جست‌وجوی استاد و پژوهشگر

Jürgen Brauerمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robotics
• +۴ مورد دیگر

Prof. Dr.-Ing. Jürgen Brauer is a professor at the Kempten University of Applied Sciences , holding positions in both the Faculty of Computer Science and Faculty of Electrical Engineering . He chairs the AI and Computer Vision and Driver Assistance Systems programs for Master’s students. His research spans Computer Vision, Machine Learning, Robotics, Deep Learning , and Sensor Data Processing , with a focus on applications in autonomous systems and medical imaging. Education : Doctoral Thesis (2014) at Karlsruhe Institute of Technology (KIT). Recent Research Trends : His work emphasizes unsupervised representation learning, addressing objective function mismatches in pretext-target task relationships, and advancing generative models for 3D human pose estimation. Publications : He has contributed to journals like Neural Computing and Applications and conferences such as IEEE CVW , ICMLA , and RoboCup , often in collaboration with industry partners like AGCO Fendt and academic institutions. Scientific Awards : Best Paper Award at ACM ARTEMIS 2010. Advising : Supervised over 40 theses (2016–2025), including topics in autonomous driving, reinforcement learning, and robotics. Collaborations : Partnered with Fraunhofer IOSB, DLR, Bosch, and others on projects involving sensor systems and industrial applications.

Wolfgang Oertelمشاهده پروفایل

استاد

• Computer Graphics
• Computer Vision
• Scientific Visualization
• +۵ مورد دیگر

Wolfgang Oertel serves as Professor of Computer Graphics at Dresden University of Applied Sciences (HTW Dresden) within the Faculty of Informatics and Mathematics. He has held significant leadership roles including Dean of Studies (2009-2012) and Dean (2012-2015) for his faculty, and coordinated international university partnerships (2007-2011). His academic career spans over four decades with appointments at TU Dresden (1984-1998), FhG/IVI Dresden (1999-2004), and TUBA Freiberg (visiting positions in 1998-1999 and 2004-2005). Professor Oertel's research spans computer graphics, computer vision, virtual reality, and artificial intelligence with emphasis on practical applications. His work integrates knowledge representation with visual systems, focusing on spatiotemporal modeling, scientific-technological visualization, and CAD-oriented IT systems. Current projects include the Saxony5 Co-Creation Lab for Artificial Intelligence (2021) and systems for image data processing in scientific infrastructure (SEVVBWG series 2020-2023). His publication trends reveal consistent evolution from database/knowledge processing (1980s-1990s) to computer vision applications (2000s) and sophisticated AI-integrated visualization systems (2010s-present). Recent work focuses on heterogeneous traffic data visualization, knowledge-based graphic object synthesis, and conceptual frameworks bridging AI with virtual reality. Key technical domains include VRML/X3D, OpenCV, VTK, and specialized hardware integration. Award for integrated data processing (Integrata AG Tübingen, 1995) Oertel actively supervises graduate works and leads multiple research projects including Saxony5CCLKI (2021), SEVVBWG2 (2023), and GWMSV traffic simulation (2021). His laboratory maintains extensive equipment including stereo displays, 3D cameras, microscopes, VR headsets, and robotic platforms supporting research in virtual intelligent environments. Current teaching includes Computer Graphics I/II, CAD systems, and Computer Vision courses across multiple informatics programs.

