جست‌وجوی استاد و پژوهشگر

Sven Buchholzمشاهده پروفایل

استاد

• Applied Computer Science
• Data Management
• Data Mining
• +۶ مورد دیگر

Prof. Dr.-Ing. Sven Buchholz serves as a Professor in the Department of Computer Science and Media at Brandenburg University of Technology in Brandenburg an der Havel, Germany. His office is located in Building C, Room C.2.18 at Magdeburger Straße 50, with contact information including telephone +49 3381 355-482 and email sven.buchholz@th-brandenburg.de. Specializing in Applied Computer Science , Prof. Buchholz focuses on data management and data mining with a distinctive research trajectory in geometric algebra applications. His scholarly work demonstrates expertise across multiple domains including neural network architectures using Clifford algebra, solving complex partial differential equations, protein structure prediction, and computer vision systems. The evolution of his research shows progression from foundational theoretical work on Clifford neurons in the early 2000s to increasingly interdisciplinary applications in recent years. Analysis of his publication history reveals a significant research focus spanning over 15 years, with a notable resurgence of activity in 2024. His recent work demonstrates sophisticated applications of geometric algebra to solve challenging problems across physics (Maxwell's equations, Navier-Stokes equations), molecular biology (protein structure prediction), and robotics (camera pose estimation). This interdisciplinary approach connects mathematical theory with practical implementations in scientific computing and artificial intelligence. Prof. Buchholz maintains an active research profile with multiple high-impact publications in 2024, indicating ongoing contributions to the advancement of geometric algebra applications in computational science. His work bridges theoretical mathematics with practical machine learning implementations, contributing to both academic knowledge and potential real-world applications in scientific computing and data analysis.

Olga Levinaمشاهده پروفایل

استاد

• Ethical Issues of Digitalization
• Business Process Management
• Low-Code Development
• +۲ مورد دیگر

Prof. Dr. Olga Levina serves as Professor of Business Informatics at Brandenburg University of Technology (BTU), Department of Economics, where she bridges technical systems design with ethical governance in digital transformation. Her institutional leadership includes contributions to DIN working groups on Responsible AI and UNESCO's "Artificial Intelligence for more gender equality and against discrimination" guideline. Her educational background includes: Industrial Engineering with technical specialization in Information and Communication Systems (Technical University of Berlin and LUISS Rome) Doctorate in Network Analysis (TU Berlin) Levina's research critically examines ethical digitalization , business process management , and low-code development through participatory frameworks. She investigates how socially-aware information systems can mitigate digital inequality while advancing sustainability. Current projects analyze dark patterns in consent interfaces and AI's impact on journalistic values, emphasizing transparency and stakeholder inclusion as foundational principles. Her 15 most recent publications (2021-2025) reveal three dominant trajectories: (1) operationalizing ethics in software design through assessment tools and pipelines, (2) analyzing low-code platforms' organizational impact, and (3) exposing biases in algorithmic systems via empirical studies of dark patterns and recommender systems. This work consistently connects technical implementation with societal consequences. Levina actively supervises Master's theses through structured processes requiring English proficiency and research proposals. Her Ethical and Legal Aspects of Digitalization course generates publishable student research on topics like cookie consent dark patterns and AI sentiment analysis. She leads the InnoWest project on sustainable IT development while maintaining roles as journal/conference reviewer and DIN working group contributor. Her research group produces empirical studies through student projects examining digital ethics in real-world contexts, including cross-national analyses of cookie banners and values in messenger apps. Current work explores "naming as framing" in sustainability discourse and machine learning system auditing.

• Computational Mechanics
• Material Science
• Biomechanics
• +۵ مورد دیگر

Prof. Bernd Markert serves as Professor and Head of the Institute of General Mechanics at RWTH Aachen University's Faculty of Mechanical Engineering. He concurrently holds critical university-wide roles including Rector's Representative for Alumni and Rectorate Mandate for Mexico Cooperation, based in Aachen's Faculty of Mechanical Engineering building. His research spans computational mechanics with emphasis on phase-field modeling, molecular dynamics, and machine learning integration. Key focus areas include biodegradable magnesium alloys for biomedical implants, multiscale composite material behavior, and AI-enhanced structural failure prediction. Recent work bridges physics-based simulations with neural networks for constitutive modeling and corrosion prediction. Analysis of his 2022-2025 publications reveals accelerating convergence of mechanics and artificial intelligence, particularly in physics-informed neural networks for crystal plasticity and phase-field frameworks. His team demonstrates consistent innovation in modeling porous media, network materials, and biomechanical systems while maintaining strong industry-relevant applications in additive manufacturing and biomedical engineering. As Institute Head, Markert leads the Chair and Institute of General Mechanics (IAM), directing research on fundamental mechanics principles applied to advanced materials and structural systems through computational and experimental methodologies.

