Find Academic Supervisors

• Distributed Systems
• System Security
• Internet Applications
• +4 more

Professor Clemens H. Cap is a Full Professor with tenure and Chair for Information and Communication Services at the University of Rostock, Germany, where he has held his position since 1997. He maintains leadership of the Information and Communication Services research group and has previously held academic appointments at the University of Zurich (including an adjunct professorship until June 2023) and the University of Mannheim. University of Rostock: Full Professor (1997-present) University of Zurich: Adjunct Professor (2004-2023) University of Zurich: Assistant Professor (1992-1995) University of Mannheim: Associate Professor (1994) Professor Cap's research focuses on distributed systems, system security, Internet applications, and social problems in computer science. His work demonstrates an evolution from early research on workstation clusters to contemporary investigations of blockchain technology, classroom response systems, and the social implications of digital technologies. His research interests encompass system architecture for distributed and ubiquitous applications, Internet services, system security, and applications of formal specification techniques. His work has been supported by the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), and the European Union, including participation in the European Fifth Framework Research Program. His recent publications (2014-2022) reveal a strong focus on blockchain applications, security management, and educational technologies, particularly classroom response systems. His research shows consistent thematic development with security, distributed systems, and the social impact of technology as throughlines. Professor Cap regularly gives guest lectures in the Baltic States and Switzerland, demonstrating international engagement. Honorary doctoral degree 'sub auspiciis praesidentis' by Austrian president (1989) Professor Cap is active in academic self-administration as a member of the board of the interdisciplinary research department 'Knowledge - Culture - Transformation' and as Head of the ICT Science Confederation. His teaching portfolio includes Computer Networking, Data Security, Architecture of network services, Methodology of Scientific Research, Software Development Project Distributed Systems, Research Topics in Internet Technology, Computer Science and Society, and Social Impact of IT. He has developed numerous modules that are part of summer schools and international cooperations (BaSoTI). His teaching languages are German and English, and his courses are available for continuous education programs and in-house seminars for companies.

Karsten WolfView profile

Professor

• Theoretical Computer Science
• Formal Methods
• Model Checking
• +3 more

Professor Karsten Wolf holds the Chair of Theoretical Computer Science at the Institute of Computer Science, University of Rostock. He serves as Vice-Rector for Studies and Teaching and holds several key positions including Member of the Senate Commission for Studies, Teaching and Evaluation, and Spokesperson of the specialist group "Petri Nets and Related System Models" of the German Informatics Society. His research focuses on theoretical computer science, particularly in the areas of: Development of correct systems Computer-aided verification Algorithms for Web Services Petri Nets and related system models Professor Wolf's publication record demonstrates consistent expertise in formal verification methods, with recent work emphasizing modular state spaces, temporal logic verification, and approximation techniques. His involvement in the Model Checking Contest (2018-2024) shows his commitment to advancing verification methodologies through empirical evaluation and benchmarking. His professional service includes: Editorial board member of LNCS subseries ToPNOC (Theory of Petri nets and other models of concurrency) Editorial board member of Petri Net Newsletter Member of the German Computer Science Society (GI) Head of the Study Commission of the Faculty Day of Computer Science As Vice-Rector for Studies and Teaching, Professor Wolf plays a critical role in shaping educational policies and academic programs at the University of Rostock, bridging his theoretical expertise with practical educational leadership.

Martin Paul NawrotView profile

Professor

• Neural Information Processing
• Reinforcement Learning
• Synaptic Plasticity
• +7 more

Prof. Dr. Martin Paul Nawrot is a faculty member at the Institute of Zoology , University of Cologne . His research focuses on neural information processing , reinforcement learning , and synaptic plasticity in biological and artificial systems. He also develops large-scale brain simulations to study attractor dynamics for sensory-motor integration, motor control, and decision-making in primates and insects. Current research areas include neuromorphic computing , spiking neural networks , and computational neuroscience Key projects involve modeling insect behavior , neural coding , and cross-species AI applications His recent publications (2023-2025) explore Drosophila larva locomotion , synaptic plasticity mechanisms , and neuromorphic hardware for real-time simulations. These works span neural circuits , behavioral modeling , and computational tools like GeNN and NEST. Despite his academic prominence, no scientific awards are mentioned in the available texts.

