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Vincent HeuvelineView profile

Professor

• Uncertainty Quantification
• High Performance Computing
• Data Intensive Computing
• +3 more

Professor Vincent Heuveline serves as Professor and Head of the Engineering Mathematics and Computing Lab (EMCL) at Heidelberg University's Interdisciplinary Center for Scientific Computing (IWR). He concurrently holds the positions of Chief Information Officer (CIO) and Director of the Computing Centre at the university, while leading the Data Mining and Uncertainty Quantification (DMQ) group at HITS gGmbH. His academic background includes: Studies in Mathematics, Physics, and Computer Science at Caen (France) and Würzburg (Germany) PhD in Computer Science (1997) from Université de Rennes and INRIA Habilitation in Mathematics (2002) at Heidelberg University Research focuses on uncertainty quantification (UQ) , high-performance computing (HPC) , and data-intensive computing with applications in medical engineering. He is deeply engaged in IT security research and teaching, developing practical frameworks for industrial deployment of numerical simulations and secure computing infrastructures. Recent publications reveal converging trends in biomedical image segmentation (e.g., Biomedisa platform), metabolic modeling for newborn diagnostics, and cybersecurity solutions like DGA detection. Key interdisciplinary themes include machine learning for medical applications, HPC algorithm optimization, and network security frameworks addressing real-world industrial challenges. He leads the EMCL and DMQ research groups, driving innovation in computational science. As CIO, he shapes Heidelberg University's digital infrastructure strategy while maintaining active industry collaborations for translating academic research into simulation and security solutions.

• Energy Systems
• Smart Grids
• Electricity Markets
• +7 more

Prof. Dr. Anke Weidlich is a faculty member at the University of Freiburg, holding the Chair for Control and Integration of Grids at INATECH. She is actively involved in energy systems research, focusing on smart grids, electricity markets, and sustainability. Her roles include Dean of Study for Sustainable Systems Engineering and Academic Director of the M.Sc. Solar Energy Engineering program. She has held previous professorships at the University of Applied Sciences Offenburg and SAP AG, and conducted research at institutions such as the University of Karlsruhe and Iowa State University. Education: PhD (Dr. rer. pol.) from University of Karlsruhe (2008), Diplôme in Energy Economics (2003) Research Interests: Agent-based modeling of energy markets, flexibility in power systems, decarbonization pathways, and policy analysis Her research emphasizes interdisciplinary approaches, integrating technical, economic, and policy dimensions of energy systems. Key contributions include frameworks for electricity market dynamics, agent-based simulations, and studies on renewable energy integration. She serves on multiple expert commissions, including the German Ministry’s Energy Transition Monitoring and the EWI Scientific Advisory Board. Publications span over 15 years, covering smart grid technologies, carbon management strategies, and energy market design. Notable awards include the DAAD scholarship and Maple-Award for modeling excellence. Her work bridges academia and policy, addressing systemic challenges in achieving climate neutrality.

• Big Data Analytics
• Particle Physics
• Deep Learning
• +7 more

Johannes Erdmann is a University Professor and leads the Research and Teaching Area of Big Data Analytics in Physics at RWTH Aachen University. He is affiliated with the Department of Physics within the Faculty of Mathematics, Computer Science and Natural Sciences. His research integrates advanced data science techniques with fundamental physics investigations. His research focuses on leveraging big data and machine learning methods to solve complex problems in particle and astroparticle physics. Key areas include the physics of the Higgs boson, data analysis at the CMS experiment at CERN, and the development of deep learning techniques for high energy physics. He also contributes to gravitational wave physics, particularly in noise suppression strategies for the future Einstein Telescope. The recent publications highlight a strong trend toward methodological innovation, especially in deep learning applications for particle physics simulations and data analysis. His team is actively developing novel neural network architectures, fast simulation tools, and automated quality assurance systems, all aimed at enhancing the precision and efficiency of large-scale physics experiments. Heisenberg-Professur funded by the Deutsche Forschungsgemeinschaft (DFG) Prof. Erdmann supervises research projects and leads a research group focused on data-intensive physics. He is actively involved in securing and managing research grants, particularly those supporting computational and methodological developments in experimental physics. His group collaborates with international teams at CERN and within the Einstein Telescope consortium. His research group, part of the Big Data Analytics in Physics unit, works on cutting-edge challenges in data processing for particle and gravitational wave physics. The team develops deep learning models, simulation frameworks, and data quality tools, contributing to major international collaborations such as CMS and the Einstein Telescope project.