Gregor Wiedemannمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Text Mining
• Computational Social Science
• +۵ مورد دیگر

Dr. Gregor Wiedemann serves as a Senior Researcher in Computational Social Science at the Leibniz Institute for Media Research (Hans Bredow Institute) since September 2020, co-heading the Media Research Methods Lab (MRML) with Sascha Hölig. His work bridges computer science and social sciences through methodological innovation in empirical media research. Wiedemann holds a doctorate in computer science from Leipzig University (2016), where his dissertation focused on automating discourse analysis using text mining and machine learning. His educational background combines political science and computer science studies at Leipzig University and the University of Miami, followed by postdoctoral work in Language Technology at the University of Hamburg under Prof. Chris Biemann. His research centers on natural language processing and text mining applications for social and media analysis, with significant contributions in hate speech detection, argument mining, and cross-platform misinformation tracking. Recent work demonstrates a strategic shift toward building research infrastructures for sensitive data handling, including the Community Data Trust model for extremism research and the Social Media Observatory open-science platform. His methodology development specifically targets unsupervised information extraction from large document corpora to support investigative journalism and social science inquiry. Wiedemann's publication trends reveal deepening specialization in computational infrastructure development, with 7 of his 11 most recent works (2024-2025) focusing on data trust frameworks, cross-platform methodologies, and AI-driven analysis systems. His projects consistently intersect computational linguistics with pressing social issues including election integrity, climate discourse, and child safety in digital spaces. He has secured major funding through the German Research Foundation (DFG) for the FAME project on argument mining and evaluation, and leads collaborative initiatives including NOTORIOUS (mis- and disinformation tracking) and ComAI (communicative AI impact studies). His work with state media authorities on family influencing content demonstrates applied policy relevance. As co-director of the Media Research Methods Lab, Wiedemann oversees a dynamic team developing cutting-edge computational approaches for media analysis. The lab functions as an interdisciplinary hub connecting computer scientists with social researchers, with current projects spanning TikTok political campaigning analysis, right-wing extremism data infrastructure, and ethical AI applications in public discourse monitoring.

• Management Accounting
• Financial Controlling
• Sustainability Accounting
• +۴ مورد دیگر

Christoph Binder is a Professor of Management Accounting and Controlling at ESB Business School, Reutlingen University. He serves as Head of Career Center & Alumni, Programme Director for MA European Management Studies, and Liaison Officer for the German National Academic Foundation. Previously, he served as Dean of ESB Business School from 2019-2024. German-French dual diploma (IPBS) from ESB Reutlingen (1996-2000) Semester at University of Texas at Austin (1997) Management consultant at McKinsey & Company (2000-2008) Doctorate in Management Accounting & Control at European Business School (2004-2006) Professor at Reutlingen University since 2008 Binder's research focuses on management accounting systems with particular emphasis on sustainability controlling, carbon accounting, and geopolitical risk management. His work bridges traditional accounting practices with emerging challenges in risk assessment and sustainable business operations. He has developed frameworks for structured risk identification in SMEs and value-oriented performance measurement systems. His publication record shows consistent contributions to controlling literature since 2014, with increasing focus on geopolitical risks (2023-2025) and sustainability accounting. The articles demonstrate evolution from foundational controlling concepts toward contemporary challenges in risk management and sustainable business practices, reflecting growing industry relevance of these topics. Member of Board of Trustees, International Controller Association Member of Advisory Board, Dieter von Holtzbrinck Stiftung Binder maintains active industry engagement through advisory roles while leading academic programs. His consulting background informs practical applications of controlling frameworks, particularly in energy, logistics, and telecommunications sectors. Current initiatives focus on integrating sustainability metrics into traditional controlling systems and developing risk management tools for volatile geopolitical environments.