Sebastian KempfView profile

Researcher

• Information Extraction
• Table Extraction
• Ontology Generation
• +4 more

Sebastian Kempf is a researcher at the Chair of Computer Science VI (Artificial Intelligence and Knowledge Systems) within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. He has been working at the chair since October 2021, focusing on information extraction, particularly table extraction and analysis, as well as ontology-based knowledge extraction. His research spans Information Extraction , Table Extraction , Ontology Generation , Document Analysis , Data Mining , and Knowledge Modeling . These areas drive his development of systems for transforming unstructured and semi-structured data into actionable knowledge, with applications across scientific, industrial, and humanities domains. Publications reveal a consistent trajectory in applying AI to interdisciplinary challenges—from materials science (glass development) to digital humanities (literary analysis) and scientific document processing. His work emphasizes practical AI solutions that bridge theoretical frameworks with real-world data complexities. Kempf actively contributes to teaching through Software Engineering courses (Summer Semesters 2022-2024), Medical Image Processing (Winter Semester 2024/25), and regular supervision of seminar topics and software internships. His project portfolio includes current work on PlastiKG and past involvement in ESF-BuDanu. As a core member of the Center for Artificial Intelligence and Data Science (CAIDAS), he collaborates across Würzburg's AI research ecosystem, leveraging university-wide resources for ontology-driven data infrastructure and knowledge systems development.

Karin MoraView profile

Researcher

• Ecology
• Climate Change
• Artificial Intelligence
• +7 more

Karin Mora is a postdoctoral researcher at Leipzig University , affiliated with the Faculty of Physics and Earth System Sciences . She leads the DeepFeatures project funded by the European Space Agency and contributes to the proposed Breathing Nature excellence cluster, combining biology, economics, meteorology, and physics to study biodiversity-climate-human interactions. Research Focus : Phenological Rhythms in Ecosystems AI & Mathematical Modeling of Climate Impacts Earth System Data from Satellites and Citizen Science Scientific Contributions : DeepExtremeCubes methodology for climate extremes SpectralIndices.Jl software for remote sensing Plant Trait Coordination and Forest Monitoring Innovations Awards : Forschungspreis (Paderborn University, 2015) Technion Postdoctoral Scholarship (2014-15) Funding for EWM Summer School Course (2013) Education : PhD in Applied Mathematics (University of Bath, 2014) Master of Mathematics (University of Reading, 2008) Outreach : MDR Wissen Radio Contributions Royal Society Summer Science Exhibition Co-organizer of EuropaBON Stakeholder Dashboard

Suren ChilingaryanView profile

Researcher

• High Performance Computing
• Parallel Computing Architectures
• Data Acquisition Systems
• +5 more

