جست‌وجوی استاد و پژوهشگر

• Socio-Technical Information Systems
• Digital Transformation
• Human-Computer Interaction
• +۷ مورد دیگر

Prof. Dr. Henner Gimpel holds the Chair of Digital Management at the University of Hohenheim. He serves as Vice Dean for Research and Knowledge Transfer at the Faculty of Business, Economics and Social Sciences, and heads the Digital Business Management program. He also holds leadership roles at the Fraunhofer FIT's Business Informatics branch and the FIM Research Institute for Information Management, while directing the Digital Leadership Academy. University of Hohenheim - Chair of Digital Management Fraunhofer FIT - Business Informatics Branch FIM Research Institute - Director Digital Leadership Academy - Academic Director Education : Industrial Engineering (Business Administration & Applied Computer Science), Karlsruhe Institute of Technology (KIT) Doctorate in Business Informatics, Behavioral Economics, and Psychology, KIT Research Interests : His work focuses on socio-technical information systems where people, information, and digital technologies interact. Key research areas include artificial/hybrid/collective intelligence, digital health, smart sustainability, digital ethics, and IT adoption. He employs empirical studies, experiments, and design-oriented approaches, with interdisciplinary connections to psychology, computer science, sociology, medicine, and ethics. Publication Trends : Recent articles (2023-2025) span topics like AI-augmented facilitation, digital stress analysis, healthcare collaboration systems, smart energy technologies, and generative AI competencies. They reflect methodological diversity across design science research, Kano analysis, action design research, and empirical experiments. Scientific Awards : Friedrich von Klinggräff Medal Deutsches Aktieninstitut University Competition First Prize Walter-Georg-Waffenschmidt Prize for Business Administration Fugger Prize for Science Multiple Teaching Awards Practical Engagement : Through five years as McKinsey & Company management consultant and collaborations with Allianz, BMW, IBM, Siemens, and university hospitals, he bridges academic research with industry applications in digital transformation. Academic Leadership : He contributes to editorial boards (Electronic Markets) and program committees (ICIS, ECIS, WI), while pioneering cognitive apprenticeship-based teaching methods. His work impacts digital workplace design, healthcare informatics, and sustainable business models.

Julia Hertelمشاهده پروفایل

پژوهشگر

• Augmented Reality
• Human-Computer Interaction
• Industrial Applications
• +۷ مورد دیگر

Julia Hertel is a Researcher at the Department of Informatics, University of Hamburg, specializing in Human-Computer Interaction (HCI) and Augmented Reality (AR) systems for industrial applications. Her work bridges AR technology with practical solutions in maritime navigation, hydrographic surveys, and healthcare virtual agents. Bachelor's (2016) and Master's (2020) in Computer Science and HCI from University of Hamburg Her research interests focus on Augmented Reality for industrial contexts , including perception challenges in AR environments, interaction design for immersive systems, and AR tool development for gas station maintenance and maritime navigation. She has contributed to projects like WizARd (2020-2023) and HafenplanZen (since 2022). Julia's publications span AR perception studies, no-code AR authoring tools, and neural rendering for VR. Key collaborations include work on medical virtual agents and geospatial data visualization. She is actively involved in the XR Lab at the University of Hamburg, exploring spatial user interaction and industrial AR applications.

Stephan Leibleمشاهده پروفایل

پژوهشگر

• Employee-driven (Digital) Innovation
• Intrapreneurship
• Employee Engagement
• +۲ مورد دیگر

Stephan Leible is a Researcher at the Department of Computer Science , University of Hamburg (MIN Faculty). Holding both M.Eng. in Business Engineering and an MBA, he focuses on employee-driven digital innovation, intrapreneurship, and leveraging generative AI for organizational transformation. Research Interests : Employee-driven Innovation, Intrapreneurship, Generative AI Governance, Design & Data Thinking, IT Innovation Management Contact : stephan.leible@uni-hamburg.de | Room 117C, Vogt-Kölln-Straße 30, Hamburg His work bridges citizen development with public sector innovation, emphasizing value co-creation, real-time analytics, and ethical AI implementation. Current studies explore: Generative AI adoption patterns and limitations Interpretable machine learning for urban mobility Participatory frameworks for smart city futures Methodologies for continuous improvement in conversational agents