• Artificial Intelligence
• Building Information Modeling (BIM)
• Machine Learning
• +۳ مورد دیگر

Changyu Du is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich. His work focuses on AI-driven automation in BIM workflows, including command recommendation systems and conflict resolution. Research Interests: Application of artificial intelligence and large language models to BIM authoring tools Transformer-based architectures for design pattern recognition Reinforcement learning for automated conflict resolution Multi-modal fusion techniques in 3D point cloud processing Human-machine interaction in engineering software Recent Publication Trends: His work spans AI integration in BIM (2025), neural network architectures for semantic segmentation (2021-2022), and collaborative human-AI systems (2024). Key technologies include transformers, graph neural networks, and reinforcement learning. Teaching: Active participant in the SoftwareLab course, contributing to hands-on computational engineering education.

• Artificial Intelligence
• Building Information Modelling & Processing
• Digital Twinning
• +۳ مورد دیگر

Florian Noichl is a Group Lead in Spatial Computing at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich . His work focuses on methods for creating digital twins of industrial environments, particularly car manufacturing plants, and includes collaborative projects with Siemens . He was a Visiting Researcher at the Department of Geomatics Engineering at the University of Calgary, Canada (May–August 2023). Research interests include Spatial computing Digital twinning Point cloud processing Artificial intelligence in construction Scan planning optimization GeoAI for retrofitting Recent publications emphasize automation in point cloud segmentation, material passport creation for steel structures, semantic facade analysis, and energy efficiency estimation using vision-language models. His work often integrates BIM with laser scanning and AI . Teaching includes Bau- und Umweltinformatik 1 (WS22/23, WS23/24) and Softwarelab . He supervises theses on topics like UAV mission planning Point cloud enrichment Image-based localization CAD model matching BIM design reviews Review activities cover journals such as Automation in Construction , IEEE Sensors Journal , and Remote Sensing . He is listed on OrcID : 0000-0001-6553-9806 Google Scholar ResearchGate Labs associated with include the BIM-Lab and Robotic Fabrication Lab . His work intersects digital construction with industrial AI applications.

• Construction Process Management
• Digital Twin
• Building Information Modeling (BIM)
• +۳ مورد دیگر

Isabella Deininger, M.Sc., is a Researcher at the Department of Construction Process Management at the Technical University of Munich. Her work focuses on planning time prediction in construction projects, digital modeling for decarbonization, and integrating technologies like BIM and Digital Twins in industrial settings. Academic Education: Master's and Bachelor's in Civil Engineering from TU Munich, with an international semester at Newcastle University (UK) Research interests include: Construction process optimization and planning time prediction Digital Twin and BIM integration Sustainable construction practices Machine learning applications in construction The 2023 publication connects building, production, and energy models to address industrial site decarbonization, reflecting broader interests in Civil Engineering, Sustainability, and Energy Modeling. Subfields span Decarbonization, Integrated Project Delivery, and Digital Twin applications.

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +۳ مورد دیگر

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.

Max Greßمشاهده پروفایل

استاد

• Civil Engineering
• Structural Engineering
• Construction Management
• +۲ مورد دیگر

Professor Max Greß is a faculty member in the Civil Engineering department at the Architecture and Civil Engineering faculty of Technical University of Applied Sciences Augsburg (THA). He teaches in the Master of Engineering in Civil Engineering program, contributing to the academic and practical training of future civil engineers. His research interests span multiple areas of civil engineering including structural engineering, sustainable construction methods, Building Information Modeling (BIM), construction process optimization, and the digital transformation of the construction industry. Professor Greß focuses on practical applications of engineering principles in real-world construction contexts, with increasing emphasis on climate-resilient infrastructure and sustainable building practices. The recent publication record shows consistent output in structural analysis, sustainable materials, digital construction methods, and infrastructure resilience. His work demonstrates a progression from traditional structural engineering topics toward more contemporary challenges including digitalization of construction processes and sustainability considerations in civil engineering projects. As a professor at a German University of Applied Sciences, Professor Greß emphasizes practical, industry-relevant education, integrating real-world construction case studies and industry software tools into his teaching. His approach ensures students gain applicable skills for the construction industry through a combination of theoretical knowledge and practical experience. Professor Greß supervises Master's students in the Civil Engineering program, guiding research on topics aligned with current industry challenges and technological developments. His mentorship focuses on preparing students for successful careers in the evolving construction sector through practical research projects and industry connections. The Architecture and Civil Engineering faculty at THA provides a collaborative environment where civil engineering intersects with architectural design, creating opportunities for interdisciplinary projects that address complex built environment challenges from multiple perspectives.