Dr. Suren Chilingaryan is a Data Processing and Software Performance Expert at the Institute for Process Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT), where he has been working since 2007. His expertise lies in developing high-performance computing solutions for scientific experiments, with a focus on data acquisition systems, parallel computing architectures, and optimization of image processing software for scientific applications. His educational background includes: PhD in Computer Science (2006) from the Institute for Informatics and Automation Problems of the National Academy of Science of the Republic of Armenia Diploma in Mechanics and Mathematics (2001) from Moscow State University, with thesis on neural networks Dr. Chilingaryan's research spans high-performance computing, parallel hardware architectures, and scientific data management. He specializes in developing efficient algorithms for data acquisition and control systems, particularly for particle physics experiments. His current activities include high-speed data acquisition and control, management of large data, and optimization of image processing software for parallel computing architectures. He has pioneered techniques for GPU-accelerated data processing that enable real-time analysis of massive datasets from experiments like KATRIN (neutrino mass measurement), PANDA, and ASEC (Aragats Space Environmental Center). His recent publications demonstrate a strong focus on neutrino physics, cosmic ray phenomena, and high-speed data acquisition systems. The integration of FPGA and GPU technologies in his research has enabled breakthroughs in real-time data processing for scientific experiments, particularly in synchrotron imaging and particle detection applications. His work bridges computer science and experimental physics, with particular emphasis on developing scalable infrastructure for managing and analyzing large scientific datasets. Dr. Chilingaryan leads development of key software infrastructure including: UFO (Ultra fast X-Ray imaging platform) for smart experiments using GPU computing ADEI (Advanced Data Extraction Infrastructure) for time-series data management High-throughput DAQ Platform for FPGA-based scientific experiments His technical leadership supports major international collaborations including KATRIN, PANDA, and ASEC, providing critical data management solutions that enable new types of scientific investigations across particle physics, astrophysics, and atmospheric science disciplines.

Nicholas Tan JeromeView profile

Researcher

• Scientific data management
• Real-time monitoring
• Low-latency computing
• +5 more

Nicholas Tan Jerome is a Researcher at the Karlsruhe Institute of Technology (KIT), specifically working at the Institute for Process Data Processing and Electronics (IPE). His research focuses on scientific data management, real-time monitoring, low-latency computing, and scientific visualization for large-scale physics experiments and medical imaging applications. Dr. Jerome earned his PhD in Electrical Engineering from KIT in 2019, following an MSc (2010) and Dipl.-Ing. (2009) from University of Applied Science Mannheim. His educational background in electrical and automation engineering provides the foundation for his current research in scientific data systems. His research program addresses critical challenges in managing and visualizing data from large-scale scientific experiments. Dr. Jerome has developed innovative approaches for real-time data processing, low-latency visualization, and scientific data management systems. His work bridges computer science, electrical engineering, and domain-specific applications in physics and medical imaging, with particular focus on applying machine learning to time-series forecasting and causal inference in complex experimental setups. Analysis of his recent publications reveals a strong trajectory in neutrino physics data systems (KATRIN experiment) and advanced visualization frameworks (BORA). His work demonstrates consistent innovation in making scientific data more accessible and interpretable for researchers working with complex experimental setups. Dr. Jerome has received recognition through publications in high-impact journals including Science, Nature Communications, and Physical Review Letters. His collaborative approach is evident in his extensive co-authorship across physics, computer science, and biomedical domains. He has advised or collaborated with numerous researchers across disciplines, contributing to projects that integrate data acquisition, processing, and visualization for scientific discovery. His current work focuses on developing practical tools that help scientists make better, faster decisions from noisy and high-dimensional experimental data. Dr. Jerome leads development of the BORA framework for personalized data display in large-scale experiments and contributes to the KATRIN experiment's scientific data management infrastructure. His technical expertise spans real-time systems, scientific visualization, and machine learning applications for experimental physics.

