Find Academic Supervisors

• Data Science
• Machine Learning
• Distributed Computing
• +4 more

Chris Mattmann is an academic affiliated with the University of Southern California, specializing in data science, machine learning, and high-performance computing. His work spans interdisciplinary applications in Earth sciences, climate modeling, and distributed systems. He has contributed to projects like the Earth System Grid Federation and Apache open-source software. His research emphasizes scalable data management, secure parser development, and automated machine learning systems. Education & Background: While specific educational details aren't listed, his extensive publication record in top-tier journals and conferences indicates advanced training in computer science and computational science. Research Interests: Focus areas include distributed computing infrastructure for scientific applications, machine learning for geosciences, and data-intensive systems. He explores challenges in data movement, scalable supercomputing, and open-source tools for scientific workflows. Publications: Over 150 peer-reviewed articles since 2003, with recent work on super-resolution satellite data, deep learning in geosciences, and secure parser systems. His contributions highlight advancements in both foundational computing and applied environmental science.

Angela LauschView profile

Professor

• Remote Sensing
• Biodiversity Monitoring
• Geodiversity
• +3 more

Angela Lausch is a Professor at the Department of Landscape Ecology , Helmholtz Centre for Environmental Research - UFZ , Leipzig, Germany. Her work focuses on trait ecology , spectral trait analysis for biodiversity and geodiversity monitoring, and integrating remote sensing with data science in social-ecological systems. Key methodologies: hyperspectral imaging, machine learning for land use intensity quantification, semantic data integration. Collaborations: multiple departments at UFZ, international research teams on ecosystem services and climate resilience. Her recent publications emphasize cross-scale environmental modeling , including applications in forest health, soil carbon dynamics, and urban ecological resilience. She serves as Guest Editor for special issues on Remote Sensing of Inland Waters and Forest Health (MDPI). No formal student advising list is provided, but her research groups include teams working on computational landscape ecology and sustainable land use. Email: angela.lausch@ufz.de

Weynants, MelanieView profile

Researcher

• Soil Hydropedology
• Soil Hydraulic Properties
• Pedotransfer Functions
• +5 more

Melanie Weynants is a Researcher at the Max Planck Institute for Biogeochemistry, affiliated with the Department Biogeochemical Integration. She contributes to two research groups: Global Diagnostic Modelling (led by Dr. Martin Jung) and Scalable Spatiotemporal Data Structures and Analytics (SSDSA) (led by Dr. Fabian Gans). Her work is centered on environmental data modeling, soil science, and geospatial analysis. Her research focuses on soil hydropedology, pedotransfer functions, land degradation, and spatiotemporal modeling of Earth systems. She has made significant contributions to the development of pedotransfer functions for soil hydraulic properties and is actively involved in creating data-driven models for assessing climate-related disasters and compound environmental extremes. Her work bridges soil science, remote sensing, and data science to understand and model complex environmental processes. The recent publications highlight a strong trend in leveraging large-scale spatiotemporal datasets and machine learning for environmental forecasting. Her research spans from fundamental soil property estimation to applied modeling of climate impacts on vegetation and compound extreme events, demonstrating a consistent focus on data-intensive environmental science. Her work has been part of significant collaborative efforts, including contributions to the World Atlas of Desertification and the European Hydropedological Data Inventory (EU-HYDI). She has collaborated with major European research institutions such as UCL and the European Commission Joint Research Centre on projects related to soil mapping and land degradation assessment. Melanie Weynants is an active researcher with no indication of part-time status, retirement, or former staff status.

