جست‌وجوی استاد و پژوهشگر

Stefan Böttcherمشاهده پروفایل

استاد

• Databases
• Data Compression
• Graph Algorithms
• +۳ مورد دیگر

Prof. Dr. Stefan Böttcher serves as Professor and Section Owner of Databases and Electronic Commerce within the Department of Computer Science at the University of Paderborn's Faculty of Computer Science, Electrical Engineering and Mathematics. His office (F2.217, Fürstenallee 11) operates by email appointment, and he teaches courses including Betriebssysteme (Operating Systems), maintaining active academic engagement through current publications and institutional roles. His research centers on advanced data compression methodologies for complex structures, specializing in grammar-based techniques for graphs, strings, and trees. Key focus areas include enabling efficient query processing directly on compressed representations—critical for semantic web triple stores and big data systems—while optimizing storage-performance tradeoffs through novel algorithmic approaches in Burrows-Wheeler transforms and tree recompression. Analysis of his 2018-2025 publications reveals consistent innovation in compressing non-linear data structures: from RECUT's tree recompression to 2025's string partitioning for BWTs, his work bridges theoretical algorithm design with practical database applications, particularly targeting high-performance querying in grammar-compressed graph frameworks for RDF triple stores. Scientific Awards No awards documented in source materials Prof. Böttcher mentors graduate researchers in database compression technologies through his Databases and Electronic Commerce section, with research outcomes published in premier venues like IEEE DCC and Big Data conferences. His projects demonstrate sustained funding through continuous high-impact outputs without explicit grant documentation in the provided texts. He leads the Databases and Electronic Commerce research group, driving innovations in compressed data structures that reduce storage overhead while maintaining query efficiency for semantic web and large-scale graph database implementations.

Ondrej Vaculinمشاهده پروفایل

استاد

• Passive Vehicle Safety
• Automated Driving
• Vehicle Dynamics
• +۳ مورد دیگر

Prof. Ondrej Vaculin, Ph.D., is a Professor at Technische Hochschule Ingolstadt (THI), specializing in passive vehicle safety, mechatronic systems, and automated driving. He joined THI in 2018 and previously held roles at TÜV SÜD Prague (2008–2018) as Vice President for Safety and Security in Automotive, and as a researcher at Czech Technical University (2005–2008) and DLR Oberpfaffenhofen (2000–2005). His research focuses on enhancing vehicle safety through advanced simulation, sensor integration, and machine learning applications. Education: Ph.D. in Mechanics of Solids, Deformable Bodies and Continua from Czech Technical University in Prague (2001), and a degree in Technical Cybernetics from the Faculty of Electrical Engineering at the same university (1991). His work bridges theoretical research and practical implementation, addressing challenges in automated driving systems, crash simulation, and human body diversity in safety design. Research interests include passive safety systems, autonomous vehicle dynamics, and infrastructure integration for safety enhancements. Recent publications emphasize collaborative smart infrastructure, sensor fusion for free space detection, and machine learning in crash detection. He actively contributes to FISITA, serving as a Council Delegate and member of technical committees. Awards/memberships: Active in FISITA, holding leadership roles in technical committees. No explicit awards listed, though his contributions to automotive safety standards are notable. Grants and advising: While no specific grants or students are detailed, his extensive industry and academic collaborations (e.g., IN2Lab testing field projects) highlight his role in applied research and development.

• Artificial Intelligence
• Computational Intelligence
• Neural Networks
• +۵ مورد دیگر

Lakhmi C. Jain is a distinguished academic affiliated with the University of South Australia. As a Professor, she has made significant contributions to the fields of Artificial Intelligence, Computational Intelligence, and Fuzzy Systems. Her research spans neural networks, decision support systems, robotics, and data analysis, with a focus on interdisciplinary applications. Her career includes over 445 publications, including books like Complex Networks in Software, Knowledge, and Social Systems (2019) and E-Learning Systems - Intelligent Techniques for Personalization (2017). She has held editorial roles in journals such as the International Journal of Intelligent Decision Technologies (IDT) and the Journal of Intelligent & Fuzzy Systems. Jain's work emphasizes practical applications of computational intelligence, including efforts in software development, biomedical signal processing, and multi-agent systems. She has collaborated extensively with researchers globally, contributing to advancements in AI-driven technologies and decision-making frameworks.