• X-ray Crystallography
• Serial Crystallography
• Femtosecond X-ray Lasers
• +3 more

Oleksandr Yefanov is a Senior Scientist at the Center for Free-Electron Laser Science (CFEL) in Hamburg, Germany, affiliated with DESY and XFEL.EU. His work focuses on advancing X-ray crystallography techniques using free-electron lasers and synchrotrons. He has contributed to data reduction methods, high-resolution imaging, and sample delivery systems for structural biology. Key Affiliations: CFEL, DESY, XFEL.EU Research Interests: X-ray crystallography, serial femtosecond crystallography, diffraction imaging, structural biology, data analysis algorithms. Article Trends (2017-2025): Yefanov's publications emphasize improving resolution in X-ray microscopy, developing computational tools for diffraction data, and optimizing sample delivery for XFEL experiments. Recent work explores sub-3nm focusing, in situ monitoring of chemical processes, and multi-dimensional serial crystallography. Advising: Supervised Galchenkova M.'s 2024 PhD dissertation on diffraction analysis methods.

Bastian BeggelView profile

Professor

• Data Science
• Database Systems
• Deep Learning
• +3 more

Bastian Beggel is a Professor at Kaiserslautern University of Applied Sciences in the School of Computer Science and Microsystems Technology (Zweibrücken campus) since January 2018. He teaches Databases and Data Analysis while serving as program director for both the regular and dual Bachelor's programs in Applied Computer Science. He also holds membership on the Computer Science Examination Board (FB IMST). Academic Background: Mathematics studies at Technical University of Munich (1998-2004) Ph.D. at Max Planck Institute for Informatics, Saarbrücken (2009-2014) Research Focus: His work centers on practical data-intensive systems including the development of the DBcoach educational platform for database instruction. Research spans Data Science applications for university data utilization, Deep Learning in Computer Vision, and Geo-Analytics with specialization in OpenStreetMap data indexing and map-matching algorithms. His approach emphasizes bridging theoretical concepts with real-world industrial applications. Professional Experience: Prior to academia, he worked as a Database Developer at o2 Germany (2005-2008), Research Assistant at Max Planck Institute (2009-2015), and Data Scientist at BMW Group (2015-2017). He supervises student theses and leads the operational development of the DBcoach platform, integrating educational technology with practical database training. Operational Context: Maintains office hours in Room H243 (Zweibrücken campus) with appointments scheduled via email. His work demonstrates consistent translation of industry experience into academic teaching and research infrastructure.

Daniel LinkView profile

Professor

• Training Science
• Sports Informatics
• Digitalization in Sports
• +3 more

Prof. Daniel Link is a Professor in the Department of Training Science and Sports Informatics at the Technical University of Munich's TUM School of Medicine and Health. His research focuses on systematics of training and competition, digitalization of sports (including AI-driven data analysis), and technical-tactical performance metrics. He supports German elite sports in match analysis processes and holds roles as spokesperson for the Sports Informatics and Sports Technology section of the German Association of Sport Science (dvs), Secretary General of the International Association of Computer Science in Sport (IACSS), and reviewer for the Federal Institute of Sport Science (BISp). Prof. Link earned a computer science degree from TU Dortmund University, a doctorate in sports science from the University of Augsburg, and a venia legendi in sports science from TU Munich. His work bridges computer science and sports science, emphasizing data-driven approaches to athlete performance and ecological studies on insects (e.g., butterfly genomics, climate vulnerability). His recent research trends include computational analysis of sports performance, ecological studies on insect adaptations, and validation of electronic tracking systems. Key contributions involve genome sequencing projects, thermal tolerance studies in endangered species, and methodological advancements in sports data collection.

