Find Academic Supervisors

• Real-Time Control Systems
• Cloud Computing
• Automation of Large-Scale Systems
• +7 more

Johan Eker is a Professor in Real-Time Control Systems at Lund University's Faculty of Engineering and a Principal Researcher in Cloud Technology at Ericsson Research. He holds a dual affiliation, actively contributing to both academic and industrial research in the domain of control systems and cloud automation. His research interests lie at the intersection of computer science and control theory, with a focus on the automation of large-scale software systems such as cloud services and telecommunications networks. He leverages machine learning and advanced control methodologies to develop intelligent, adaptive, and scalable systems. His work benefits from close collaboration with Ericsson Research, enabling access to real-world data and operational environments for validation and innovation. Johan Eker is involved in cutting-edge research areas including autonomous cloud systems, event-based control, and learning-based control architectures. His contributions support the evolution of resilient, efficient, and self-managing infrastructures in modern computing and networking environments. He is actively engaged in research collaboration between academia and industry, bridging theoretical advancements with practical deployment. His work is supported through partnerships with Ericsson, allowing for real-world testing and implementation of control solutions in cloud and telecom systems. Johan Eker is affiliated with the Department of Control Engineering at Lund University, where his expertise contributes to education and research in systems, control, and real-time computing. He plays a key role in advancing the integration of control theory into modern software and networked systems.

Michael NothnagelView profile

Professor

• Statistical Genetics
• Genetic Epidemiology
• Population Genetics
• +5 more

Michael Nothnagel is a Professor at the University of Cologne, where he leads the Department of Statistical Genetics and Bioinformatics within the Cologne Center for Genomics (CCG). His work spans statistical genetics, genetic epidemiology, and forensic genetics, focusing on methodological development and large-scale genomic data analysis. His research interests encompass theoretical and applied statistical genetics, with emphasis on human genetic diversity, disease etiology, and forensic applications. Key areas include Y-chromosomal phylogeography, genome-wide association studies for complex diseases, development of statistical methods for variant interpretation, and forensic marker optimization. His group leverages next-generation sequencing data and specialized forensic markers to address questions in population history, disease mechanisms, and identification systems. Recent publications reveal a strong focus on computational approaches to genetic analysis, including spatial frequency interpolation for haplogroup mapping, polygenic risk score applications for behavioral traits, and advanced methods for variant classification. His work demonstrates consistent integration of statistical theory with practical applications in medical and forensic genetics, often through international collaborations like the VISAGE Consortium. Nothnagel maintains active involvement in the Cologne Center for Genomics, contributing to seminars and collaborative projects including the upcoming 34th International Genetic Epidemiology Society meeting. His research group operates at the intersection of computational biology and medicine, with particular strengths in handling complex genomic datasets and developing novel analytical frameworks for genetic epidemiology.

• Algorithms for Big Data
• Parallel Computing
• External-Memory Algorithms
• +5 more

Ulrich Meyer is a Professor at the Institute for Computer Science at Goethe University Frankfurt. He serves as a prominent researcher in algorithms for big data, with extensive contributions to parallel and external-memory graph algorithms. His work spans theoretical foundations and practical implementations for processing large-scale data sets. Spokesperson of the DFG priority program (SPP 1736) on Algorithms for Big Data in Germany SEA23 Symposium on Experimental Algorithms, Steering Committee Chair ALENEX23 Algorithm Engineering and Experiments, Program Committee Member Professor Meyer's research interests focus on the theoretical and experimental aspects of processing large data sets on advanced computational models. His work particularly emphasizes parallel and external-memory graph algorithms, with recent focus on efficient large-scale network generation according to various stochastic models. His research has produced significant contributions including the parallel Delta-Stepping algorithm (which received the ESA Test of Time Award in 2019) and the first BFS approach with sublinear I/O. He has also explored more specialized topics like energy-efficient sorting (with records in the JouleSort competition 2009/10 and the Germany Land of Ideas Award) and fragile computing (which earned him a best-paper award at ESA 2019). His recent publications demonstrate a strong focus on graph algorithms, network generation, and parallel computing techniques. The research trends show consistent advancement in scalable algorithms for massive graphs, with particular emphasis on efficient sampling methods, shortcutting techniques, and communication-free distributed approaches. His work bridges theoretical computer science with practical engineering considerations for real-world big data applications. ESA Test of Time Award 2019 for Parallel Delta-Stepping algorithm Records in the JouleSort competition 2009/10 Germany Land of Ideas Award Best-paper award at ESA 2019 for fragile computing research Professor Meyer has made substantial contributions to the academic community through his leadership in the DFG priority program on Algorithms for Big Data, which has fostered significant research collaborations across Germany. His extensive publication record in top venues demonstrates sustained research productivity and impact in the algorithms community. While specific grant details aren't provided in the text, his role as spokesperson for a major DFG priority program indicates substantial research funding and leadership responsibilities. His work appears to be conducted within collaborative research environments focused on algorithm engineering and experimental evaluation. His research appears to be conducted within the Institute for Computer Science at Goethe University Frankfurt, likely involving collaborations with other researchers in the Algorithms for Big Data priority program. The extensive list of co-authored publications suggests active participation in research teams focused on parallel algorithms, graph processing, and network generation.

