eckstein is the Head of the Institute of Automotive Engineering (ika) at RWTH Aachen University and a leading expert in automotive engineering. He concurrently serves as President of the VDI Association of German Engineers, President of the VDI Scientific Advisory Board for the Federal Ministry of Digital and Transport (BMDV), and Scientific Director of the NRW Competence Network for Automated and Connected Mobility. His professional background includes senior roles at BMW and DaimlerChrysler, focusing on vehicle safety, driver assistance systems, and ergonomics. Eckstein holds a Doctorate from the University of Stuttgart (2000) and received the Artur Fischer Prize (1995) for academic excellence. His research interests span automated driving systems, human-machine interfaces, scenario-based testing, and mobility infrastructure. He leads high-profile projects like UNICARagil and coordinates the Mobility and Transport profile at RWTH Aachen. Over 80 patents and publications reflect his contributions to automotive innovation, including advancements in sensor fusion, digital twins, and cooperative intelligent transport systems (C-ITS). His recent work emphasizes safety assurance for automated vehicles, scenario databases, and scalable simulation frameworks. Eckstein's interdisciplinary approach bridges academia and industry, driving transformative solutions for future mobility challenges.