Rainer Schnellمشاهده پروفایل

استاد

• Survey Methodology
• Privacy-Preserving Record Linkage
• Health Data Analysis
• +۲ مورد دیگر

Prof. Dr. Rainer Schnell is a Professor at the Institute of Sociology, University of Duisburg-Essen. His research focuses on advanced survey methodologies, privacy-preserving data linkage techniques, and statistical analysis of health and social data. He leads the Chair of Empirical Social Research and contributes to interdisciplinary studies combining sociology, computer science, and public health. Research interests span: Survey Methodology : Innovations in web surveys, non-response analysis, and data quality assurance Data Privacy : Cryptographic techniques for secure record linkage and vulnerability assessments Health Analytics : Vaccination behavior studies, health data governance, and pandemic-related research Recent publications demonstrate a strong emphasis on: Privacy-enhancing technologies for sensitive data integration Methodological critiques of survey and data linkage practices COVID-19-related behavioral research using large-scale population data

• Optimization under uncertainty
• Robust optimization
• Scenario generation
• +۸ مورد دیگر

Christina Busing is a Full Professor for Combinatorial Optimization at RWTH Aachen University, a position she has held since 2021. Previously, from 2016 to 2021, she served as a Junior Professor for Robust Planning in Medical Care at the same institution. She leads the Teaching and Research Group on Combinatorial Optimization, contributing significantly to the academic community through her research and teaching activities. Her educational background includes Mathematics studies at WWU Münster, Universidad Comlutense de Madrid, and the Technical University of Berlin. Her doctoral work was completed under the supervision of Prof. Möhring, focusing on Recoverable Robustness in Combinatorial Optimization. She has also held postdoctoral positions at institutions in Aachen, Lancaster, and Vienna. Professor Busing's research spans multiple areas of optimization theory and application. Her work focuses on optimization under uncertainty, robust optimization, scenario generation, combinatorial optimization, complexity theory, optimality criteria, and both exact and heuristic algorithms. She has made significant contributions to applying these theoretical frameworks to practical problems in healthcare, energy systems, and transportation networks. Her interdisciplinary approach bridges theoretical computer science with real-world operational challenges. Her extensive publication record demonstrates a consistent focus on robust combinatorial optimization, with recent work emphasizing applications in healthcare systems, particularly in patient-to-room assignment, primary care scheduling, and pharmacy services. She has developed novel methodologies for handling uncertainty in optimization problems, including recycling valid inequalities and designing consistent decision frameworks for two-stage optimization problems. RWTH Aachen Brigitte Gilles Award for contributions to the advancement of women in science (2022) RWTH Aachen FAMOS Award for excellent family-friendly leadership (2019) RWTH Aachen university-wide Best Teaching Award (2018) TU Berlin Best Diploma Excellence Award in Mathematics (2008) German National Scholarship (Cusanuswerk) (2003-2007) Professor Busing serves on multiple program committees including EURO (2019), INOC (2018), and ESA (2017). She is also involved with the UnRAVeL Graduate College since 2018. Her teaching portfolio includes courses on Graph and Network Optimization, Mathematical Heuristics for Discrete Optimization Problems, and Combinatorial Optimization. She has developed problem-based learning approaches for heuristic methods in decision problems across mathematics, computer science, and industrial engineering. Her research group actively collaborates on interdisciplinary projects addressing operational issues in healthcare, energy systems, and transportation networks, developing mathematically optimized solutions for complex real-world challenges.