• Database Systems
• Stream Processing
• Internet of Things (IoT)
• +7 more

Steffen Zeuch is a researcher at Humboldt University of Berlin, Germany, with a strong research focus on database systems, stream processing, and Internet of Things (IoT) data management. He is a key contributor to the NebulaStream platform, an extensible, high-performance system for multi-modal edge applications. His work spans query optimization, GPU acceleration, fault tolerance, and distributed state management in stream processing environments. Research interests include database systems, stream processing, IoT data management, query optimization, GPU computing, and hardware-aware execution. His research addresses challenges in real-time analytics, efficient data placement, adaptive compilation, and complex event processing in distributed and edge environments. The publication trends in his recent articles highlight a consistent focus on stream processing systems—especially NebulaStream—with increasing attention to GPU acceleration, adaptive optimization, fault tolerance, and complex event processing. His work bridges theoretical query optimization with practical system implementation, emphasizing performance, scalability, and deployment in real-world IoT and edge infrastructures. Steffen Zeuch has made significant contributions to top-tier venues such as VLDB, SIGMOD, EDBT, and DEBS. His collaborative work, primarily with Volker Markl and other members of the database group at Humboldt University, demonstrates strong research leadership and technical depth in data-intensive systems. He has led and contributed to projects involving system design, performance benchmarking, and real-world deployment of stream processing platforms. His work on tutorials and system demonstrations indicates active engagement in community education and dissemination. While no specific lab or team name is mentioned, his research is centered around the NebulaStream project, a comprehensive platform for managing and analyzing data across fog, edge, and cloud environments. This platform supports complex analytics beyond traditional cloud boundaries, enabling scalable and efficient IoT applications.

• Bioinformatics
• High Performance Computing
• Machine Learning
• +3 more

Felix Kallenborn is an Academic Staff Member at Johannes Gutenberg University Mainz specializing in High Performance Computing, with active research and teaching engagements documented through 2024. His work bridges computational biology and physics through advanced algorithm development. His research focuses on developing context-aware computational methods for genomic sequencing data analysis and physics simulations. Key contributions include the CARE series of bioinformatics tools for error correction and read extension in DNA sequencing, which evolved to incorporate machine learning techniques for improved accuracy. He also applies GPU-accelerated parallel computing to complex physics problems like neutrino oscillation modeling, demonstrating cross-disciplinary expertise in optimizing computational workflows. Analysis of his 2019-2024 publications reveals a strategic evolution from physics-oriented high-performance computing (neutrino simulations) toward bioinformatics dominance, with machine learning integration becoming increasingly prominent. The consistent thread across all works is the development of massively parallel algorithms that leverage hardware acceleration to solve computationally intensive scientific problems, particularly in genomic data processing where accuracy and scalability are critical. He operates within Johannes Gutenberg University Mainz's High Performance Computing research infrastructure, contributing to the development of specialized computational tools that address fundamental challenges in both life sciences and physics through innovative parallel processing approaches.

• Cloud Computing
• Big Data Analytics
• Edge Computing
• +5 more

Ashiq Anjum is Professor of Cloud Computing at the University of Derby's School of Computing and Mathematics. He has established himself as a leading researcher in cloud computing, big data analytics, and related fields with over 145 publications spanning from 2003 to 2025. His research has consistently focused on advancing cloud infrastructure, distributed systems, and data analytics techniques. Professor Anjum's research interests encompass Cloud Computing, Big Data Analytics, Edge Computing, Blockchain, Machine Learning, Video Analytics, Internet of Things, and Distributed Systems. His work bridges theoretical advancements with practical applications, particularly in developing efficient systems for data-intensive computing environments. He has made significant contributions to optimizing cloud resource management, developing novel approaches for blockchain storage, and creating efficient machine learning architectures for edge devices. His recent publications reveal a strong trend toward integrating AI/ML techniques with cloud and edge infrastructure, with particular emphasis on explainable AI, physics-informed neural networks, and energy-efficient computing. His work spans both theoretical foundations and practical implementations across various application domains including healthcare, security, and industrial IoT systems. Professor Anjum has collaborated extensively with researchers worldwide, with notable long-term collaborations with Richard McClatchey, Lu Liu, Richard Hill, and Nick Antonopoulos. His research has appeared in top-tier journals including Future Generation Computer Systems, IEEE Transactions on Cloud Computing, and Journal of Cloud Computing. His research group has developed innovative approaches for video analytics in cloud environments, blockchain-based storage solutions, and edge-enhanced deep learning systems. Current projects appear to focus on digital twin technologies, explainable AI for decision systems, and adaptive resource management for distributed computing environments.