Daniel KasperView profile

Researcher

• Evaluation of Education Systems
• Primary School Research
• Educational Disparities
• +4 more

Daniel Kasper is a researcher at the University of Hamburg's Faculty of Education, specializing in Educational Science with a focus on International Educational Monitoring and Reporting. He currently serves as the National Project Manager for TIMSS 2027, continuing his leadership role from previous TIMSS cycles (2023, 2019). His work centers on large-scale educational assessments, statistical methodology, and primary education research. Dr. Kasper completed his Habilitation in Educational Science with special consideration of empirical educational research at TU Dortmund (2012-2020), followed by his PhD in Educational Science at the same institution (2010-2012). He earned his Diploma in Educational Science from the University of Münster (2001-2007). His research interests span evaluation of education systems, primary school research, educational disparities, and advanced statistical methods in education. He has developed significant expertise in TIMSS methodology, multilevel modeling, and analysis of large-scale assessment data, with numerous publications addressing methodological challenges in international comparative studies. Analysis of his 15 most recent publications reveals a strong focus on TIMSS methodology and results, particularly regarding mathematics and science competencies in primary education. His work demonstrates expertise in statistical methodology for educational assessment, with several publications developing and refining analytical techniques for large-scale data. A recurring theme is examining educational disparities related to student composition, socioeconomic factors, and gender differences. Dr. Kasper has served as National Project Manager for multiple TIMSS cycles (2019, 2023, 2027) and has been involved in PIRLS studies, demonstrating sustained leadership in major international educational assessments. His work bridges methodological innovation with practical application in educational monitoring. He teaches courses across all academic levels, including 'Introduction to Empirical Research Methods' for Bachelor students, 'Methods of Empirical Educational Research' for Master students, and advanced workshops on longitudinal scaling and the Rasch model for doctoral candidates. His teaching reflects his dual expertise in educational research methodology and statistical analysis.

• Theoretical Neuroscience
• Statistical Physics of Neural Networks
• Dynamics of Neuronal Networks
• +6 more

Prof. Dr. Moritz Helias is a University Professor and leads the Theory of Multi-Scale Neuronal Networks group at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience, Forschungszentrum Jülich. His research bridges biological and artificial neural networks, focusing on dynamics, information processing, and the physics of AI. The group is part of a larger interdisciplinary institute that integrates theory, simulation, and data analysis to understand the brain. Institution: Forschungszentrum Jülich School: Institute for Advanced Simulation Department: IAS-6, Computational and Systems Neuroscience Position: Professor and Group Leader Email: m.helias@fz-juelich.de His research interests lie at the intersection of statistical physics and neuroscience. He investigates how structure shapes dynamics in both biological and artificial networks, aiming to uncover general principles of information processing. Using methods from statistical physics, his work enables a unified framework for understanding collective phenomena, learning, and generalization. Key areas include spiking neural networks, renormalized field theory, and the theoretical foundations of AI. The recent publications reflect a strong trend toward multi-scale modeling of neural systems, integrating statistical physics with neuroscience. Topics include spiking network dynamics, mean-field theory, renormalization, and applications of machine learning in physics. The work spans biological realism and artificial intelligence, with implications for neuromorphic computing and brain-inspired AI architectures. While no scientific awards are listed in the provided texts, his group actively contributes to open science through tools like NEST and theoretical frameworks that influence both neuroscience and AI. Prof. Helias supervises a research group focused on theoretical and computational approaches, contributing to collaborative projects involving large-scale simulations and data analysis. His team works closely with experimentalists and theorists to validate models and advance understanding of brain function. The group is also involved in developing simulation technologies and theoretical tools that support reproducible neuroscience. The Theory of Multi-Scale Neuronal Networks group is embedded within a vibrant research environment at IAS-6, collaborating with teams in statistical neuroscience, computational neurophysics, and future simulation architectures. This fosters a loop between data, theory, and simulation, enabling cutting-edge research on brain function and artificial intelligence.