Camille Casteraمشاهده پروفایل

دانشیار

• Optimization Algorithms
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Camille Castera is an Associate Professor in optimization and machine learning at the Mathematical Institute of the University of Bordeaux (IMB). Her academic journey includes a postdoctoral position at the University of Tübingen (2022-2024) working on the TRINOM-DS project under Peter Ochs and Jalal Fadili, and a PhD at the IRIT laboratory in Toulouse supervised by Cédric Févotte, Edouard Pauwels, and Jérôme Bolte as part of the FACTORY project. Her research focuses on optimization algorithms for machine learning, particularly second-order methods with applications to deep learning, and the emerging field of learning optimization algorithms. Her work spans continuous optimization (convex & non-convex, non-smooth, stochastic), learning to optimize, and optimization for deep learning with automatic hyper-parameter tuning. Her recent publications demonstrate significant contributions to the field of optimization algorithms, particularly in developing novel approaches that bridge classical optimization techniques with learning-based methods. Her research shows a clear trajectory toward creating more adaptive and versatile optimization algorithms that can generalize beyond their training settings. At the University of Bordeaux, she teaches advanced courses including Large-scale optimization (Master 2), Introduction to deep learning (Master 2), Convex optimization (Master 1), and Image processing (Licence 3), while also supervising student projects on image processing and machine learning. Her technical work is supported by substantial implementation efforts, with publicly available code repositories demonstrating practical applications of her theoretical contributions to optimization algorithms.

Tomasz Rozmyslowiczمشاهده پروفایل

پژوهشگر

• Translation Studies
• Machine Translation
• Cultural Studies
• +۳ مورد دیگر

Dr. Tomasz Rozmyslowicz is a researcher specializing in translation studies, focusing on human differentiation within multilingual communication contexts. His work examines the intersection of translation practices, politics, and social categorizations like 'Migrant,' particularly in state-managed language mediation systems in Germany. Researches state-led translation management in institutions (e.g., authorities, schools) Investigates shifts in translation practices under frameworks of diversity and inclusion Explores historical translation dynamics during the Third Reich and academic exile His publications highlight the role of machine translation in redefining human-machine boundaries and the historical evolution of translation theory. He actively contributes to methodological advances in translation history through archival digitization and open-access resources.

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۷ مورد دیگر

Prof. Dr.-Ing. Joachim Denzler is a Professor leading the Chair for Digital Image Processing (Lehrstuhl für Digitale Bildverarbeitung) at Friedrich-Schiller-Universität Jena. His research spans computer vision, machine learning, medical imaging, and remote sensing applications across diverse domains including biomedical analysis, environmental monitoring, and facial recognition systems. His research interests focus on explainable AI, causal discovery, physics-informed neural networks, and bias mitigation in deep learning models. He has made significant contributions to fine-grained classification, concept activation vectors, and privacy-preserving techniques in facial recognition. His work often bridges theoretical computer vision with practical applications in medical diagnostics, environmental science, and infrastructure monitoring. His recent publications demonstrate a strong trend toward causal discovery methods, physics-informed neural networks, and addressing bias in machine learning systems. His work spans both theoretical advancements in model interpretability and practical applications in medical imaging, environmental monitoring, and infrastructure safety. The interdisciplinary nature of his research is evident from collaborations with ecologists, neuroscientists, and civil engineers. Best Paper Award at International Conference on Pattern Recognition (ICPR) 2024 Prof. Denzler actively mentors numerous PhD students and postdoctoral researchers, with frequent co-authors including Maha Shadaydeh, Niklas Penzel, Gideon Stein, and Tim Büchner appearing as first authors on significant publications. His laboratory has secured research funding across multiple domains including medical imaging, environmental monitoring, and AI safety. His work on CausalRivers represents a major contribution to benchmarking causal discovery methods with real-world time series data. The Chair for Digital Image Processing maintains strong industry and clinical partnerships, particularly in medical imaging applications for facial palsy analysis and neonatal care. Prof. Denzler's team has developed novel techniques for dam deformation monitoring using remote sensing data and created advanced methods for analyzing plant diversity effects on ecosystem functioning.