• Building Performance Optimization
• Sustainable Construction Materials
• Building Automation Systems
• +3 more

Prof. Dr. Jan Mehnert is a Professor specializing in building performance optimization, sustainable construction materials, and data-driven building automation systems. His research bridges the gap between theoretical design and operational performance through innovative quality management frameworks and big data analytics. Current work focuses on algorithmic toolkits for technical monitoring, eco-materials like additive manufactured earth, and carbon-neutral campus initiatives. His primary research domains include building performance verification (addressing the design-operation gap), sustainable material systems (earth-based construction and paper composites), and data-intensive building diagnostics (leveraging big data for energy efficiency). Key methodologies involve functional testing protocols, algorithmic performance monitoring, and lifecycle assessment of novel materials. Recent projects demonstrate practical applications in campus sustainability and neighborhood energy systems. Analysis of his 2017-2023 publications reveals three converging trends: (1) Integration of real-time data analytics into building commissioning processes, (2) Development of eco-material systems with validated thermal and structural properties, and (3) Creation of modular toolkits for scalable performance management. His work consistently targets the operational phase of buildings, emphasizing measurable outcomes in energy conservation, indoor environmental quality, and carbon reduction across both new construction and retrofits.

• Knowledge Engineering
• Semantic Web
• Data Infrastructures
• +7 more

Christoph Lange is the Head of the Data Science and Artificial Intelligence department at the Fraunhofer Institute for Applied Information Technology (FIT) and a senior researcher at the Chair of Information Systems 5 (DBIS) at RWTH Aachen University, Germany. He has been affiliated with RWTH Aachen since 2019 and with Fraunhofer since 2014. He received his Ph.D. from Jacobs University Bremen in 2011 for work on enabling collaboration on semiformal mathematical knowledge through semantic web integration. His research focuses on knowledge engineering, data infrastructures, and semantic technologies, particularly aligned with Linked Data and FAIR principles (Findable, Accessible, Interoperable, Reusable). Applications span enterprise data integration, research infrastructures, and cross-organizational data spaces, with contractual R&D conducted for leading German and international automotive and industrial enterprises. His research interests include Knowledge Engineering , Semantic Web , Knowledge Graphs , Ontology Engineering , FAIR Data , and Intelligent Computer Mathematics . He has co-supervised 10 PhD students and taught courses such as the Knowledge Graph Lab at RWTH Aachen. He has organized numerous conferences, workshops, and training events in semantic web and intelligent mathematics domains and contributed to W3C and OMG international standards. Recent publications (2023–2025) demonstrate a strong trend towards integrating Large Language Models with semantic technologies for FAIR data spaces, ontology development, and explainable AI. His work also advances tools for knowledge graph creation (e.g., KGraphX), RDF validation, federated data catalogues, and semantic interoperability in manufacturing and scientific domains. h-index 30 (Google Scholar) Christoph Lange has served as an expert reviewer for international funding bodies and has led impactful collaborations across academia and industry. His work bridges theoretical advances in knowledge representation with real-world applications in data-intensive sectors. He is actively involved in research projects such as NFDI4DS and FAIR Data Spaces, contributing to next-generation data ecosystems. He leads the development of semantic tools and infrastructures, including contributions to Wikidata and CEUR-WS semantification. His lab work includes the Knowledge Graph Lab series, focusing on practical implementation and teaching of semantic technologies. Future work is expected to further explore the synergy between generative AI and formal knowledge representation to enhance data quality, reuse, and machine understanding.

Luise PufahlView profile

Professor

• Business Process Management
• Process Mining
• Operations Research
• +6 more

Prof. Dr. Luise Pufahl is a Professor of Information Systems at the Technical University of Munich (TUM), leading the Information Systems research group at the Heilbronn Campus since 2023. Previously, she served as a visiting professor at the Software and Business Engineering research group in 2022 after completing postdoctoral work at TU Berlin. Her research focuses on business process management, process mining, and sustainability integration in business processes, particularly in healthcare and logistics. She holds a PhD from the Hasso Plattner Institute (University of Potsdam) and has conducted research in the Netherlands and Austria. Her academic career includes studies at the Berlin School of Economics and Law and Humboldt University of Berlin. She specializes in applying operations research, simulation, and machine learning to optimize resource-intensive processes. Notable awards include the Best Demo Award at BPM 2021 and the Runner-up Dissertation Award at BPM 2018. Her work emphasizes regulatory compliance, sustainability, and the practical application of process mining in healthcare systems. Education : PhD: Hasso Plattner Institute, University of Potsdam MSc: Berlin School of Economics and Law & Humboldt University of Berlin Awards : Best Demo Award (BPM 2021) Runner-up Dissertation Award (BPM 2018) Best Paper Award (International Conference on Business Information Systems 2016) Her research addresses challenges in process automation, sustainability-oriented frameworks (e.g., SOPA), and healthcare process optimization. Current projects include SimuBridge for environmental impact simulation and AB testing with reinforcement learning for process improvements.