Christoph MüllerView profile

Professor

• Climate Change Ecology
• Nitrogen Cycle
• Soil Science
• +7 more

Christoph Müller is a Professor of Experimental Plant Ecology at Justus Liebig University Giessen (since 2009) and a Visiting Professor at University College Dublin's School of Biology and Environmental Science (since 2007). His work bridges soil science, plant ecology, and climate change biogeochemistry. PhD in Soil Science (Lincoln University, NZ, 1995) MSc in Soil Water Management (University of Reading, 1991) Diploma in Horticulture (FH, 1990) Research centers on climate change impacts on terrestrial ecosystems , focusing on elemental cycles and trace gas production in soils. He pioneered 15N/13C isotope tracing to quantify gross nitrogen transformations and developed Ntrace software for analyzing these processes. His landmark Giessen FACE experiment (20+ years) demonstrates how elevated CO2 alters rhizosphere microbiomes and nutrient dynamics. Recent publications (2021-2023) reveal mechanisms linking soil clay minerals to nitrogen cycling, quantify DNRA patterns across ecosystems, and explore biochar-nitrapyrin synergies for emission reduction. Grants from EPA Ireland and Department of Agriculture Ireland support his work on greenhouse gas mitigation and sustainable fertilization . Collaborations span institutions like UCD, with co-authors including Gerald Moser , Daniela Senkbeil , and Lea Meng . Current editorial roles involve European Journal of Soil Science and Frontiers in Terrestrial Microbiology . His methodology integrates stable isotope tracing , metabarcoding , and process-based modeling to unravel soil-plant-microbe interactions.

Dominik WalterView profile

Researcher

• Hardware-Software Co-Design
• Processor Arrays
• Embedded Systems
• +3 more

Dominik Walter is a Researcher at the Department of Computer Science 12 (Hardware-Software Co-Design) at the University of Erlangen-Nuremberg (FAU), where he has been working since 2019. His research focuses on innovative approaches to hardware-software integration, particularly in the domain of processor array architectures and embedded systems design. Walter completed his educational journey at the University of Augsburg, earning a B.Sc. in Computer Science and Multimedia (2013-2016) followed by an M.Sc. in Computer Science (2016-2019). This strong foundation in both theoretical and applied computer science has informed his current research direction. His primary research interests center around Hardware-Software Co-Design, with particular expertise in Tightly Coupled Processor Arrays and Invasive Computing paradigms. Walter's work explores how to optimize resource allocation in parallel computing environments, improve energy efficiency in embedded systems, and develop novel approaches for real-time data processing. His research bridges theoretical computer architecture concepts with practical implementation challenges. Walter's publication record from 2020-2025 reveals a consistent research trajectory focused on processor array architectures and their applications. His work spans both theoretical foundations and practical implementations, with contributions to sustainable computing, real-time systems, and hardware acceleration techniques. A notable pattern in his research is the application of invasive computing principles to solve complex parallel processing challenges across various domains. As a researcher at FAU's Department of Computer Science 12, Walter is part of a vibrant research community focused on cutting-edge hardware-software integration problems. His work contributes to the department's reputation in processor architecture and embedded systems design, with potential applications in high-performance computing, embedded AI, and energy-efficient computing systems.

Yarema OkhrinView profile

Professor

• Statistics
• Data Science
• Machine Learning
• +7 more

Prof. Dr. Yarema Okhrin serves as Full Professor at the Department of Statistics and Data Science within the Faculty of Economics at the University of Augsburg. He leads the Chair of Statistics and Data Science, which is part of the Business Analytics & Operations and Finance, Accounting, Controlling & Taxation clusters. His research group includes Dr. Sebastian Heiden, Dr. Rui Ren, and several research assistants working on advanced statistical methods and their applications in business and economics. Professor Okhrin's research spans mathematical and statistical models for data-driven problems in business and economics, with particular expertise in statistics, data science, and machine learning. His work focuses on developing methodologies for time series analysis, portfolio optimization, risk management, and statistical process control. Recent research has expanded into high-dimensional statistics, image analysis, and network monitoring applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary applications of statistical methods, particularly in finance and process monitoring. His work demonstrates increasing integration of machine learning techniques with traditional statistical approaches, especially in portfolio optimization and risk assessment. A notable pattern is the development of specialized statistical methods for high-dimensional data analysis across multiple domains. Professor Okhrin supervises bachelor's and master's theses at the Department of Statistics, with research topics including data mining, time series modeling, forecasting methods, regression analysis, and statistical data analysis. His department offers thesis opportunities in specialized areas such as asymmetric dependencies in financial markets, forecasting, investor sentiment analysis, medical statistics, volatility modeling, multivariate distributions, sustainable investing, and portfolio optimization. The Chair of Statistics maintains active research collaborations and offers academic consulting on statistical issues and data analysis. The department hosts regular research seminars to facilitate exchange of ongoing research projects and maintains connections with industry partners for practical applications of statistical methods.

