جست‌وجوی استاد و پژوهشگر

Ralph Ewerthمشاهده پروفایل

استاد

• Multimedia Retrieval
• Machine Learning
• Visual Analytics
• +۵ مورد دیگر

Prof. Ralph Ewerth is a Professor leading the Visual Analytics research group at the Technische Informationsbibliothek (TIB) since 2015. His work bridges computer science, information science, and education, focusing on multimedia retrieval, machine learning, and visual analytics. He has pioneered research in areas like content-based video retrieval, image-text relation analysis, and educational technology platforms. Key research interests include: Development of AI-driven tools for educational analytics (e.g., learning platforms for chemistry education) Advancements in multimedia retrieval systems (e.g., TIB AV-Analytics platform) Analysis of sports data through spatiotemporal event detection Design of open research knowledge graphs and journal recommendation systems (B!SON project) Recent publications (2022-2024) emphasize multimodal misinformation detection, search behavior analysis, and fusion of vision-language models. He has edited conference proceedings and contributed to over 150 peer-reviewed works across domains like digital libraries, computer vision, and educational technology. His research infrastructure includes the TIB AV-Analytics platform for scholarly video analysis and the B!SON open access journal recommendation tool. Collaborative projects focus on integrating semantic web technologies with educational and sports data analysis.

Wil van der Aalstمشاهده پروفایل

استاد

• Process Mining
• Data Science
• Petri Nets
• +۸ مورد دیگر

Wil van der Aalst is a full professor at RWTH Aachen University , leading the Process and Data Science (PADS) group . He serves as Chief Scientist at Celonis , part-time affiliated with Fraunhofer FIT , and holds an unpaid professorship at Queensland University of Technology . His roles include deputy CEO of the Internet of Production Cluster of Excellence, co-director of the RWTH Center for Artificial Intelligence , and speaker of the RWTH ICT Profile Area . Research Interests : Wil van der Aalst pioneered process mining , bridging model-based process analysis (e.g., Petri nets, simulation) and data-centric techniques (e.g., machine learning). His work spans business process management , workflow modeling , interorganizational workflows , and data science . He developed tools like ProM , YAWL , and Woflan , influencing process mining standardization and commercial tools ( Celonis , Disco , etc.). Scientific Contributions : He has authored over 300 journal papers, 35 books, 720 conference papers, and 90 book chapters. His H-index of 184 and over 158,000 citations reflect his global impact. Key articles focus on workflow patterns , YAWL , interorganizational workflow verification , and social network discovery from event logs . Awards and Honors : Recipient of the Alexander von Humboldt Professorship (2018, €5M), he is an IFIP , IEEE , and ACM Fellow , and elected to multiple academies (e.g., Koninklijke Nederlandse Akademie van Wetenschappen, Academia Europaea). He received honorary degrees from Moscow Higher School of Economics , Tsinghua University , and Hasselt University .

• Neuroscience
• Neurophysiology
• Systems Neuroscience
• +۵ مورد دیگر

Professor Björn Kampa is a faculty member at RWTH Aachen University specializing in Molecular and Systemic Neurophysiology. Based at Worringerweg 3, Building 5350, Room 0.113 in Aachen, Germany, he leads research on cortical circuit function and neuronal development. His work bridges experimental neurophysiology with computational approaches to investigate fundamental brain mechanisms, particularly in visual processing and neural plasticity. Contact is available via kampa@brain.rwth-aachen.de. Dr. Kampa's research spans Systems Neuroscience , Molecular Neuroscience , and Computational Neuroscience , with emphasis on cortical circuit dynamics, dendritic physiology, and sensory processing. He employs advanced techniques including two-photon calcium imaging, electrophysiology, and computational modeling to study neuronal maturation, visual perception, and the impact of genetic modifications on brain function. His work frequently examines mouse models of retinal degeneration and cortical interneuron function. Analysis of his 2023-2025 publications reveals three dominant research thrusts: First, visual system neuroscience exploring cortical plasticity in degenerative models and sensory enhancement mechanisms. Second, neuronal development focusing on human neuron maturation and dendritic spine dynamics. Third, computational neurotechnology developing spike sorting tools, holography frameworks, and network analysis methods. These themes consistently integrate experimental data with theoretical modeling to address fundamental questions in cortical function.