• Production Engineering
• Electrical Engineering
• Industry 4.0
• +۵ مورد دیگر

Dr. Andreas Zedler is a Researcher at Leuphana University Lüneburg's Institute for Production Engineering and Systems (IPTS), where he bridges practical craftsmanship with advanced scientific research. His unique career trajectory spans from electrical installation apprenticeship to doctoral work in technical optics, creating a distinctive perspective that informs both teaching and research. Apprenticeship: Electrical installer (1980s) Bachelor: Applied Automation Technology, Northeast Lower Saxony University of Applied Sciences Master: Applied Computing, Northeast Lower Saxony University of Applied Sciences/University of Wolverhampton Doctorate: Dr. rer. nat. in technical optics His research integrates hands-on engineering experience with cutting-edge academic inquiry, focusing on production engineering systems where he leads projects addressing Industry 4.0 challenges, material forming processes, and digital competence development. Zedler's work consistently connects theoretical frameworks with practical implementation, particularly in remote laboratory systems and AI-driven manufacturing optimization. Recent publications reveal a strong trend toward data-driven process optimization, with 60% of his recent work incorporating AI/ML methodologies applied to manufacturing challenges. His research spans materials science (particularly magnesium nanocomposites and copper conductors), sensor fusion systems, and educational technology for engineering education, demonstrating remarkable interdisciplinary range while maintaining focus on practical industrial applications. Teaching Award recognizing innovative pedagogical approaches Zedler actively mentors students through bachelor's and master's thesis supervision at IPTS, while leading multiple collaborative research initiatives including BMBF-funded projects like PROMINT_40 for women in STEM and TrICo for sustainable production communities. His work connects academic research with industry through SME partnerships focused on 5G implementation and digital transformation. He contributes to Leuphana's technical infrastructure development, particularly in remote laboratory systems for electrical engineering education and the 10 MN extrusion press facility for advanced materials research, supporting both educational programs and industrial collaboration projects.

Harry Hauptمشاهده پروفایل

استاد

• Statistics
• Econometrics
• Data Analytics
• +۴ مورد دیگر

Prof. Dr. Harry Haupt is a full Professor of Statistics and Data Analytics at the University of Passau's School of Business, Economics and Information Systems, where he leads the Chair of Statistics and Data Analytics. He has held this position since 2012 and previously served as Vice President for Research (2014-2020) and Deputy President (2017-2020) of the university. His research focuses on developing and applying flexible regression methods, with emphasis on semi-parametric techniques, quantile regression, and spatio-temporal modeling. Key application areas include environmental statistics, econometrics, marketing research, and machine learning. His work bridges theoretical statistics with computational implementations for real-world problems. Prof. Haupt's publications demonstrate strong interdisciplinary collaboration, particularly in climate science, environmental health, and econometrics. Recent work emphasizes quantile methods, election forecasting, air pollution modeling, and educational pedagogy in econometrics. Awards & Recognition: Habilitation Award, University of Regensburg PhD Supervision (Selected): Matthias Wild (2024): Machine learning applications Svenia Behm (2022): Environmental statistics Markus Fritsch (2019): Panel data methods 7 additional doctoral students in statistics and econometrics He leads a research team focusing on computational statistics and maintains active collaborations across economics, environmental science, and social research domains.

Martin Schörnerمشاهده پروفایل

پژوهشگر

• Robotics
• Software Engineering
• Automation
• +۳ مورد دیگر

Martin Schörner is a Researcher at the Institute for Software & Systems Engineering within the Faculty of Applied Computer Science at the University of Augsburg, Germany, where he has been affiliated with the Chair of Software Engineering since 2019. His academic qualifications include: Master in Computer Science and Engineering (University of Augsburg, 2017-2019) Bachelor in Computer Science and Engineering (University of Augsburg, 2013-2017) His research centers on Software-driven Robotics and Automation , specializing in UAV-based inspection systems, distributed robot ensembles, and reconfigurable hardware architectures. His work bridges theoretical software engineering with practical robotics applications, focusing on autonomous navigation, emergency control systems, and semantic integration for industrial automation. Analysis of his 2020-2023 publications reveals a concentrated research trajectory in UAV inspection methodologies, featuring advancements in offline/online path planning, ROS 2 interfaces, and plug-and-play systems. Key thematic clusters include real-time route optimization for large-component inspection, emergency control architectures for UAV swarms, and semantic frameworks for hardware reconfiguration. No formal scientific awards or fellowships are documented in the available sources. While no student advisement records or specific grant funding details are publicly listed, his collaborative work with Prof. Wolfgang Reif and colleagues indicates active participation in institutional research initiatives. Current projects emphasize industrial automation through UAV ensembles and reconfigurable systems. He operates within the Software Engineering team at the Institute for Software & Systems Engineering (ISSE), which conducts fundamental and applied research under Prof. Reif's direction. The institute focuses on robotics software engineering, with applications in industrial inspection and autonomous systems development.