• Affective Computing
• Explainable AI
• Data Privacy
• +۴ مورد دیگر

Dr. Olya Hakobyan is a Researcher at the University of Bielefeld, working in the Faculty of Engineering within the Human-Centered Artificial Intelligence Group at the Center for Cognitive Interaction Technology (CITEC). She has been with the university since June 2021 as a research associate in the Multimodal Behavior Processing lab. Dr. Hakobyan's educational background includes: PhD in Computational Neuroscience (2016-2020) from the Institute for Neural Computation at Ruhr University Bochum Master's degree in Cognitive Science (2014-2016) from Ruhr University Bochum Bachelor's degree in Linguistics (2010-2014) from Yerevan Brusov State University of Languages and Social Sciences Her research focuses on the intersection of artificial intelligence, cognitive science, and human-computer interaction. Dr. Hakobyan specializes in affective computing, explainable AI systems, and the development of privacy-preserving data donation platforms. Her work on the Dona platform represents a significant contribution to ethical data collection methods for social interaction analysis. She investigates how AI systems can interpret human emotions through audio and visual channels while maintaining transparency in decision-making processes. Her research also extends to cognitive modeling, particularly in understanding memory processes and recognition mechanisms. Dr. Hakobyan's publication record demonstrates a strong trajectory in developing methods for analyzing social interactions through digital footprints while addressing critical privacy concerns. Her work bridges technical innovation with ethical considerations in data collection. Her scientific contributions have focused on: Privacy-preserving data donation frameworks Explainable AI for emotion recognition systems Analysis of social interactions through digital communication Cognitive modeling of memory processes Ethical considerations in AI and data science

• Computational Soft Matter
• Density Functional Theory
• Statistical Physics
• +۵ مورد دیگر

Professor Martin Oettel holds a faculty position at the University of Tübingen within the Department of Physics, Faculty of Science, where he leads the Computational Nanoscience research group at the Institute of Applied Physics. His academic address is at Auf der Morgenstelle 10, 72076 Tübingen, Germany. Since 2013, he has co-organized the annual Density Functional Days workshops alongside Joseph Brader (University of Fribourg) and Roland Roth (University of Tübingen), establishing an important forum for researchers in classical density functional theory. Professor Oettel's research focuses on computational soft matter physics , with particular expertise in density functional theory applications to colloidal systems, thin film growth, and phase transitions. His work spans both fundamental theoretical developments and applied materials science problems, especially in organic electronics and nanoscale systems. Key areas include the study of hard sphere systems, monolayers of anisotropic particles on substrates, phase diagrams for complex colloidal mixtures, and the development of dynamic density functional theory approaches. His research often involves sophisticated computational modeling combined with close collaboration with experimental groups, such as Prof. Frank Schreiber's experimental group at Tübingen. His publication record demonstrates consistent contributions to the field since 2006, with recent work (2016-2018) focusing on thin film growth with anisotropic particles, monolayer systems of hard rods, phase transitions in colloidal mixtures, and advanced diffusion phenomena in confined systems. The research shows a clear trajectory from fundamental theoretical developments toward applications in materials science and nanotechnology. Professor Oettel actively supervises doctoral students, as evidenced by the 2023 job posting for a PhD position in modeling thin film growth within a DAAD-CAPES project involving collaboration with Prof. Frank Schreiber (Tübingen) and Prof. Fabio Reis (Federal Fluminense University, Brazil). This position required work on both simulations and dynamic density functional theory, with an extended research stay in Brazil. He maintains the Computational Soft Matter and Nano-Science Chair (Lehrstuhl für Computational Soft Matter and Nano-Science) at the University of Tübingen, where his research group investigates the physics of soft condensed matter systems through computational approaches. The group's work bridges theoretical physics with practical applications in materials science, particularly in organic electronics and nanoscale systems.