• Hydrology
• Climate Change Impact Assessment
• Drought Monitoring
• +2 more

Ehsan Modiri is a researcher at the Department of Hydrosystem Modelling , Helmholtz Centre for Environmental Research (UFZ), Germany. His work focuses on climate change impacts on hydrological systems, drought monitoring, and environmental modeling using advanced computational frameworks. Affiliation: UFZ - Helmholtz Centre for Environmental Research Department: Hydrosystem Modelling Research Themes: Climate Change, Droughts, Hydrological Forecasting, Water Resource Management Research Interests: Modiri specializes in understanding hydrological responses to climate change, particularly in drought dynamics and soil moisture variability. His work bridges observational data with sophisticated modeling frameworks to improve predictability of water balance components under warming scenarios. Scientific Contributions: Recent publications highlight his role in developing high-resolution drought simulations, evaluating hydrological model performance, and analyzing groundwater responses to global warming. He participates in large-scale European hydrological projects and collaborates on climate-hydrology integration initiatives.

• Database Systems
• Stream Processing
• Distributed Data Management
• +5 more

Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.

Bernd MöllerView profile

Professor

• Sustainable Energy Systems
• Renewable Energy (Wind, Solar, Biomass)
• Geographical Information Systems (GIS)
• +3 more

Prof. Dr. Bernd Möller is a faculty member at Europa-Universität Flensburg, specializing in sustainable energy systems management for developing countries. His research focuses on renewable energy integration, spatial analysis, and environmental planning, leveraging GIS tools for energy system transformation. Current position: Professor at Department Energy and Environmental Management Alumni of Flensburg University of Applied Sciences and Aalborg University (PhD in Energy Planning) Research interests encompass wind/solar/biomass systems, energy atlas development, and climate-resilient infrastructure. His work bridges technology, economics, and spatial planning in renewable transitions. Publications highlight trends in offshore wind modeling, biomass logistics, district heating expansion, and GIS-driven energy solutions. Key themes include decarbonization, resource constraints, and regional energy equity. Teaching involves applied GIS science, energy planning frameworks, and development cooperation. He leads the PhD program in Energy and Environmental Management for Developing Countries.

• Network and Service Management
• Cybersecurity
• Distributed Systems
• +5 more

Aiko Pras is a Professor at the University of Twente in Enschede, Netherlands. His research spans network and service management, cybersecurity, and distributed systems, with a focus on DNS security, DDoS mitigation, IPv6, and software-defined networking. Research Interests: Network and Service Management Cybersecurity and DDoS Mitigation DNS Security and IPv6 Software-Defined Networking (SDN) Internet Measurement and Performance Critical Infrastructure Protection (ICS/SCADA) Article Trends: Pras's recent work emphasizes collaborative DDoS defense, DNS security, and IPv6 vulnerabilities. His studies often involve large-scale measurements and propose practical solutions for internet resilience and security. Collaborations: He frequently collaborates with Anna Sperotto, Roland van Rijswijk-Deij, and other experts in network security and measurement.

• Database Systems
• Big Data Analytics
• Cloud Database Systems
• +5 more

Michael J. Franklin is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He has a prolific publication record spanning over three decades with more than 300 publications in top-tier database and systems conferences and journals, demonstrating his continued active research and leadership in the field. Franklin's research spans multiple areas within data management, with a recent focus on time-series analysis, AI-integrated database systems, cloud-native databases, and data quality. His work has evolved from traditional database systems to address modern challenges in big data, machine learning integration, and distributed systems. He has made significant contributions to data cleaning, crowdsourced data management, and stream processing systems. Analysis of his recent publications (2022-2025) reveals a strong trend toward integrating AI/ML capabilities with database systems, particularly in time-series anomaly detection, LLM applications for data management, and resource-adaptive query processing for cloud environments. His work increasingly focuses on practical systems that address real-world data challenges, often involving collaborations with industry partners and other leading academic researchers. Throughout his career, Franklin has mentored numerous PhD students who have become prominent researchers in their own right, including Sanjay Krishnan, Aaron Elmore, and Jiannan Wang. His collaborative research has frequently involved significant funding from NSF and industry partnerships, enabling large-scale systems research with real-world impact. Franklin leads research efforts that bridge theoretical database principles with practical system implementations. His work on projects like Data Station demonstrates his commitment to building trustworthy infrastructure for data sharing and analysis, addressing critical challenges in data privacy, security, and usability in collaborative environments.