• Quantitative Methods
• Energy Sector
• Distributed Flexibilities
• +۵ مورد دیگر

Prof. Dr. Dietmar Graeber is a Professor of Energy Economics at Ulm University of Applied Sciences (THU), where he has worked since 2017. He leads the Smart Grids Research Group and the Transfer Center for Energy Economics , while overseeing the Energy Economics B.Sc. program . He is also a board member of the Institute for Energy Technology and Economics (IEE) , the Smart Grids Platform Baden-Württemberg , and participates in the Center for Energy Research and Technology , Center for Digitization, Analytics, and Data Science Ulm , and HAW Doctoral Center in Baden-Württemberg . Focuses on quantitative methods in energy systems , particularly distributed flexibilities , sector integration , and European energy market design . Coordinates multiple national and international research projects , including collaborations with ENTSO-E. Teaches in Bachelor and Master programs , with lecture materials available via Moodle . Research trends highlight innovations in TSO-DSO interaction , small PV participation in balancing markets , capacity trading , and smart meter applications for grid services. His work emphasizes market coupling , data-driven solutions , and privacy-preserving flexibility .

Yannik Hahnمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۲ مورد دیگر

Yannik Hahn serves as a Scientific Researcher and doctoral student at the Institute for Technologies and Management of Digital Transformation (TMDT) at the University of Wuppertal since June 2021, focusing on machine learning applications for industrial and environmental systems. His educational background includes a Bachelor's and Master's degree in Computer Science from Heinrich Heine University Düsseldorf, specializing in machine learning and deep learning. His Master's thesis investigated cross-lingual text summarization using transformer-based neural networks. Hahn's research spans Machine Learning, Deep Learning, Natural Language Processing, Explainable AI, and Reinforcement Learning, with emphasis on practical implementations in manufacturing quality control and environmental forecasting. His work bridges theoretical AI advancements with real-world industrial challenges. Analysis of his publications reveals a consistent focus on transformer architectures and deep learning for time-series applications, particularly in arc welding quality prediction and flood forecasting. His research demonstrates innovation in representation learning, out-of-distribution detection, and efficient continual learning systems for industrial deployment. No scientific awards are documented in the available materials. As a doctoral candidate, Hahn has no listed advisees or formal mentoring responsibilities. No research grants or funding sources are specified in the provided documentation. He operates within the Institute for Technologies and Management of Digital Transformation (TMDT), which specializes in digital transformation technologies and their industrial implementation, particularly in manufacturing and environmental monitoring contexts.

• Sensor Networks
• Middleware Systems
• Federated Learning
• +۵ مورد دیگر

Eleftheria Katsiri is a researcher with extensive contributions to computer science , focusing on sensor networks , middleware systems , and serious games . Her work bridges privacy-preserving machine learning with IoT applications in healthcare and environmental monitoring. Collaborations with Alexandros Gazis, Pavlos S. Efraimidis, and other researchers highlight her interdisciplinary approach. Key publication venues: Frontiers Robotics AI , ICR , CoRR , Communications of the ACM Research trends since 2022 include federated learning for health apps, blockchain middleware, and edge intelligence for air quality monitoring. Her articles demonstrate expertise in abstract reasoning, sensor data fusion, and scalable middleware design. No formal awards or academic appointments are mentioned in available data.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Jiawei Zhang is an active researcher with primary affiliations at institutions like the University of California, Davis and other universities in China and the USA. His work spans Computer Science , Artificial Intelligence , and Biomedical Engineering , focusing on applications in image processing , autonomous vehicles , and remote sensing . Research interests include deep learning , graph neural networks , 3D reconstruction , and medical image analysis . Recent publications emphasize transformer models , lightweight AI frameworks , and multimodal data fusion . His work has been published in prestigious venues such as IEEE Transactions on Biomedical Engineering , CVPR , and IEEE Internet of Things Journal . Collaborations include researchers from institutions in China, the US, and Europe. Key trends in his 2024-2025 articles involve industrial anomaly detection , liver tumor segmentation , autonomous vehicle control , and AI-driven sensor networks . Methodologies often integrate attention mechanisms , generative models , and real-time optimization .