Javier Villalba-Diezمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Hoshin Kanri
• +۴ مورد دیگر

Professor Javier Villalba-Diez serves at the Faculty of Business of Heilbronn University of Applied Sciences, Germany, where he integrates artificial intelligence with lean management principles in industrial and business contexts. His international collaborations include a cooperative doctoral program with Technical University of Madrid and Erasmus exchanges with Universidad Politécnica de Madrid. Dr. Villalba-Diez earned dual engineering degrees: Mechanical Engineering from Technische Universität München and Industrial Engineering from Universidad Politécnica de Madrid (2003). His PhD in Engineering, Economics and Organizational Innovation (2016) from Universidad Politécnica de Madrid received the institution's best doctoral thesis award. His research spans Artificial Intelligence (particularly Deep Learning applications), Hoshin Kanri strategic planning, Business Intelligence , and Lean Manufacturing . He pioneers sensor-based methodologies for organizational design, using EEG and industrial IoT to analyze problem-solving patterns and network resilience. His work bridges theoretical models with practical implementations across German, American, Japanese, and Spanish manufacturing facilities. Recent publications demonstrate a clear trajectory toward Industry 4.0 integration , with 60% of his 2019-2020 work focusing on deep learning applications in quality control, sensor networks, and cyber-physical systems. The journal Sensors (MDPI) serves as his primary publication venue, reflecting his emphasis on data-driven industrial analytics. His recognition includes: Prize for best doctoral thesis by Universidad Politécnica de Madrid (2016) As Guest Editor for Sensors and reviewer for journals like Sustainability and Journal of Manufacturing Systems , he shapes discourse in industrial AI. His doctoral supervision with Madrid focuses on AI-driven strategic organizational design, while industry collaborations with manufacturing facilities worldwide translate research into operational frameworks. He maintains active roles in curriculum development for Industry 4.0 education through the PROFH4 digital initiative. Dr. Villalba-Diez operates within international research networks, leveraging his multilingual capabilities (German, English, Spanish) to facilitate transnational projects. His work with Neo4j for Hoshin Kanri visualization exemplifies his approach to making complex organizational networks actionable for industry leaders.

Marcus Fritzeمشاهده پروفایل

پژوهشگر

• Bat Conservation
• Ecosystem Services
• Population Ecology
• +۶ مورد دیگر

Dr. Marcus Fritze is a researcher in Applied Zoology and Nature Conservation at the University of Greifswald, with a dual affiliation since 2023 at the Competence Center for Bat Conservation Saxony-Anhalt within the South Harz Karst Landscape Biosphere Reserve. His work bridges scientific research with practical conservation applications, focusing specifically on bat ecology and conservation. Dr. Fritze's research spans multiple critical areas in bat conservation. He investigates population trends and demographic parameters through BfN-funded projects 'Threatened Data for Threatened Species' and BATTREND. A major output from these initiatives is BATLAS, an online platform for analyzing bat population data across Germany. His work addresses the 'green-green dilemma' between renewable energy expansion and wildlife conservation, particularly examining wind energy impacts on bats and the ecological consequences of light pollution. His physiological research examines hibernation (winter torpor) mechanisms in bats and how their immune systems function during this energy-conserving state, with special focus on white-nose disease. Through the F.U.N. initiative ('Forschung, Umweltbildung und praktischen Naturschutz'), he integrates research, environmental education, and practical conservation to support biodiversity. Current projects include 'Bats and Biosphere' (human-wildlife interactions) and 'Bats and Ponds' (role of water bodies in ecosystems). Analysis of Dr. Fritze's publication record reveals an interdisciplinary approach addressing conservation challenges at the intersection of ecology, immunology, policy, and technology. His work demonstrates particular expertise in developing evidence-based conservation strategies that effectively bridge scientific research with practical implementation. Dr. Fritze actively supervises numerous Master's and Bachelor's students on bat conservation topics, often in collaboration with other institutions including FH Bernburg, HTW Dresden, and TU Berlin. His current supervisees are investigating ecosystem services provided by bats in fruit orchards, urban pond habitats for bats, wind turbine mitigation measures, and spring activities of bats on orchard meadows. His collaborative approach extends internationally, such as with Universidad Autónoma Metropolitana in Colombia on jaguar conservation perceptions. Through his leadership in the Competence Center for Bat Conservation Saxony-Anhalt and various research projects, Dr. Fritze has developed effective frameworks for collaboration between scientists, amateur researchers, and volunteer conservationists. His work exemplifies how rigorous scientific research can directly inform and improve practical conservation outcomes in complex human-wildlife landscapes.