• High-Performance Computing
• Scientific Workflows
• Distributed Computing
• +14 more

Justin M. Wozniak is a computational scientist at Argonne National Laboratory’s Mathematics and Computer Science Division within the Computing, Environment and Life Sciences directorate. He is a key contributor to advanced scientific workflow systems such as Swift/T and Parsl, enabling scalable, distributed, and many-task computing for data-intensive science. His work supports major initiatives in cancer research (CANDLE), epidemiological modeling, and exascale computing (ExaWorks). He collaborates extensively with leading researchers including Ian T. Foster, Michael Wilde, and Kyle Chard. His research focuses on high-performance computing, scientific workflows, distributed systems, and machine learning applications in science. He has pioneered techniques in workflow automation, fault tolerance, in-situ data analysis, and performance optimization. His work enables robust, scalable execution of complex computational pipelines across heterogeneous environments, from supercomputers to cloud platforms. His recent publications (2021–2025) emphasize workflow interoperability, resilience, benchmarking, and applications in cancer and epidemic modeling. Themes include automated model comparison, job management portability (PSI/J), adaptive workflow steering, and exascale-ready workflow toolkits. These works reflect a strong trend toward reproducibility, scalability, and real-world scientific impact. Justin M. Wozniak has no listed scientific awards in the provided text. However, his leadership in major DOE-funded projects and high-impact publications in top venues (SC, HPDC, e-Science) underscores his significant contributions to computational science. He has mentored or collaborated with numerous researchers, though specific advisees are not listed. His work is supported by large-scale computing grants and initiatives such as the ExaWorks project and CANDLE, which aim to accelerate scientific discovery through advanced computing infrastructure. He contributes to open science through tools like Parsl and Swift/T, which are widely used in the scientific community. He is a core developer in the ExaWorks ecosystem and contributes to workflow frameworks that integrate with AI/ML pipelines, containerization, and real-time data analysis. His work on Braid-DB and provenance tracking supports AI-driven science with full reproducibility. These efforts are central to modern computational laboratories aiming for autonomous, data-intensive discovery.

• High-Performance Computing
• Energy Efficiency
• Parallel Computing
• +3 more

Ananta Tiwari is a researcher specializing in High-Performance Computing (HPC), energy efficiency, and parallel system optimization. His work focuses on optimizing HPC applications, workload management, and resource allocation strategies to enhance both performance and energy efficiency. Tiwari has collaborated extensively with institutions like the University of Maryland, UC San Diego, and Lawrence Livermore National Laboratory through his research activities. Education: PhD in Computer Science, University of Maryland, College Park (2011) Research Interests: Energy-efficient HPC systems Parallel application auto-tuning frameworks Workload characterization and extrapolation Node-sharing and resource pricing models ARM architecture optimization for HPC Key Contributions: Tiwari's research spans energy optimization techniques for large-scale MPI applications, colocation strategies for HPC workloads, and binary instrumentation tools for program analysis. His work on auto-tuning frameworks and multi-objective modeling with machine learning addresses critical challenges in balancing performance, power consumption, and scalability in modern HPC environments.

Patrick MäderView profile

Professor

• Secure Software Development
• Reliable Software Systems
• Machine Learning
• +5 more

Prof. Dr. Patrick Mäder is a University Professor and head of the Group of Data-intensive Systems and Visualization at TU Ilmenau's Department of Computer Science and Automation. He previously served as an Assistant Professor in Software Engineering for Safety-Critical Systems. His career includes doctoral research on software traceability (earning the Thuringian STIFT Prize), a Lise Meitner Fellowship at Johannes Kepler University Linz, and research stays at DePaul University Chicago. Bachelor/Master studies at TU Ilmenau PhD in 2005 on software traceability Postdoc at Johannes Kepler University Linz (2010-2012) Research focuses on secure/reliable software systems, scalable machine learning, explainable AI, computational biology/ecology, and interdisciplinary projects like the award-winning Flora Incognita plant identification app. He has coordinated numerous third-party funded projects in software engineering and machine learning, expanding his team to 18 researchers by 2020. Awards include the 2020 Thuringian Research Prize for applied research and the STIFT Prize for his dissertation on software traceability.