• Process Systems Engineering
• Chemical Engineering
• Energy Conversion Systems
• +۴ مورد دیگر

Prof. Kai Sundmacher is a Full Professor and Chair of Process Systems Engineering at Otto-von-Guericke University Magdeburg, and Director of the Max Planck Institute for Dynamics of Complex Technical Systems. His expertise spans model-based analysis of complex process systems, integrated chemical processes, energy conversion (fuel cells/electrolysis), and biosystems engineering. He holds a Dr.-Ing. habil. from Clausthal University of Technology and has held visiting positions at institutions worldwide. Research focuses include catalyst design for CO2 methanation, digital twins for power-to-X processes, and sustainable chemical production. Awards include the Arnold Eucken Prize (1999) and Carl Zerbe Prize (1998). He leads initiatives like the Max Planck Research Network for Synthetic Biology and chairs the German Chemical Energy Research group. Teaching covers advanced process systems engineering, fuel cell technology, and bioprocess design. Over 500 publications include 400 peer-reviewed articles, with contributions to journals like Chemical Engineering Science and Fuel Cells. Active in academic service, he reviews ERC grants, chairs selection committees, and advises the Alexander von Humboldt Foundation.

• Telecommunications
• 5G/6G Networks
• Network Slicing
• +۷ مورد دیگر

Christian Wietfeld is a Professor at TU Dortmund, Germany, specializing in telecommunications, 5G/6G networks, and robotics. His research focuses on network slicing, machine learning for communications, vehicular networks, and intelligent reflecting surfaces. Affiliations: TU Dortmund Key Collaborations: Stefan Böcker, Benjamin Sliwa, Manuel Patchou His work spans 6G multi-X communications, private industrial networks, and disaster response robotics. Recent projects include mmWave reflector systems, predictive uplink slicing, and AI-driven network planning. 2024-2025 publications highlight advancements in 6G IRS, energy-efficient 5G, and vehicular connectivity. Sub-fields include beam management, digital twins, and non-terrestrial networks. He contributes to experimental frameworks like Open RAN and ns-3 simulations, emphasizing scalable solutions for industrial and emergency applications.

• Smart Grids
• Energy Transition
• Cybersecurity in Energy Systems
• +۵ مورد دیگر

Prof. Andreas Ulbig is a Universitätsprofessor at RWTH Aachen University's Institute of Electric Installations and Networks, Digitization and Energy Economics (IAEW), situated in Aachen, Germany. His work focuses on advancing smart grid technologies, energy transition strategies, and cybersecurity in power systems. He is affiliated with the Department of Active Energy Distribution Networks within the Faculty of Electrical Engineering and Information Technology. Ulbig's research emphasizes resilient communication protocols, grid topology identification, multi-use energy storage systems, and electrification of transportation. His contributions include co-authoring studies on adaptive protection systems, local energy markets, and cyber-physical resilience frameworks. He leads projects addressing municipal energy system transformations and grid-friendly energy sharing concepts. Ulbig holds a doctoral degree (Dr.) and has extensive experience in energy infrastructure digitization and grid stability. His recent publications span 2023–2025, focusing on technical and economic aspects of energy systems, with particular attention to cyberattack simulations, distribution grid flexibility, and hydrogen integration in gas networks. Collaborations include IEEE conferences and institutions like Westenergie AG, where he contributed to studies on grid-forming converters. His research is methodologically diverse, combining machine learning, stochastic control, and experimental validation with industry partners. In academic service, Ulbig advises on energy policy implications and co-supervises research projects at IAEW. His work bridges theoretical advancements with practical grid solutions, addressing challenges such as curative distribution measures, tariff design for local markets, and infrastructure expansion costs. Key technical interests include broadband powerline communication, digital twin applications, and optimization of DC/AC grid integration.

• Statistical Relational Learning
• Machine Learning
• Probabilistic Graphical Models
• +۵ مورد دیگر

Lise Getoor is a Professor in the Department of Computer Science at the University of California, Santa Cruz, within the Baskin School of Engineering. She previously held positions at the University of Maryland, College Park and has established herself as a leading researcher in statistical relational learning and neuro-symbolic artificial intelligence. Her research focuses on developing principled approaches to reasoning and learning with rich, relational data. She has made significant contributions to probabilistic soft logic, statistical relational learning, knowledge graph construction, and the integration of symbolic and neural approaches to AI. Her work bridges theoretical foundations with practical applications in areas including social network analysis, sustainable recommendations, cyberbullying detection, and fair machine learning systems. Analysis of her recent publications reveals a strong trend toward neuro-symbolic integration, with increasing focus on combining neural networks with logical reasoning frameworks. Her research demonstrates consistent innovation in developing scalable algorithms for structured prediction across diverse application domains while addressing critical issues of fairness and interpretability in AI systems. Professor Getoor has advised numerous PhD students who have become active contributors to the field, including Connor Pryor, Charles Dickens, Eriq Augustine, and Varun Embar. Her research has been supported by multiple grants from major funding agencies, though specific details aren't provided in the current data. She leads research in the Statistical Relational AI (StaRAI) Lab at UC Santa Cruz, where her team develops frameworks that combine logical reasoning with probabilistic modeling to address complex real-world problems requiring both structured knowledge and statistical learning.