Fred HamprechtView profile

Professor

• Scientific Computing
• Computer Science
• Applied Mathematics
• +1 more

Fred Hamprecht is a Professor and Principal Investigator at the Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, where he leads research initiatives within one of Germany's premier academic institutions. His research focuses on Scientific Computing , Computer Science , and Applied Mathematics , with specific contributions to image analysis through the SIMPLAIX project. This work bridges computational theory and practical applications in data-intensive scientific domains. As part of IWR's interdisciplinary framework, Professor Hamprecht collaborates across technical and scientific teams to advance computational methodologies, leveraging Heidelberg University's resources for cutting-edge research in algorithm development and visual data interpretation.

• Social Network Analysis
• Formal Concept Analysis
• Graph Mining
• +7 more

Bénédicte Le Grand is a prominent computer science researcher with an extensive publication record spanning over two decades, from 1999 to 2025. Her academic work demonstrates significant contributions across multiple domains within computer science, particularly in social network analysis, formal concept analysis, and graph mining methodologies. She maintains active collaborations with numerous researchers including Marie-Aude Aufaure (16 publications), Michel Soto (13 publications), and Nourhène Ben Rabah (12 publications), indicating her integration within a strong academic network. Her research interests center around information retrieval systems, semantic web technologies, and data mining approaches, with recent work showing a notable shift toward healthcare applications of artificial intelligence. In the past five years, she has published extensively on medical diagnostic applications including Alzheimer's detection, polycystic ovary syndrome detection, and depression identification through social network analysis. Her work also extends to educational data mining, affective computing for workplace stress detection, and explainable AI for security applications. The analysis of her publication trends reveals a consistent research trajectory that has evolved from foundational work in semantic web and topic map visualization in the early 2000s toward increasingly applied machine learning and AI solutions for healthcare and education challenges. Her recent publications demonstrate a strong interdisciplinary approach, bridging computer science with medical and educational domains to develop practical AI solutions. She has been particularly active in French academic conferences such as INFORSID and has contributed to international venues including RCIS (Research Challenges in Information Systems), ICAART (International Conference on Agents and Artificial Intelligence), and AICCSA (International Conference on Computer Systems and Applications), establishing herself as a significant contributor to the European computer science research community.

• High-Performance Computing
• Computational Physics
• Data Science
• +3 more

Guido Juckeland is the Head of the Computational Science department at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , a leading research center focused on interdisciplinary computational science and high-performance computing. Role: Head of Computational Science Institution: Helmholtz-Zentrum Dresden-Rossendorf (HZDR) School: Department of Information Services and Computing Department: Computational Science His research interests center on advancing computational methods, parallel programming, and simulation technologies for scientific applications, particularly in physics and data-intensive domains. As a professor, he drives innovation in computational infrastructure and education. Contact: g.juckeland@hzdr.de | LinkedIn | ResearchGate

• Psychometrics
• Structural Equation Modeling
• Bayesian Statistics
• +5 more

Prof. Dr. Holger Brandt serves as Professor of Psychometrics at the Methods Center within the Department of Social Sciences, Faculty of Economics and Social Sciences, Eberhard Karls University of Tübingen since August 2021. Previously, he held Assistant Professor positions at the University of Zurich (2019-2021) and University of Kansas (2016-2019), following a postdoctoral fellowship at Tübingen's Hector Institute for Empirical Educational Research (2013-2016). His educational background includes a PhD (Promotion) from Goethe University Frankfurt's Institute of Psychology in 2013. Brandt's research pioneers advanced methodological frameworks at the intersection of psychometrics, statistics, and machine learning. He specializes in developing dynamic models for intensive longitudinal data, Bayesian estimation techniques, causal mediator analysis, and identification of inattentive response behaviors in surveys. His work rigorously addresses challenges in measurement invariance, structural equation modeling, and handling complex dependencies in social science data. Analysis of his recent publications reveals a dominant focus on Bayesian approaches for latent variable modeling, particularly spike-and-slab priors and latent class methods. His research consistently targets data quality issues in survey methodology while advancing causal inference techniques that relax traditional no-unmeasured-confounder assumptions. Applications span educational research, psychological assessment, and therapeutic alliance dynamics. As a core member of Tübingen's Methods Center, Brandt provides critical methodological infrastructure for social science research across the university, supporting researchers through statistical consulting and advanced methodology development.