• Laser Plasma Physics
• High-Performance Computing (HPC)
• Computational Physics
• +3 more

René Widera is a researcher at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), specifically within the Laser Particle Acceleration department of the Institute of Radiation Physics. His work focuses on advancing high-performance computing (HPC) techniques for plasma simulations, particularly leveraging GPU architectures and exascale computing frameworks. He contributes to the development and optimization of the PIConGPU code, a leading particle-in-cell (PIC) simulation tool. His research integrates machine learning for real-time data analysis, parallel algorithms for HPC scalability, and cross-platform visualization strategies. Areas of expertise include laser plasma acceleration, high-energy-density physics, and the design of efficient numerical methods for large-scale simulations. He explores hardware-agnostic solutions for computational challenges, including memory access optimizations and DAG-based parallelism. Collaborations involve international HPC initiatives and open-source software projects like openPMD and alpaka . Key projects include the TWEAC initiative to overcome limitations in laser-wakefield acceleration and the development of in-situ visualization pipelines for real-time simulation insights. He also evaluates modern GPU architectures (e.g., AMD, ARM-based systems) for scientific workloads. His contributions bridge theoretical plasma physics with practical computational advancements, aiming to enable next-generation high-intensity laser experiments.

• Media Processes
• Business Process Management
• Process Customization
• +5 more

Thomas Rose is Professor for Media Processes at RWTH Aachen University and heads the research group on business process management at Fraunhofer FIT. He is affiliated with the Department of Computer Science (Informatik 5) and conducts research in media processes, process management, and digital collaboration systems for high-stakes domains such as healthcare and emergency response. His research focuses on the design and implementation of media processes for information capture and dissemination, along with advanced process management and customization techniques. Projects under his leadership include Setric (Security and Trust in Cities), ERMA (Electronic Risk Management Architecture), Olga (Online Guideline Assist for Intensive Care), and ZAMOMO (integration of model-based software and control engineering), all targeting real-world applications in public safety, healthcare, and industrial systems. His work has been recognized at the European level, with the Apnee(-Tu) project highlighted as a success story of European IST research by Commissioner Vivian Reding in 2005. The research group is funded by the B-IT Foundation, a 56 million Euro endowment supporting innovation at the intersection of Bonn and Aachen. Project Apnee(-Tu) selected as one of the success stories of European IST research by Commissioner Vivian Reding in 2005 Thomas Rose has supervised thesis projects and taught courses such as Data Visualization and Analytics and Distributed Ledger Technology. He collaborates extensively with Fraunhofer FIT and leads a research team focused on decision and process management support. His lab is embedded within the Fraunhofer Institute for Applied Information Technology, leveraging interdisciplinary teams to develop scalable, secure, and trustworthy process-aware systems.

• Data Stream Processing
• In-Network Computing
• Industrial Cyber-Physical Systems
• +7 more

Liam Tirpitz is a researcher and PhD candidate at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University. He is affiliated with the Chair of Computer Science i5 (Information Systems and Databases) and the Data Stream Management and Analysis Group, where he has been working since February 2022. Education : Master's degree in Computer Science from RWTH Aachen University (2021). His research focuses on data stream processing, in-network computing, and FAIR data ecosystems, with applications in industrial and cyber-physical systems. He explores methods for efficient data aggregation, stability detection, and cross-organizational data sharing, often leveraging technologies like WebAssembly and knowledge graphs. His work integrates distributed systems principles with database technologies to enable scalable and secure data processing in industrial environments. The recent publications of Liam Tirpitz reflect a strong focus on industrial data management, with trends toward in-network computing, edge-based stream processing, and FAIR principles. His work spans both theoretical architectures (e.g., GALOIS) and applied implementations in domains like manufacturing and health data, demonstrating interdisciplinary collaboration and real-world impact. Liam Tirpitz is actively involved in teaching and academic supervision. He has taught courses such as Data Ecosystems Lab and Implementation of Databases, and is currently supervising multiple thesis projects on topics including WebAssembly-based query functions, anomaly detection, and hybrid stream processing. He participates in research projects like StreamFröst and the Cluster of Excellence Internet of Production, contributing to the development of digital infrastructure for sustainable industrial systems. He is based at the Chair of Computer Science i5, a leading research group in information systems and databases at RWTH Aachen, which supports his work in data-intensive applications and distributed computing environments.