Michael Gertzمشاهده پروفایل

استاد

• Data Science
• Natural Language Processing (NLP)
• Machine Learning
• +۳ مورد دیگر

Michael Gertz is a full professor at Heidelberg University, leading the Data Science Group within the Faculty of Mathematics and Computer Science. He holds a diploma from TU Dortmund University and a Dr. rer. nat. from Leibniz University Hannover (1996). Before joining Heidelberg in 2008, he was faculty at the University of California, Davis (1997–2008). His research spans Data Science, Natural Language Processing, Machine Learning, Complex Networks, and Legal Technology, with applications in law, medicine, economics, and political sciences. Academic roles include Dean of the Faculty of Mathematics and Computer Science (2014–2016), Managing Director of the Institute of Computer Science (2016–2023), and co-speaker of the Helmholtz Information & Data Science School for Health (since 2019). He has held editorial roles at ACM Transactions on Spatial Algorithms and Systems, ACM Journal of Data and Information Quality, and The VLDB Journal. Research focuses on interdisciplinary projects, such as legal text analysis, network models, and scientific data management. He has presented widely, including keynote speeches on AI in law and legal technology. His work emphasizes exploring quantities in text, temporal networks, and event detection in social media. Professional activities include program committee memberships for conferences like SIGIR, EMNLP, and CIKM, as well as reviewing for funding agencies like the Helmholtz Society and DFG. He is a member of ACM, IEEE Computer Society (inactive since 2012), and Gesellschaft für Informatik (GI). Key projects include developing frameworks like EVELIN, CQE, and QuantPlorer, and datasets such as EUR-Lex-Sum and Klexikon. His contributions bridge computational methods and domain-specific applications, fostering innovation in data-driven solutions across disciplines.

• Natural Language Processing
• Semantic Relations
• Knowledge Graphs
• +۳ مورد دیگر

Vivi Nastase is a senior researcher affiliated with Fondazione Bruno Kessler and the Heidelberg Institute for Theoretical Studies . Her work focuses on Natural Language Processing , Knowledge Graphs , and Machine Learning , with particular emphasis on multilingual semantic analysis and linguistic structure extraction. Key research areas: Semantic relations between nominals Transformer-based sentence embeddings Cross-lingual knowledge alignment Linguistic generalization in neural models Knowledge graph construction from Wikipedia Recent publications analyze syntactic information in sentence embeddings and develop structured synthetic datasets like Blackbird Language Matrices for Italian/French benchmarks. She contributes to advancing FAIR digital objects and scholarly knowledge graph integration.

Pierluigi D’Acuntoمشاهده پروفایل

استادیار

• Computational Design
• Structural Engineering
• Architecture
• +۳ مورد دیگر

Pierluigi D’Acunto is an Assistant Professor of Structural Design at the Technical University of Munich (TUM) , holding the Rudolf Mößbauer Tenure Track Professorship. He leads the Structural Design research group, focusing on the intersection of architecture and structural engineering through computational methods. His work emphasizes geometry-based structural design, equilibrium modeling, and fabrication-aware strategies leveraging machine learning and digital manufacturing. Before joining TUM in 2021, he completed a PhD (2018) and postdoctoral research at ETH Zurich, where he also taught. His career includes professional experience in architecture and engineering firms in Italy and Switzerland, alongside international competition awards. His research has been recognized with prestigious accolades, including the ETH Medal (2019) and the IASS Tokyo Award (2016). Key areas of exploration include structural form-finding via AI, computational graphic statics, and sustainable design integration. He has published extensively in journals like Journal of Facade Design and Engineering and International Journal of Solids and Structures , emphasizing innovative structural solutions and human-machine collaboration. Education: Master of Architecture (Distinction, Architectural Association London, 2012), PhD (ETH Zurich, 2018) Awards: ETH Medal, IASS Tokyo Award, Rotary Foundation Scholarship Research Themes: Machine Learning in Design, Structural Form-Finding, Digital Fabrication, Computational Geometry

• Geospatial Data Management
• Distributed Computing
• Machine Learning
• +۴ مورد دیگر

Prof. Werner Martin is a Professor of Big Geospatial Data Management at the Technical University of Munich (TUM), affiliated with the School of Engineering and Design and the Department of Aerospace and Geodesy. His research focuses on georeferenced data processing, distributed computing, quantum algorithms, and machine learning applications in geospatial contexts. Prof. Martin holds a doctorate from LMU Munich and has held academic and research positions at institutions including LMU Munich, Leibniz-University Hannover, the German Aerospace Center (DLR), and UniBW Munich. His work bridges theoretical advancements with practical applications in spatial data analysis, visualization, and high-performance computing. Awards ACM SIGSPATIAL GIS Certificate of Appreciation (2019) 1st place ACM SIGSPATIAL GIS Cup (2015) IPIN Best Paper Award (2014) His recent publications emphasize geospatial AI, quantum computing for data processing, and environmental monitoring systems. Collaborations with industry partners like DLR highlight his focus on real-world geospatial challenges.