• Latent Variable Models
• Dynamic Latent Variable Models
• Latent Class Methods
• +8 more

Augustin Kelava is a Professor at the Department of Quantitative Methods, Eberhard Karls University of Tübingen. He has held this position since 2018 and leads the Methods Center as Managing Director. Previously, he was Professor at the Hector Institute for Empirical Educational Research (2013-2018) and Junior Professor at Technical University of Darmstadt (2011-2013). PhD in Psychology (Goethe University Frankfurt, 2009) Diploma in Psychology (Goethe University Frankfurt, 2004) Kelava specializes in latent variable modeling, machine learning in social sciences, and educational research. His work spans dynamic latent class models, Bayesian regularization techniques, and prediction of human behavior using intensive longitudinal data. He contributes to psychometric theory (e.g., item response theory extensions) and applies these methods to diverse fields including sports science and emotion regulation. Editor of "Testtheorie und Fragebogenkonstruktion" (3rd ed., Springer, 2020) Key researcher in the Cluster of Excellence "Machine Learning in Science" Active in methodological conferences (FGME 2017, SEM 2019) Review activities for 20+ journals and foundations including Psychometrika, DFG, and SNSF His recent publications focus on integrating machine learning with psychometrics, addressing identifiability in complex models, and evaluating personality assessment validity for large language models. He collaborates with researchers across psychology, education, and computational fields.

• Medical Natural Language Processing
• Clinical Text Analysis
• Radiology Informatics
• +5 more

Chaitanya Shivade is a prominent researcher specializing in medical natural language processing with significant contributions to clinical text analysis, radiology informatics, and behavioral health documentation. His work bridges computational linguistics and healthcare applications, focusing on practical solutions for clinical documentation challenges. Shivade's research spans multiple critical areas: developing evaluation frameworks for behavioral therapy notes (TN-Eval), creating shared tasks for medical summarization (MEDIQA), advancing visual dialog systems for radiology, and pioneering synthetic clinical note generation. He has made substantial contributions to textual inference in clinical domains through the MedNLI dataset and has explored fundamental linguistic challenges like negation detection and gradable term analysis in medical text. As a workshop organizer for the NLP for Medical Conversations series, he has helped shape community standards and foster collaboration. His publication record demonstrates consistent leadership in applying NLP to real-world healthcare problems, with particular emphasis on evaluation methodologies, dataset creation, and practical clinical applications. Shivade has collaborated extensively with medical professionals and researchers across institutions to ensure clinical relevance of his technical work. Organized MEDIQA shared tasks (2019, 2021) Co-organized NLP for Medical Conversations workshops (2019, 2020) Developed TN-Eval framework for therapy note quality assessment Created MedNLI dataset for clinical textual inference Pioneered synthetic clinical note generation approaches His work consistently addresses the tension between clinical utility and technical innovation, with growing emphasis on evaluating LLM performance in healthcare contexts. The progression from foundational clinical NLP techniques to complex evaluation frameworks demonstrates his evolving research trajectory toward ensuring reliable AI deployment in medical settings.