• Information Systems
• Databases
• Knowledge Graphs
• +7 more

Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.

Cristiano PorcianiView profile

Professor

• Cosmology
• Large-scale Structure
• Galaxy Formation
• +2 more

Cristiano Porciani is Professor of Astrophysics at the University of Bonn's Argelander Institute for Astronomy, specializing in cosmological structure formation and galaxy evolution. He leads a research group working on numerical simulations of large-scale structure and theoretical cosmology. His research focuses on dark matter distribution, galaxy bias, and cosmological parameter estimation using perturbation theory and high-performance computing. Recent work examines relativistic effects in large-scale structure and intensity mapping techniques. Publications show strong emphasis on Euclid mission science, including instrument characterization, survey simulations, and cosmological tests. Article trends reveal consistent development of statistical methods for analyzing next-generation sky surveys. Supervises 9 graduate students working on cosmological simulations, galaxy clustering statistics, and radiative transfer modeling. Leads research projects within the Euclid Consortium and Transregional Collaborative Research Centre.

Matteo Große-KampmannView profile

Professor

• Information Security
• Cybersecurity
• Distributed Systems
• +12 more

Prof. Dr. Matteo Große-Kampmann is a faculty member at Hochschule Rhein-Waal, serving as Professor of Distributed Systems within the Faculty of Communication and Environment. His research and teaching are centered on building secure, resilient, and reliable digital systems, with a strong emphasis on integrating information security from the earliest stages of system design. He is based at the Kamp-Lintfort Campus and actively leads research in the Cloud Resilience Lab. His research interests span a wide range of cybersecurity domains, including information security awareness, healthcare IT security, mobile and 5G/6G network security, threat modeling, and privacy in smart devices. He advocates for a proactive, design-first approach to security, particularly in increasingly interconnected environments. His work combines technical depth with human factors, examining both system-level vulnerabilities and user behavior in cyber risk contexts. The recent publications reflect a strong focus on applied cybersecurity research, with trends in mobile network penetration testing, privacy in wearables, governmental cybersecurity communication, and security in healthcare and childcare technologies. His work frequently appears in top-tier venues such as DSN, PETS, ESORICS, and ACSAC, often in collaboration with students and international researchers. His scientific contributions have been recognized with awards including an Honorable Mention Award at the International Conference on Mobile and Ubiquitous Multimedia (2024) and a Best Paper Candidate at the ACM Web Conference 2022. He also contributes to the academic community as a reviewer and technical program committee member for major security conferences including NDSS, PETS, ESORICS, and ACSAC. Prof. Große-Kampmann actively supervises bachelor's and master's theses, encouraging students to explore topics such as post-Darknet marketplaces, AI in cybersecurity education, and flood of information challenges. He emphasizes ownership, preparedness, and learning through failure, fostering independent research skills. He collaborates with students and industry partners on practical projects, particularly in the areas of penetration testing and security analysis. He is involved in several research initiatives, most notably the Cloud Resilience Lab , where he and his team investigate real-world security and privacy issues in modern digital systems. His work bridges academic research with practical applications, often receiving media attention, such as coverage in Wired , EFF , and Die Zeit for his study on childcare app security.

Thomas NeumannView profile

Professor

• Database Systems
• Query Optimization
• Query Processing
• +2 more

Thomas Neumann is a full-time Professor at the Department of Database Systems within the TUM School of Computation, Information and Technology at the Technical University of Munich . Research Focus : Query optimization and processing in database systems Hardware-aware database engine design Scalable RDF/semantic data management Hybrid OLTP/OLAP processing Join optimization algorithms Scientific Recognition : Gottfried Wilhelm Leibniz Prize (2020), Germany's most prestigious research award Academic Contributions : His recent publications focus on high-performance query execution strategies, hybrid transactional/analytical systems using virtual memory snapshots, and scalable graph processing techniques. These works demonstrate consistent advancements in database engine optimization for modern hardware architectures and large-scale semantic data management.

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.