Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.
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.
Dr. Hans Oechsner is a leading academic in agricultural engineering and bioenergy, holding multiple leadership positions including Head of the State Institute for Agricultural Engineering and Bioenergy (Landesanstalt für Agrartechnik und Bioenergie), Managing Director of the Baden-Württemberg Working Group on Agricultural Engineering and Rural Construction (ALB), and Project Leader for the International Graduate College focused on maize-based agricultural systems adaptation to limited phosphate reserves. He teaches advanced courses on biogas technology and bioenergy infrastructure at the Department of Agricultural Engineering in the Tropics and Subtropics. His research centers on optimizing bioenergy systems and sustainable resource management, with key interests in: Advanced biogas production techniques (e.g., mechanical pretreatment, two-stage digestion) Phosphorus recovery from organic waste streams Valorization of agricultural residues and livestock manure Development of circular bioeconomy models for crop systems Dr. Oechsner's recent publications (2022-2025) demonstrate a strong focus on enhancing biogas plant efficiency, nutrient recycling from digestate, and sustainable biomass utilization. His work consistently integrates full-scale applications with fundamental research, particularly in pretreatment optimization and waste stream valorization. He leads research infrastructure at the State Institute for Agricultural Engineering and Bioenergy, coordinating teams working on large-scale biogas optimization and agricultural waste management. His projects frequently involve international collaborations and industry partnerships.
Val Tannen is a Professor at the University of Pennsylvania, specializing in database systems, provenance analysis, and programming languages. His research focuses on data management, query languages, and systems like DBSP and ORCHESTRA. Collaborations include work with co-authors such as Zachary Ives, Susan Davidson, and Todd Green. Key research interests include provenance for databases, incremental view maintenance, and data integration. His work bridges theoretical foundations and practical applications in systems like DBSP for stream processing and ORCHESTRA for collaborative data sharing. Publications span provenance frameworks, query optimization, and distributed systems. While no awards are explicitly listed, his contributions to database theory and systems are widely recognized.
Claudius Gros is a Professor of Theoretical Physics at Goethe University Frankfurt. He holds a PhD from ETH Zurich and has held academic positions at Indiana University, University of Dortmund, and Saarland University. His research focuses on complex systems theory, physics of AI, self-organized robotics, and the Genesis Project, an interstellar mission concept for establishing life on exoplanets. His work bridges theoretical physics with interdisciplinary applications, including epidemiology modeling and societal dynamics analysis. Key contributions include the textbook Complex and Adaptive Dynamical Systems (Springer) and foundational studies on attention mechanisms in AI architectures. Education: Bachelor/Master: ETH Zurich, Theoretical Condensed Matter Physics PhD: ETH Zurich, 1985 (Advisor: T. Maurice Rice) Postdoc: Indiana University, 1988–1990 (With Steve Girvin and Allan MacDonald) Research Interests: Physics of AI : Analysis of transformer models, attention mechanisms, and neural scaling laws. Complex Systems : Epidemic models, dormancy dynamics in cellular automata (Spore Life), and self-organized robotics. Genesis Project : Feasibility of interstellar probes to seed life on exoplanets, magnetic sail deceleration. Societal Dynamics : Strategy condensation, envy-driven class stratification, and pandemic policy modeling. Articles Overview: Recent work spans AI physics (attention mechanisms, neural scaling), complex systems (epidemic oscillations, dormancy models), and robotics (self-organization principles). Themes include theoretical frameworks for embodied systems, computational models of societal behavior, and interdisciplinary applications of dynamical systems theory. Advising & Grants: Claudius Gros has advised multiple researchers, with co-authored papers featuring collaborators like O. Neumann, D.H. Nevermann, and B. Sandor. His grants include funding for Genesis Project studies and robotics research. Labs & Teams: His research group focuses on Physics of AI and Self-Organized Robotics , with active projects on embodied robots, neural network dynamics, and interstellar mission feasibility.
Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University working with 5G and Internet evolution. She completed her PhD in computer science in 2017 at the Simula Research Lab and Universitetet i Oslo under the supervision of Dr. Ozgu Alay and Prof. Michael Welzl. Her PhD dissertation focused on increasing robustness in multipath transport with MPTCP. Dr. Ferlin's educational background includes a PhD in Computer Science from the Simula Research Lab and Universitetet i Oslo (2017). Her doctoral research centered on enhancing robustness in multipath transport protocols, specifically focusing on MPTCP (Multipath TCP). She also completed undergraduate work that contributed to a book project with Prof. Friedrich Oehme on electronics and circuit technology. Dr. Ferlin's research spans multiple domains at the intersection of networking, systems, and performance engineering. Her primary interests include network and system measurements, performance analysis, security, and congestion control. She investigates how networks like the Internet evolve, examining technology development, adoption patterns, and their impacts on various entities. Additionally, she explores ways to harmonize security and privacy while making them more usable and assessable. Her work particularly focuses on transport layer and multipath transport protocols, examining their performance and security aspects. She also investigates application and transport layer performance, automation, and monitoring. Her research extends to network programming in both Linux kernel and user space, mobile broadband networks from 2G to 5G, and their intersection with the Internet. She is deeply engaged in observability, distributed and system performance monitoring, and automation. Analysis of Dr. Ferlin's recent publications reveals a strong focus on next-generation networking technologies. Her work spans multiple domains including 5G/6G networks, transport protocols (particularly QUIC and MPTCP), network virtualization, container orchestration, and the application of machine learning to networking problems. She has increasingly incorporated large language models into network configuration and automation research. Her publications demonstrate a consistent emphasis on performance measurement, optimization, and security across diverse networking environments from the edge to the cloud. Dr. Ferlin has received notable recognition for her research contributions: Best paper award at IEEE ICIN'21 for 'Learning-based Incast Performance Inference in Software-Defined Data Centers' Applied Networking Research Prize (ANRP)'25 winner for 'NetConfEval: Can LLMs Facilitate Network Configuration?' Dr. Ferlin is actively involved in mentoring the next generation of networking researchers. She has co-supervised numerous Master's and PhD students across multiple institutions including Karlstad University, KTH, TU Berlin, University of Oslo, and universities in Brazil. Her students have worked on diverse topics including NAT64 performance comparison, system tracing visualization, network observability, ML applications to multipath transport, FEC integration with QUIC, high-performance networking for 5G, congestion control, shared bottleneck detection, multipath IoT applications, and container runtime performance. She is also involved in several significant research projects including Vinnova's SEMLA (Securing Enterprises via Machine-Learning-based Automation), Horizon Europe's CODECO (Cognitive Decentralised Edge Cloud Orchestration), and the Knowledge Foundation of Sweden's DRIVE (Data-driven Latency-Sensitive Mobile Services for a Digitized Society). Dr. Ferlin serves as Workshop Chair for ACM SIGCOMM '25, is a member of the ACM/IRTF Applied Networking Research Workshop (ANRW) steering committee, and co-chairs the Internet Congestion Control Research Group (ICCRG) at the IRTF. She previously served as Associate Technical Editor for IEEE Communications Magazine and has been active on numerous program committees for major networking conferences including SIGCOMM, CoNEXT, IMC, and PAM.
Nele Mentens is a full professor at both KU Leuven and Leiden University, where she leads cutting-edge research in applied cryptography, hardware security, and secure embedded systems. At KU Leuven, she is affiliated with the Faculty of Engineering Technology and the Electrical Engineering Department (ESAT), leading the Emerging Technologies, Systems & Security (ES&S) research group at the Diepenbeek campus. Simultaneously, she holds a full professorship at Leiden University’s Leiden Institute of Advanced Computer Science (LIACS), focusing on applied cryptography and security. She has been instrumental in numerous national and international research initiatives, including Horizon Europe and NWO-funded projects. Full Professor, KU Leuven (since 2023) Full Professor, Leiden University (since 2020) Associate Professor, KU Leuven (2014–2023) Post-doctoral Researcher & Lecturer, KHLim / KU Leuven (2007–2014) Ph.D. in Engineering Science, KU Leuven (2007) M.Sc. in Electrical Engineering, KU Leuven (2003) Her research focuses on secure and efficient hardware design, particularly for cryptographic applications on FPGAs, reconfigurable architectures, IoT security, and neuromorphic computing. She explores physical attack resistance, side-channel analysis protection, and trusted computing architectures, with applications in healthcare, industrial monitoring, and endpoint AI. Her work bridges theoretical cryptography with practical hardware implementations, emphasizing energy efficiency and real-time performance. The 15 most recent publications reflect a strong trend toward secure, energy-efficient, and intelligent embedded systems. Topics include neuromorphic AI accelerators, trusted IoT architectures, dynamic reconfiguration for side-channel protection, and secure medical data processing. These works span disciplines such as computer architecture, cybersecurity, digital design, and embedded systems, with a focus on hardware-software co-design and real-world deployment. Nele Mentens has received recognition for her contributions, including: Best Paper Award, DATE'16 Best Paper Nomination, AsianHOST'17 Best Paper Award, CHES'19 She has supervised over 15 Ph.D. students and post-docs, both current and former, and has served as principal investigator in approximately 25 funded research projects. Her work has attracted significant grants from Horizon Europe, NWO, FWO, and national innovation programs. She actively contributes to the academic community through editorial roles in top journals and leadership in major conferences. Nele Mentens leads the ES&S research group at KU Leuven and collaborates closely with LIACS at Leiden University. Her team includes Ph.D. students, post-docs, and research experts working on projects like NimbleAI, NeuroSoC, and TrustedIoT. She has also established secure electronics labs through infrastructure grants and maintains strong international ties with institutions such as EPFL, Ruhr University Bochum, and ETH Zurich.