• Power Systems
• Smart Grids
• Renewable Energy Integration
• +۳ مورد دیگر

Prof. Ferdinanda Ponci is a Professor at RWTH Aachen University, leading the Department of Monitoring and Distributed Control for Energy Systems. Her work focuses on smart grid technologies, renewable energy integration, and control systems for modern power networks. She actively contributes to advancing grid resilience, EV charging infrastructure, and cybersecurity in energy systems. Key research interests include hybrid AC-DC grids, distributed energy resources coordination, and application of AI in grid management. She has pioneered frameworks like datafev for EV charging management and SMU platforms for synchronized measurements. Her interdisciplinary approach also addresses diversity in engineering and societal impacts of energy systems. Prof. Ponci’s laboratory activities involve hardware-in-the-loop testing, blockchain-based grid solutions, and development of educational toolboxes for emerging technologies. She collaborates on projects such as submarine transmission systems and grid interoperability testing, contributing to both academic and industrial advancements in power systems engineering.

August Wilhelm Scheerمشاهده پروفایل

استاد

• Business Informatics
• Process Management
• Digital Transformation
• +۵ مورد دیگر

Prof. Dr. August-Wilhelm Scheer is a Professor of Business Informatics at Saarland University and founder of the Scheer Group, a leading IT enterprise. He pioneered the ARIS method for integrated information systems and established the School of Digital Sciences at Saarland University. His work bridges academic research and industry application through ventures like IDS Scheer Holding and IMC AG. Educations: Founded Institute of Business Informatics at Saarland University Doctoral supervision of 76 theses (over 3/year average) Research collaborations with German Research Center for Artificial Intelligence (DFKI) Research Interests: Focuses on business process management, digital transformation, and AI-driven enterprise solutions. His ARIS method revolutionized IT-business alignment through process modeling. Current work explores composable enterprise architectures and AI agent systems in business contexts. Awards: German Research Hall of Fame Inductee (2017) Lifetime Achievement Award (Global University Alliance) Advising & Innovation: Supervised 76 doctoral students and spun-off 1,300+ client companies through Scheer Group solutions. Spearheaded digital learning platforms via IMC AG. Labs/Teams: Leads August-Wilhelm Scheer Institute for Digital Products and Processes, fostering research-practice partnerships in digital innovation.

Philippe Toblerمشاهده پروفایل

دانشیار

• Neuroeconomics
• Social Neuroscience
• Decision Making
• +۳ مورد دیگر

Prof. Philippe Tobler is an Associate Professor of Neuroeconomics and Social Neuroscience at the Department of Economics, University of Zurich. His research focuses on the neural mechanisms underlying decision making, reward learning, and social behavior, particularly studying brain structures like the dopaminergic midbrain, striatum, and prefrontal cortex. He employs functional magnetic resonance imaging (fMRI) and pharmacological interventions to investigate how economic reward parameters (e.g., risk, delay, probability) are processed in the brain. His work also explores individual differences in reward processing and the implications for conditions like schizophrenia and bipolar disorder. Tobler has authored numerous peer-reviewed publications, including studies on social threat learning, testosterone's impact on social behavior, and the role of neurotransmitters in decision-making processes. He collaborates with institutions like the Zurich Center for Neuroeconomics and teaches courses on decision neuroscience and neuroeconomics.

Lorenz Linhardtمشاهده پروفایل

پژوهشگر

• Explainable AI
• Robust Machine Learning
• Machine Learning for Medical Applications
• +۳ مورد دیگر

Lorenz Linhardt is a Researcher and PhD candidate at the Machine Learning Group of Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on robustness of deep neural networks, spurious correlations, and representation learning, with applications in explainable AI and medical domains. Educational Background: M.Sc. in Computer Science, 2019 – ETH Zürich B.Sc. in Computer Science, 2016 – University of Vienna Research Interests: Robust Machine Learning : Addressing vulnerabilities in neural networks caused by spurious correlations and adversarial examples. Human Alignment : Bridging gaps between AI outputs and human judgments via representation learning and similarity metrics. Medical Applications : Leveraging machine learning for counterfactual inference in healthcare and dose-response modeling. Article Trends: Recent work emphasizes alignment between human judgments and AI systems, with studies on latent diffusion models, adversarial robustness, and forensic analysis of malware classifiers. Earlier contributions span astronomy surveys and decision tree optimization. Labs/Teams: Active in BIFOLD and the TU Berlin Machine Learning Group, focusing on foundational AI research.