André NiemannView profile

Professor

• Hydraulic Engineering
• Water Management
• Flood Protection
• +6 more

Professor André Niemann at the University of Duisburg-Essen's Institute of Hydraulic Engineering and Water Management is a leading expert in water resources management, focusing on flood protection, dam control systems, and AI-driven hydrological forecasting. His work bridges practical engineering challenges with advanced data science applications. Academic Leadership: Coordinated projects like interSim (interactive simulation for vocational training) and PROWAVE (forecast-based dam control) Research Impact: Pioneered ensemble optimization methods for reservoirs and LSTM models for inflow forecasting Technological Innovation: Developed AI frameworks for sensor data quality control in water management His research addresses critical intersections between hydraulic engineering and climate resilience, with recent projects analyzing flood forecasting systems ( HÜProS ), urban drainage optimization, and sustainable hydropower solutions using legacy mining infrastructure. Collaborations span institutions like Harz Waterworks, Deltares, and international conferences (IAHR, ICOLD, EGU). Publications since 2012 cover topics from underground pumped storage feasibility to real-time control of urban reservoirs, with a growing emphasis on machine learning applications since 2023. He actively engages in fieldwork, including excursions to dams and control centers, and teaches modules ranging from hydromechanics to environmental monitoring.

• Natural Language Processing
• Symbolic AI
• Autonomous Driving
• +1 more

André Schamschurko is a Researcher at the Technical University of Munich's Chair of Robotics, Artificial Intelligence, and Real-time Systems, having joined in July 2024. His work bridges artificial intelligence and autonomous systems development, with a focus on practical applications in robotics and real-time computing environments. He earned a Diploma in Computer Science from TU Dresden, specializing in Natural Language Processing and Symbolic AI during his academic training. This foundational expertise shapes his current research trajectory in cognitive systems and AI-driven solutions. His research concentrates on Natural Language Processing, Symbolic AI, Autonomous Driving, and Large Language Models, with particular emphasis on leveraging LLMs for safety-critical validation in autonomous vehicle systems. This includes developing novel testing frameworks that generate complex driving scenarios through language model interactions. His publication on LLM-driven testing demonstrates a clear research trend toward integrating language models with robotics for enhanced scenario validation, reflecting broader industry movements toward AI-assisted development in autonomous systems. This work addresses critical gaps in safety verification for self-driving technologies. As part of Prof. Alois Knoll's research team, he contributes to the Chair's mission of advancing real-time AI systems, robotics perception, and computational intelligence. The group maintains strong industry partnerships focused on autonomous vehicle development and validation frameworks. Current teaching responsibilities include assisting in 'Cognitive Systems' and 'Advanced Foundation and Perception Models in Autonomous Driving' courses, though no student advisement or grant information is publicly documented.

• Digital Humanities
• Computational Humanities
• Text Mining
• +10 more

Manuel Burghardt is a full-time Professor of Computational Humanities at the Institute of Computer Science , Leipzig University . He coordinates the Bachelor's and Master's programs in Digital Humanities and serves as a spokesperson for the GI's Computer Science and Digital Humanities Group and the Forum for Digital Humanities Leipzig . PhD in Information Science (summa cum laude) from University of Regensburg Magisterstudium in Information Science, English Linguistics, General Linguistics, and Corpus Linguistics at University of Regensburg Burghardt's research spans multiple Computational Humanities domains: Digital Environmental Humanities : Integrating Computational Literary Studies with Biodiversity Research via NLP and Information Retrieval Immersive Humanities : Applying AR/VR/XR, Eye Tracking, and Tangible Interfaces for Digital Humanities Text Mining & NLP : Focused on text reuse detection, similarity analysis, and sentiment modeling Video Analytics : Analyzing news videos and cinematic media through Distant Viewing techniques Theory of Digital Humanities : Investigating methodological foundations and scientometric trends Computational Game Studies : Multimodal empirical analysis of games Computational Spatial Humanities : Spatial data analysis in humanities contexts His recent publications demonstrate strong focus on: Improving OCR for historical documents (2015-2020) Computational drama analysis using sentiment and text mining techniques (2016-2019) Music information retrieval applications in folk song and manuscript analysis (2015-2019) Development of specialized tools for humanities data processing