Jürgen Wilhelm Böseمشاهده پروفایل

استاد

• Logistics
• Supply Chain Management
• Port Terminal Planning
• +۵ مورد دیگر

Professor Jürgen Wilhelm Böse is a Professor of Logistics at the Faculty of Commerce and Social Work, Ostfalia University of Applied Sciences, Suderburg Campus. He has held this position since 2018 and is responsible for teaching and research in logistics, particularly focusing on port terminal planning and operations. His office is located in Building C, Room 106.2, Suderburg. Professor Böse's academic journey began with studies in Industrial Engineering at the Technical University of Braunschweig (1991-1997), followed by research assistant work at the same institution (1997-2002). He then gained industry experience as Branch Manager at LOGIS.NET (2003-2005) and Senior Consultant at HPC Hamburg Port Consulting (2005-2010), before returning to academia as Senior Engineer at the Institute for Maritime Logistics, Hamburg University of Technology (2010-2018). His research focuses on planning complex logistics systems, particularly seaport container terminals. His work encompasses developing initial system solutions based on business experience and spreadsheet calculations, using discrete event-oriented simulation for validation and optimization, and coupling simulation tools with Artificial Intelligence for automated system optimization. This approach typically yields qualitatively better results at significantly lower costs compared to manual expert use of these tools. His research interests include: Planning of complex logistics systems, especially seaport container terminals Discrete event-oriented simulation for logistics optimization Integration of production and logistics systems in value creation networks Artificial Intelligence applications in logistics and port operations Truck arrival forecasting and resource planning at logistics nodes Sustainable port operations and green terminal planning Professor Böse has made significant contributions to the field through his publications, most notably as editor of the 'Handbook of Terminal Planning' (Springer, 2nd edition 2020). His work spans numerous journal articles, conference papers, and research project reports focused on port logistics, terminal planning, and simulation applications. He has received research funding for projects including: Lkw-Wartezeitprognose für logistische Knoten (Truck Wait Time Forecasting for Logistics Nodes) IMOTRIS (Intermodal Transport Routing Information System) ISETEC II (Location Systems in Port Handling) NETRALOG (Bio-analog algorithms for horizontal transport control) As an educator, Professor Böse teaches regular courses in Project Management, Supply Chain Management, Transport Management, and Warehousing. He also offers specialized courses on logistics simulation and quantitative methods. In addition to his teaching duties, he serves in several administrative roles including as Research Commissioner and Internationalization Commissioner for Faculty H.

Udo Triltschمشاهده پروفایل

استاد

• Manufacturing Metrology
• Information Technology in Production
• Digital Twin
• +۴ مورد دیگر

Prof. Dr.-Ing. Udo Triltsch serves as Dean of the Faculty of Mechanical Engineering at Ostfalia University of Applied Sciences, where he leads the Institute IPT (Institute for Production Technology and Management). His academic profile combines administrative leadership with active teaching in Manufacturing Metrology, Digital Production, and Project Management across bachelor's and master's programs in Mechanical Engineering disciplines. Contact details include Building H, Room 115b, telephone +49 5331 939-45620, and email u.triltsch@ostfalia.de, with office hours arranged via Stud.IP. Research focuses on the digital transformation of manufacturing through Manufacturing Metrology and Information Technology in Production. Key investigations include Digital Twin implementation using physics-based simulations, Process Mining for shopfloor optimization, and Smart Factory applications demonstrated through practical case studies like commercial coffee machine monitoring. His work bridges theoretical frameworks with industrial implementation, particularly addressing challenges faced by small and medium enterprises in adopting Industry 4.0 technologies. Analysis of publications from 2017-2024 reveals evolving research trajectories: early work established foundational Industry 4.0 concepts (robotics, sensor integration), while recent output emphasizes educational frameworks for digital competencies (DiKom Project) and practical implementation strategies. The consistent focus on real-world case studies demonstrates a commitment to applicable research that solves tangible production challenges. Scientific awards are not documented in available sources. However, his active grant-funded projects like DiKom and continuous publication record indicate significant professional recognition within production engineering circles. As an academic supervisor, Prof. Triltsch maintains teaching responsibilities across multiple programs while leading research initiatives. His involvement in the DiKom Project suggests active grant management focused on digital transformation for SMEs. Prospective students would engage with practical Industry 4.0 applications through Institute IPT's industry-connected projects, though specific advising capacity isn't detailed. The Institute IPT serves as his primary research ecosystem, driving work in production technology and management. Current activities center on digital twin development, process mining integration, and competency frameworks for industrial digitalization, with strong emphasis on translating research into implementable solutions for manufacturing partners.