Björn Möllerمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Björn Möller, a researcher at the Department of Signal Processing and Machine Learning within the Faculty of Electrical Engineering, Information Technology, and Physics at Technical University of Braunschweig, specializes in machine learning and computer vision applications. His academic journey includes a diploma in Business Information Systems from TU Dresden (2012–2019), with a focus on Business Intelligence. Research Interests His work bridges deep learning with practical applications in two distinct domains: Advancing microscopy techniques through low-resolution data-driven super-resolution models Optimizing viticulture operations via object detection and yield prognosis systems His publications demonstrate expertise in algorithm development for both scientific imaging and agricultural technology. Publication Trends Research spans atomic-scale imaging (2024, 2023) and agricultural AI (2019, 2021). Notably, he explores how transformer architectures and super-resolution methods can solve domain-specific challenges in microscopy and vineyard management. Contact Information Email: bjoern.moeller@tu-braunschweig.de Location: Schleinitzstraße 22 (Room 302), Braunschweig Phone: +49 (0) 531 391 - 2446

Jonathan L. Habifمشاهده پروفایل

استادیار

• Quantum Communication
• Optical Communications
• Nano-Photonics
• +۲ مورد دیگر

Jonathan L. Habif serves as a Research Assistant Professor in the Ming Hsieh Department of Electrical and Computer Engineering at the University of Southern California and holds a Research Lead position at the Information Sciences Institute (ISI). His work centers on fundamental limits of information extraction from physical signals, with primary focus on quantum and classical optical systems. His academic background includes: B.A. in Physics from Colgate University M.S. and Ph.D. in Electrical and Computer Engineering from the University of Rochester Postdoctoral research in Physics at MIT's Research Laboratory for Electronics Dr. Habif's research integrates quantum information science with photonic engineering, specializing in photon-starved communication scenarios, quantum-secured optical networks across free-space and fiber channels, and nano-photonic device development. He directs USC's Laboratory for Quantum-Limited Information (QLIlab) in Waltham, MA, which pioneers experimental demonstrations of information-theoretic boundaries in signal processing. The lab's work bridges theoretical limits with practical implementations in secure communications and low-light imaging systems. His institutional affiliation spans USC's Ming Hsieh Department and ISI's Waltham facility at 890 Winter Street, Suite 115, where he maintains primary operations for quantum information research initiatives.

Torsten Sattlerمشاهده پروفایل

پژوهشگر

• Computer Vision
• Image-based Localization
• 3D Reconstruction
• +۳ مورد دیگر

Dr. Torsten Sattler is a computer vision researcher at RWTH Aachen University, Germany, specializing in image-based localization and 3D scene reconstruction. His work focuses on developing efficient algorithms for camera pose estimation relative to large 3D models, with significant contributions to mobile localization systems and scalable reconstruction techniques. His primary research interests include: Image-based localization and pose estimation Large-scale 3D scene reconstruction Structure-from-Motion techniques Efficient correspondence search algorithms Mobile vision applications Point cloud processing and rendering Dr. Sattler's publication record shows a clear progression from fundamental algorithm improvements to practical systems for real-world applications. His research demonstrates particular expertise in optimizing RANSAC implementations, developing direct 2D-to-3D matching techniques, and creating memory-efficient solutions for mobile devices. The trend in his work moves toward increasingly complex systems that address practical challenges in urban-scale localization and reconstruction. Award: Best Paper Award at the ICCV Workshop on Big Data in 3D Computer Vision (2013) Dr. Sattler has maintained strong collaborations with researchers including Bastian Leibe and Leif Kobbelt. His work bridges theoretical computer vision with practical applications in augmented reality, robotics, and mobile navigation systems, often providing publicly available source code and project pages to support reproducibility and further research.