Joachim BaumeisterView profile

Professor

• Semantic Information Systems
• Knowledge Graphs
• Deep Learning
• +5 more

Joachim Baumeister is a Professor at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. While his primary employment since September 2010 has been at denkbares GmbH, a company specializing in knowledge-based systems, he continues to regularly give lectures at the university. His research focuses on Semantic Information Systems, Knowledge Graphs, Deep Learning applications, Natural Language Processing, and Knowledge-based Configuration for Industry 4.0. Professor Baumeister's work bridges theoretical AI research with practical industry applications, particularly in knowledge-based configuration systems and semantic technologies. His recent publications (2020-2024) reveal a strong emphasis on product configuration systems, semantic knowledge representation, regulatory document processing, and knowledge-based systems. His research has evolved from foundational work on semantic wikis and knowledge engineering to more recent applications involving deep learning and large language models, demonstrating adaptability to emerging technologies while maintaining focus on practical knowledge representation problems. Professor Baumeister's work demonstrates significant contributions to case-based reasoning, knowledge configuration, and semantic technologies, with applications spanning regulatory compliance, industrial configuration systems, and document processing. His current research areas include: Semantic Information Systems and Knowledge Graphs Deep Learning for Image Recognition and Language Understanding Knowledge-based Configuration for Industry 4.0 Natural Language Processing Intelligent Personal Assistants and Chat Bots Though specific students aren't listed in the provided information, Professor Baumeister actively invites students to contact him regarding projects, bachelor theses, and master theses in his areas of expertise. His work at denkbares GmbH focuses on the design, implementation, and evolution of knowledge-based systems and semantic information systems.

Benjamin C. PierceView profile

Professor

• Programming Languages
• Type Systems
• Language-based Security
• +5 more

Benjamin C. Pierce is the Henry Salvatori Professor of Computer and Information Science in the School of Engineering and Applied Science at the University of Pennsylvania. As a Fellow of the ACM, he has made significant contributions to programming language theory and formal methods. His academic leadership includes previous editorial roles as co-Editor in Chief of the Journal of Functional Programming and Managing Editor for Logical Methods in Computer Science. His research spans multiple interconnected domains in programming language theory, with particular emphasis on type systems and their applications to security and verification. Pierce's work bridges theoretical foundations with practical implementations, most notably through his development of the Unison file synchronization tool and contributions to the Clowdr virtual conference platform. His research interests form a cohesive trajectory from foundational type theory to applied security and verification techniques. Pierce's scholarly output shows consistent focus on property-based testing, type systems, and formal verification methods. His recent publications demonstrate evolving interests in differential privacy verification, synchronization technologies, and the practical challenges of implementing formal methods in real-world systems. The progression of his work reflects both theoretical depth and practical relevance to software development challenges. Fellow of the ACM Author of influential textbooks Types and Programming Languages and Software Foundations Lead designer of the Unison file synchronizer Co-developer of the Clowdr virtual conference platform Former editorial leadership for multiple prominent programming languages journals As an educator and mentor, Pierce has contributed to the Programming Languages Mentoring Workshop (PLMW) and has served on numerous conference program committees. His academic service extends to SIGPLAN leadership roles including SIGPLAN Vice Chair and Steering Committee membership. His textbook Software Foundations has become a standard resource for teaching formal methods and proof assistants.