• Computational Neuroscience
• Neurophysics
• Dynamical Systems
• +۴ مورد دیگر

Prof. Dr. Klaus R. Pawelzik is a Professor at the University of Bremen's Institute for Theoretical Physics, where he leads the Theoretical Bio- and Neurophysics research group. His research bridges theoretical physics and neuroscience, with laboratories located in the Cognium building on the university campus. His primary research interests include: Computational models of neural dynamics and information processing Neurophysics and mechanisms of cortical computation Brain-computer interfaces and neuroprosthetic systems Dynamical systems approaches to causality and network analysis Biologically plausible learning algorithms for spiking neural networks Attention mechanisms and sensory processing in primate brains Recent publications (2015-2020) demonstrate strong focus on attention mechanisms in visual processing, causal inference methods for dynamical systems, hardware implementations for neuroprosthetics, and biologically inspired learning algorithms. The work consistently integrates mathematical rigor with experimental neuroscience, featuring collaborations with neurophysiology labs and engineering groups. Prof. Pawelzik leads an active research team developing both theoretical frameworks and experimental platforms for neuroscience research. The lab specializes in open-source neurotechnology solutions, including wireless implantable devices for electrocorticography and FPGA-based processing systems for real-time neural signal analysis.

• Business Application Systems
• ERP Systems
• Method Engineering
• +۵ مورد دیگر

Professor Florian Johannsen is a faculty member at Schmalkalden University of Applied Sciences in the Faculty of Business and Economics, where he leads research and teaching in Business Application Systems. His office is located in Building B, Room 0213, and he holds regular office hours on Thursdays from 2:00-3:00 PM, with appointments available during semester breaks. Professor Johannsen's research spans business application systems, method engineering, chatbots for internal business use, quality management for Smart Services & Industry 4.0, and social media analysis. His work often bridges theoretical frameworks with practical applications, particularly in developing modeling tools for business process improvement. He has led the WiWiNow Campus App project in collaboration with the University of Bremen, which received the Comenius EduMedia Seal in 2021 for its innovative approach to integrating technology into university teaching contexts. His recent publications (2021-2024) demonstrate consistent productivity with a focus on digital transformation in business processes, mobile learning applications, and the integration of sustainability principles into quality management systems. These works appear in top-tier information systems journals and conferences, showing his strong standing in the academic community. Best Associate Editor Award at WI 2022 Best Paper Award at DESRIST 2021 Comenius EduMedia Seal 2021 Best Associate Editor at ECIS 2019 Teaching Award from University of Regensburg (2017) Hilti Best Paper Award BDVB Award 2007 Professor Johannsen teaches advanced courses in business application systems, including specialized topics on ERP systems, service management, and social media analysis. His teaching reflects his research expertise, with courses designed to prepare students for the digital transformation challenges in modern business environments. His recent work on mobile learning applications demonstrates his commitment to innovative educational approaches that support student success, particularly in the transition to higher education.

Klim Zaporojetsمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Information Extraction
• Entity Linking
• +۳ مورد دیگر

Klim Zaporojets is a Marie Skłodowska-Curie Postdoctoral Fellow in the Department of Computer Science at Aarhus University, where he conducts research within the Data-Intensive Systems Group. His work bridges theoretical advancements and practical applications in natural language understanding. His research focuses on information extraction systems that connect textual content with structured knowledge bases. His methodology emphasizes leveraging external knowledge sources to enhance information extraction performance, particularly in document-level contexts where entities evolve over time. His work spans temporal relation extraction, entity linking, and biomedical text mining applications. The publication record reveals a strong focus on document-level information extraction with increasing emphasis on temporal aspects and knowledge integration. Recent work explores large language model applications for graph learning and calibration challenges in LLMs, showing evolution from traditional NLP tasks to cutting-edge foundation model research. His publications appear in top-tier venues including ACL, EMNLP, CIKM, and NeurIPS. His scientific recognition includes the prestigious Marie Skłodowska-Curie Postdoctoral Fellowship, supporting his research at Aarhus University. His work has produced several influential datasets including DWIE, TempEL, and BioDEX that have become benchmarks in document-level information extraction. Zaporojets maintains active collaborations with researchers at Ghent University (evidenced by his ugent.be email address) and has contributed to multiple interdisciplinary projects spanning computational linguistics, healthcare informatics, and knowledge representation. His technical contributions include open-source implementations of his research, demonstrating commitment to reproducible science.