Ganna Gryn'ovaمشاهده پروفایل

پژوهشگر

• Computational Chemistry
• Machine Learning in Chemistry
• Materials Science
• +۳ مورد دیگر

Dr. Ganna (Anya) Gryn'ova is a Principal Investigator at Heidelberg Institute for Theoretical Studies (HITS), specializing in computational chemistry and materials science. Her work bridges machine learning with quantum chemistry to explore reaction mechanisms, molecular stability, and novel material design. Role: Principal Investigator (HITS) Key Affiliation: SIMPLAIX Initiative Research Interests: Dr. Gryn'ova focuses on computational modeling of radical chemistry, noncovalent interactions, and machine learning applications in chemical space exploration. Her studies address reaction barriers, host-guest systems, and sustainable materials for energy and biomedical applications. Quantum chemistry and machine learning integration Radical stability and reactivity in organic systems Design of covalent organic frameworks (COFs) Hydrogen atom transfer in proteins Recent Publication Trends: Her recent work emphasizes AI-driven chemical predictions, interfacial interactions in perovskites, and multiscale modeling of host-guest systems in porous materials. Topics include redox potentials, reaction mechanisms, and noncovalent interactions in graphene and MOF-based technologies. Scientific Recognition: Acknowledged as an Outstanding Reviewer for Chemical Science (2024), highlighting her contributions to peer review and chemical innovation.

• Computer Science
• Electrical Engineering
• Machine Learning
• +۳ مورد دیگر

Xue Jiang is a faculty member at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering, China. Their research focuses on interdisciplinary areas including machine learning, signal processing, biomedical engineering, and cybersecurity. With a prolific publication record, they specialize in neural networks, fault diagnosis systems, federated learning, and brain-computer interfaces. Key research interests include developing intelligent algorithms for fault detection in mechanical systems, advancing medical imaging techniques through deep learning, and enhancing privacy in distributed data frameworks. Their work spans applications in robotics, cybersecurity, and bioinformatics. Notable contributions include adversarial robustness studies for EEG-based systems and PML-based computational methods for wave propagation analysis. Publications highlight innovations across engineering and computer science, with a focus on practical implementations in real-world systems. Collaborations with institutions like the Hong Kong University of Science and Technology and Harbin Institute of Technology reflect their extensive academic network.

• Artificial Intelligence
• Natural Language Processing
• Linked Data
• +۸ مورد دیگر

Professor Kai Eckert holds the position of Professor for Artificial Intelligence at the Department of Computer Science, Mannheim University of Applied Sciences since March 2023. His research focuses on Artificial Intelligence, Natural Language Processing, Linked Data, and their applications in Social Sciences, Information Science, and Digital Humanities. He has contributed to projects like JudaicaLink and Linked Open Citation Database, emphasizing data integration and provenance. Research Interests: His work spans AI-driven solutions for data management, semantic technologies, and domain-specific knowledge bases. Notable areas include RDF validation, metadata modeling (RDF-Star, Named Graphs), and cross-lingual summarization (X-SCITLDR). He explores ethical and technical aspects of data provenance in scholarly communication and library systems. Publications: Over 35+ peer-reviewed works since 2007. Key themes include semantic web applications, metadata provenance (e.g., Dublin Core extensions), and crowdsourced ontology development. Recent trends highlight AI in social networks (cyberbullying detection) and multilingual NLP systems. Grants & Collaboration: Active in EU-funded initiatives and international conferences (e.g., ISWC, ESWC). Developed tools like Semtinel for interactive indexing and ICE-Map for KOS evaluation. Collaborates with institutions like Stuttgart Media University and Springer.

Malte Esdersمشاهده پروفایل

پژوهشگر

• Machine Learning
• Force Fields
• Regularization of ML models
• +۱ مورد دیگر

Malte Esders is a post-doctoral researcher in the Machine Learning group at Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on applying machine learning to physical sciences, including regularization of neural networks for quantum systems, force fields, and graph neural networks. He holds a B.Sc. in Cognitive and Neurobiological Psychology from Utrecht University (2014), an M.Sc. in Computational Neuroscience from the Bernstein Center Berlin (2017), and is completing a Ph.D. in Machine Learning at TU Berlin (expected 2025). His work with BASF on chemical plant simulations addressed challenges in modeling cyclic processes using fixed-point iteration and ML surrogates. Key contributions include developing methods to stabilize ML-based simulations through fine-tuning and exploring industrial applications of machine learning. His interdisciplinary background spans computational neuroscience, quantum systems, and robotics. Beyond research, Esders engages in technical collaborations, such as optimizing machine learning models for chemical engineering processes, and maintains a personal blog exploring topics like meditation, Linux system administration, and scientific experiments (e.g., broccoli sprouting optimization).