• Precision Agriculture
• Sensor Technology in Crop Production
• Machine Learning for Weed Detection
• +۳ مورد دیگر

Karl-Heinz Dammer is a senior researcher at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) in Potsdam, Germany, specializing in the Agromechatronics department. With over 25 years of experience, his work focuses on sensor-based crop protection , precision agriculture , and machine learning applications for weed and disease detection. He has contributed to projects like Precision PS for targeted pesticide use and FungiDetect for early disease identification. Habilitation : Martin Luther University Halle-Wittenberg (2000) Education : Agricultural Sciences (1980-1985), Doctorate in Phytopathology (1988) Key Research Areas : Image segmentation for weed detection, sensor-guided crop protection, and agricultural robotics His recent publications emphasize deep learning models like U-Net for identifying weeds (e.g., Bur Chervil) and diseases (e.g., stripe rust in wheat) using RGB imagery and UAV-based systems. Collaborations span institutions in Germany, Iran, and international teams. Awards include the LEOPOLDINA Prize (1992) for early career achievements.

Nathalie Lenkeمشاهده پروفایل

پژوهشگر ارشد

• Attosecond Physics
• Ultrafast Laser Science
• Quantum Optics
• +۳ مورد دیگر

Dr. Nathalie Lenke is a Postdoctoral Research Fellow in the Attosecond Physics group at the Max Planck Institute of Quantum Optics and affiliated with Ludwig-Maximilians-Universität (LMU) Munich. Her research is centered on ultrafast laser science and quantum optical phenomena occurring on attosecond timescales. Her work lies at the intersection of atomic, molecular, and optical physics, focusing on the generation and application of ultra-short light pulses to probe electron dynamics in matter. This field enables the observation of fundamental processes in real time, contributing to advances in both basic science and future technologies in photonics and quantum control. Dr. Lenke is based at the Garching campus in Germany, with office locations at LMU (Room 217) and LEX (Room U110). While specific publications and research projects are not detailed in the available text, her affiliation with one of the world’s leading institutes in quantum optics underscores her active role in cutting-edge scientific inquiry. She has not been listed with any scientific awards in the provided information, and there is no mention of advised students or teaching responsibilities. Dr. Lenke collaborates within a high-profile research environment dedicated to pushing the frontiers of ultrafast science, utilizing advanced laser systems and experimental setups designed to explore matter at the fastest observable timescales.

• Algorithmic Game Theory
• Stochastic Games
• Distributed Systems
• +۵ مورد دیگر

Hugo Gimbert is a Researcher at the LaBRI (Laboratoire Bordelais de Recherche en Informatique) within the Méthodes Formelles Team . His work bridges computational theory and practical applications, focusing on algorithmic game theory, stochastic processes, and formal methods in computer science. Research interests include: Decidability problems in distributed and stochastic games Partial observation in multi-player systems Cooperation and competition in bridging scenarios Social choice theory implementation Precision farming automation Probabilistic logics and humanoid robot control via MDPs Advising roles: PhD advisor for Soumyajit Paul, Edon Kelmendi, Quentin Rouxel, and Ludovic Hofer PhD co-advisor for Simon Mauras Former advisor for Youssouf Oualhadj Key projects: Stamina - Stabilisation monoids and regular language analysis Marmotte - CNRS committee digitalization platform Rhoban System - Real-time robotics for agriculture and soccer Parcoursup - Algorithmic college admission system for French government

• Medical Image Analysis
• Deep Learning for Biomedical Applications
• Edge Computing and Real-Time Segmentation
• +۵ مورد دیگر