Stefan KugeleView profile

Research Professor

• Model-based Systems Engineering
• Software Engineering
• Artificial Intelligence
• +3 more

Stefan Kugele is a Research Professor for Model-based Systems Engineering and Software Engineering at Technische Hochschule Ingolstadt (THI). His research addresses complexity in mobility systems (e.g., automated driving/flying) through model-based approaches and formal verification. He investigates interactions between humans, environments, and autonomous systems to ensure reliability in software-intensive systems with AI components. Research Interests: Focuses on mathematically rigorous methods for system modeling, specification, and analysis of self-adaptive systems. Key areas include: Integration of data-driven AI components in engineering workflows Formal guarantees for system correctness Monitoring frameworks for autonomous operations Current Projects: MANNHEIM AutoDevSafeOps DevGPT Legacy AI Education & Career Since 07/2020: Research Professor, THI 10/2015–06/2020: Temporary Academic Council, Software & Systems Engineering, Technical University of Munich 11/2006–09/2015: Research Assistant, Technical University of Munich PhD (2006–2012) and MSc in Computer Science (minor: Electrical Engineering), Technical University of Munich

• Media Processes
• Business Process Management
• Process Customization
• +5 more

Thomas Rose is Professor for Media Processes at RWTH Aachen University and heads the research group on business process management at Fraunhofer FIT. He is affiliated with the Department of Computer Science (Informatik 5) and conducts research in media processes, process management, and digital collaboration systems for high-stakes domains such as healthcare and emergency response. His research focuses on the design and implementation of media processes for information capture and dissemination, along with advanced process management and customization techniques. Projects under his leadership include Setric (Security and Trust in Cities), ERMA (Electronic Risk Management Architecture), Olga (Online Guideline Assist for Intensive Care), and ZAMOMO (integration of model-based software and control engineering), all targeting real-world applications in public safety, healthcare, and industrial systems. His work has been recognized at the European level, with the Apnee(-Tu) project highlighted as a success story of European IST research by Commissioner Vivian Reding in 2005. The research group is funded by the B-IT Foundation, a 56 million Euro endowment supporting innovation at the intersection of Bonn and Aachen. Project Apnee(-Tu) selected as one of the success stories of European IST research by Commissioner Vivian Reding in 2005 Thomas Rose has supervised thesis projects and taught courses such as Data Visualization and Analytics and Distributed Ledger Technology. He collaborates extensively with Fraunhofer FIT and leads a research team focused on decision and process management support. His lab is embedded within the Fraunhofer Institute for Applied Information Technology, leveraging interdisciplinary teams to develop scalable, secure, and trustworthy process-aware systems.

• Data Stream Processing
• In-Network Computing
• Industrial Cyber-Physical Systems
• +7 more

Liam Tirpitz is a researcher and PhD candidate at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University. He is affiliated with the Chair of Computer Science i5 (Information Systems and Databases) and the Data Stream Management and Analysis Group, where he has been working since February 2022. Education : Master's degree in Computer Science from RWTH Aachen University (2021). His research focuses on data stream processing, in-network computing, and FAIR data ecosystems, with applications in industrial and cyber-physical systems. He explores methods for efficient data aggregation, stability detection, and cross-organizational data sharing, often leveraging technologies like WebAssembly and knowledge graphs. His work integrates distributed systems principles with database technologies to enable scalable and secure data processing in industrial environments. The recent publications of Liam Tirpitz reflect a strong focus on industrial data management, with trends toward in-network computing, edge-based stream processing, and FAIR principles. His work spans both theoretical architectures (e.g., GALOIS) and applied implementations in domains like manufacturing and health data, demonstrating interdisciplinary collaboration and real-world impact. Liam Tirpitz is actively involved in teaching and academic supervision. He has taught courses such as Data Ecosystems Lab and Implementation of Databases, and is currently supervising multiple thesis projects on topics including WebAssembly-based query functions, anomaly detection, and hybrid stream processing. He participates in research projects like StreamFröst and the Cluster of Excellence Internet of Production, contributing to the development of digital infrastructure for sustainable industrial systems. He is based at the Chair of Computer Science i5, a leading research group in information systems and databases at RWTH Aachen, which supports his work in data-intensive applications and distributed computing environments.