Johannes Maeßمشاهده پروفایل

پژوهشگر

• Explainable AI
• Graph Neural Networks
• Computational Neuroscience
• +۲ مورد دیگر

Johannes Maeß is a PhD student in Machine Learning at Technische Universität Berlin and a full-time researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) since 2023. He holds a Master’s in Computer Science from TU Berlin (2020-2022). His research focuses on the theoretical foundations of explainability in neural networks and applications of AI in language education, aiming to bridge theoretical concepts with practical implementations. Education: PhD in Machine Learning, Technische Universität Berlin (2023–present) MSc in Computer Science, Technische Universität Berlin (2020–2022) Industry Background: Amazon Web Services: Low-latency query processing for Redshift IBM: Machine learning-based cardinality estimation in databases Research interests include Explainable AI, Graph Neural Networks, Computational Neuroscience, Probabilistic ML, and Language Education. He contributes to projects like the SchNetPack toolbox for atomistic machine learning.

Julius Martinetzمشاهده پروفایل

پژوهشگر

• Digital Humanities
• Computer Vision
• Education
• +۱ مورد دیگر

Julius Martinetz is a doctoral researcher and research associate at the Machine Learning Group of Technische Universität Berlin. He is pursuing a PhD focused on machine-based visual element detection and clustering in historical sources, as well as graph-based learning for knowledge evolution analysis in educational historical sources. His work is part of the 'The Sphere' project at the Max Planck Institute for the History of Science (MPIWG). Education: B.Sc. in Computer Science, Technische Universität Dresden (2018) M.Sc. in Computer Science, University of Bath, United Kingdom (2021) Research interests include Digital Humanities, Computer Vision, and Machine Learning, with a focus on applying computational methods to historical data analysis and educational knowledge evolution studies. He is affiliated with the Machine Learning Group at TU Berlin and actively contributes to interdisciplinary projects at the MPIWG.

Winfried Ripkenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Graph Neural Networks
• +۴ مورد دیگر

Winfried Ripken is a PhD candidate and research associate at Technical University of Berlin, affiliated with BIFOLD (Berlin Institute for the Foundations of Learning and Data). He pursues doctoral studies in Computer Science under Stefan Chmiela's supervision and participates in the BIFOLD graduate school. His academic background includes: M.Sc. in Computer Science from Hasso Plattner Institute (2021) B.Sc. in Computer Science from TU Chemnitz (2017) Ripken specializes in applying machine learning to natural sciences, with core expertise in Graph Neural Networks, Geometric Deep Learning, and Quantum Chemistry. His research bridges scientific computing and deep learning to solve complex problems like boundary value equations and molecular simulations, emphasizing interpretability and operator learning. Recent publications reveal a strong trend toward scientific machine learning applications, particularly in physics-informed neural networks and image processing. His work demonstrates consistent innovation in adapting graph-based architectures for scientific domains while advancing whitebox methodologies in computer vision. As a doctoral researcher, Ripken has no supervisees but contributes significantly through active GitHub development (26 repositories, 141 stars) and prior industry experience. He previously worked two years as a full-time machine learning researcher at Merantix Momentum, focusing on Graph Neural Networks and interpretability. He operates within Stefan Chmiela's research group at BIFOLD, collaborating on interdisciplinary projects that integrate machine learning with chemistry, physics, and engineering challenges through Berlin's prominent AI research ecosystem.

• Computer Vision
• Medical Imaging
• Deep Learning
• +۴ مورد دیگر

Zhifeng Wang is a Professor at the National University of Defense Technology's School of Computer Science, specializing in computer vision, medical imaging, and artificial intelligence. His research spans multiple application domains with a recent focus on medical image synthesis, 3D reconstruction, and fluid dynamics simulation. Research interests include developing advanced AI techniques for medical imaging applications, particularly in vascular structure analysis and synthesis. His work bridges computer vision with healthcare applications, focusing on creating anatomically accurate 3D representations without invasive procedures. He has made significant contributions to diffusion models, neural radiance fields (NeRFs), and meta-learning frameworks for cross-domain applications. His publication record shows a clear progression from foundational work in graph algorithms and communications systems to cutting-edge medical AI applications. Recent work demonstrates expertise in combining state space models with diffusion techniques for medical image generation, showing particular strength in maintaining anatomical continuity in 3D vascular structures. While specific awards aren't documented in the provided information, his publications in top venues including CVPR 2025 indicate significant recognition in the computer vision community. His collaborative work spans multiple institutions and research domains, suggesting an active research group with diverse funding sources. His laboratory appears to focus on medical imaging AI, with particular emphasis on non-invasive diagnostic techniques. Current projects involve developing sophisticated diffusion models that can generate high-fidelity angiographic images from non-contrast inputs, potentially reducing patient exposure to harmful contrast agents.