Jian Zhuang is a Professor at the Department of Artificial Intelligence, School of Computer Science and Engineering, University of Electronic Science and Technology of China. With over 30 years of academic contributions, he leads research at the intersection of medical image analysis , deep learning , and biomedical engineering . His work focuses on real-time medical imaging solutions for cardiac disease diagnosis and surgical planning. Research highlights include: Pioneering 3D CT and MRI segmentation frameworks for congenital heart disease Developing domain-adapted neural networks for clinical applications Advancing edge computing techniques for low-latency medical image processing Creating large-scale medical datasets like ImageCAS and ImageCHD His publications (90+) in venues such as Medical Image Analysis , IEEE Transactions , and MICCAI demonstrate technical leadership in medical AI and computer vision . Recognized with multiple awards including the MICCAI Best Paper Award and IEEE Access Outstanding Paper Award , he mentors a research group that has produced key tools for cardiac surgical telementoring and image-guided interventions .

Frank Eichingerمشاهده پروفایل

پژوهشگر

• Graph Mining
• Software Defect Localization
• Data Mining
• +۴ مورد دیگر

Dr. Frank Eichinger is a researcher at the Institute of Program Structures and Data Organization (IPD) within the Faculty of Computer Science at Karlsruhe Institute of Technology (KIT), Germany. He works under the Chair of Prof. Klemens Böhm, focusing on advanced data mining applications in software engineering and energy systems, with significant contributions to graph-based analysis techniques. Education: PhD in Computer Science, Karlsruhe Institute of Technology (KIT), 2011 Research Interests: Eichinger's work centers on graph mining for software defect localization , particularly through call-graph analysis in multicore and multithreaded environments. His research extends to smart grid data compression and energy domain analytics , combining entropy-based methods with scalable algorithms. Key innovations include hierarchical mining of dynamic call graphs and weight-constrained subgraph analysis for improved fault detection in complex systems. Publication Trends: His 15 most recent publications (2008-2015) reveal a cohesive trajectory from foundational graph-mining algorithms to real-world energy applications. Early work focused on software bug localization via call graphs, evolving toward time-series compression for smart grids. The research demonstrates consistent methodological rigor in adapting graph mining to domain-specific challenges, with strong representation in top venues like VLDB Journal and ECML PKDD. Scientific Awards: No awards documented in source material Advising and Grants: While specific student supervision details are absent, his dissertation role and collaborative publications indicate mentorship activities. Project involvement in Graph Mining and LogoTakt suggests grant-funded research, though specific funding sources aren't disclosed. Labs and Teams: Eichinger operates within KIT's Information Management Systems research group (IPD Böhm), actively contributing to the Graph Mining project and LogoTakt initiative alongside collaborators J. Mülle and M. Bracht. His work integrates closely with KIT's energy informatics and software engineering research ecosystems.

Holger Gieseمشاهده پروفایل

استاد

• Model-Driven Engineering
• Self-Adaptive Systems
• Cyber-Physical Systems
• +۱۲ مورد دیگر

Prof. Dr. Holger Giese is a full Professor at the System Analysis and Modeling Group of the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany. He leads research initiatives in model-driven engineering , self-adaptive systems , and cyber-physical systems , with a focus on causal representations , neuro-symbolic AI , and multi-agent reinforcement learning . His research explores the intersection of formal modeling and machine learning , addressing challenges in: Runtime verification and validation of dynamic systems Model transformation and synchronization Code generation for self-optimizing architectures Probabilistic decision-making under uncertainty Transfer learning for autonomic computing Recent work (2024-2025) emphasizes neuro-symbolic approaches for robust multi-agent systems , spatio-temporal graph modeling for cloud systems, and incremental query evaluation in dynamic environments. Collaborations span institutions like IBM Japan, Krems University, and Humboldt University. He actively teaches courses such as Advanced Topics in Software Engineering , Graph Neural Networks , and AI Ethics Engineering , and leads labs including the Model-Driven Engineering Laboratory (MDELab.de) and Software Engineering for Self-Adaptive Systems (self-adaptive.org) .