Matthias Meckelمشاهده پروفایل

مدرس ارشد

• e-Business Strategies
• Small and Medium Enterprises (SMEs)
• Online Consumer Behavior
• +۳ مورد دیگر

Dr. Matthias Meckel is a Senior Lecturer in the School of Business, Social & Decision Sciences at Constructor University. Previously, he held academic positions at the University of Central Lancashire, including Principal Lecturer (2020-2023), Senior Lecturer (2007-2020), and Lecturer (2004-2007). His research focuses on e-business strategies for SMEs, organizational technology adoption barriers, and survey methodology. He holds a PhD in e-business strategies for SMEs from the University of Central Lancashire, alongside an MSc in Business Administration with IT (Distinction) and a Diplom-Informatiker (FH) degree. His research interests include digital transformation frameworks for SMEs, healthcare information systems, and cross-channel survey techniques. Notable work includes developing a hospital resource management system using real-time data and analyzing organizational barriers in e-Banking adoption. His publications span journals like Strategic Change and conferences such as the International Conference on Systems and Informatics. Teaching expertise spans business management, information systems, and technology strategy. He has advised numerous projects on SME digitalization and contributed to academic events globally. Currently based at Constructor University's Bremen campus, he continues advancing knowledge in business innovation and technology adoption.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +۳ مورد دیگر

Prof. Felix Bießmann holds a professorship in Computer Science and Media at Berlin University of Applied Sciences' Department VI. His research focuses on machine learning applications in diverse fields including healthcare, urban planning, environmental science, and robotics through his Cognitive Algorithms Lab. He teaches courses such as Machine Learning, Deep Learning, and Data Science Workflows, alongside roles at TU Berlin and Korea University. Education: PhD (Dr. rer. nat.) in Natural Sciences Research interests span machine learning theory and practical implementations across domains like computer vision, generative AI, and sensor data analysis. His work addresses challenges in automated systems, healthcare monitoring, and sustainable technologies. Recent student theses explore topics like license plate recognition, adaptive game soundtracks, and bird song detection using TinyML. Collaborations include projects with the Charité Berlin and Robert-Koch Institute. Lab: Cognitive Algorithms Lab (developing machine learning methods/applications) Contact: felix.biessmann@bht-berlin.de | Office D138, Berlin University of Applied Sciences.

Thilo Dieingمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Thilo Dieing is a social data scientist at the Chair of Artificial Intelligence led by Prof. Dr. Heiner Stuckenschmidt at the University of Mannheim's Mannheim Center for Data Science. His research focuses on AI-supported study orientation, including developing chatbots and machine learning models to improve student advising. He contributes to projects such as NEST-BW, aiming to reduce dropout rates by creating personalized degree recommendations via generative AI chatbots, and the TransforMA project, which produced the multilingual recipe chatbot Foodle. His work emphasizes ethical AI use and explainable methods (XAI). Thilo holds a bachelor’s degree in political science and a master’s in data science from Mannheim. His technical expertise spans NLP, large language model optimization, and classification models. Future interests include integrating speech/image recognition and combining LLMs with knowledge graphs for advanced reasoning. His research underscores the critical role of data science in analyzing student data to create tailored educational pathways, ensuring chatbots like Foodle and the NEST-BW advisor deliver safe, effective guidance. Collaboration with student teams (e.g., a six-person TransforMA group) highlights his commitment to mentoring and practical application of AI in education.

Michael Ritterمشاهده پروفایل

پژوهشگر

• Combinatorial Optimization
• Integer Programming
• Operations Research
• +۴ مورد دیگر

Dr. Michael Ritter is a researcher and academic staff member at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Mathematics. His primary role involves advising Master's students in Mathematics programs and coordinating administrative duties for academic affairs. He is part of the Discrete Optimization research group and collaborates with professors like Stefan Weltge and Peter Gritzmann on advanced optimization projects. His research focuses on Combinatorial Optimization, Integer Programming, and real-world applications such as flight schedule design, semiconductor manufacturing optimization, and logistics systems. He has contributed to projects involving automated driving systems, production network resilience, and low-power semiconductor design. Ritter teaches courses on Discrete Optimization and Case Studies in Optimization, balancing theoretical instruction with practical problem-solving. He has co-advised numerous theses on topics ranging from supply chain optimization to timetabling algorithms, demonstrating his commitment to both academic research and student mentorship. His work bridges mathematical theory with industrial applications, addressing challenges in manufacturing, transportation, and electronics through rigorous optimization frameworks. He maintains an active publication record in reputable journals and conferences, reflecting his expertise in interdisciplinary optimization research.