Prof. Dr. Reinhold Decker is a full professor of business administration with a focus on marketing and market research at Bielefeld University's Faculty of Economics, where he has been affiliated since 1997. He currently serves as the Rector's Representative for Cooperation with Business, BRIC, and Research Transfer (since October 2023), following previous roles as Vice Rector for Information Infrastructure and Business (2019-2023), Vice Rector for Information Management (2015-2019), and Vice Rector for Financial Affairs and Resources (2012-2015). He is also the Scientific Director of BI2000plus - Research Projects on the Region since 2005 and a member of the Bielefeld Graduate School of Economics and Management (BiGSEM). Decker earned his degree in industrial engineering with a focus on OR/Computer Science in 1988, received his doctorate in 1993, and completed his habilitation in 1997, all from the University of Karlsruhe (KIT). His academic career includes visiting professorships at the University of Vienna, Moscow Academy of Economics, University of Veliko Turnovo, Universidade NOVA de Lisboa, Université Paris III – Sorbonne Nouvelle, and the University of Maryland. His research focuses on the development and empirical testing of methods and models for acquiring and analyzing consumer data, particularly from social media, and data-driven, consumer-centric development of intelligent products and services. His work spans social media analysis, buyer behavior modeling, brand image analysis, web mining in marketing, and internet-based preference measurement. Decker's interdisciplinary approach bridges marketing, data science, and consumer behavior, with increasing emphasis on intelligent systems and digital transformation in marketing contexts. Analysis of his 15 most recent publications reveals a strong focus on emerging technologies in marketing, including augmented reality, voice assistants, and social media analytics. His research increasingly examines privacy concerns in the digital age, sustainability communication, and the integration of AI in consumer decision-making processes. The interdisciplinary nature of his work is evident in publications spanning marketing journals, data science publications, and technology-focused outlets. Member of the Scientific Council of the journal Argumenta Oeconomica Cracoviensia (since 2013) Associate Editor of the journal Behaviormetrika (since 2012) Member of the Editorial Board of the Springer series Studies in Classification, Data Analysis, and Knowledge Organization (since 2004) Vice President of the European Association for Data Science – EuADS (2018-2022) Decker has served on numerous editorial boards and scientific program committees, including for the International Federation of Classification Societies. His extensive reviewing work spans prestigious journals such as Journal of Business Research, Journal of Product Innovation Management, and Review of Managerial Science. His leadership extends to project management, including BiLinked (2025), Bielefeld 2000plus (2024), and Bielefelder DatenNarrative (2022). Decker has also edited multiple volumes and special issues on data analysis and marketing, demonstrating his commitment to advancing methodological approaches in business research. As Scientific Director of BI2000plus, Decker leads interdisciplinary research projects focused on regional development. His work bridges academia and industry through initiatives like the Bielefeld Center for Data Science (BiCDaS) and the Bielefeld Graduate School in Theoretical Sciences. His recent projects emphasize data narrative techniques, linking data analysis with effective communication strategies for diverse audiences.
Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.
Dr. Arish Sateesan serves as Professor and Chair of the Institute for Networked Systems at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, located at Kackertstrasse 9 in Aachen, Germany. His research group operates from House C (Room C046) with direct contact via asa@inets.rwth-aachen.de. His primary research domains center on hardware-accelerated network security solutions, specializing in FPGA implementations for high-speed networking. Key focus areas include: Real-time network monitoring and intrusion detection systems Hardware-optimized cryptographic and non-cryptographic algorithms Machine learning integration for wireless beamforming and LiDAR processing Ultra-high-speed flow measurement architectures His work bridges theoretical computer science with practical hardware constraints, emphasizing throughput optimization for security-critical applications. Analysis of his 15 most recent publications (2021-2025) reveals a pronounced shift toward hardware-software co-design for next-generation networks. The research trajectory shows increasing integration of quantized neural networks with traditional security primitives, particularly for mm-Wave and 5G/6G applications. A consistent theme across all publications is the prioritization of hardware friendliness through algorithmic simplification and architectural innovation. As Institute Chair, he leads a research ecosystem focused on developing deployable security solutions for modern network infrastructures, with current projects targeting autonomous vehicle communication systems and infrastructure protection against distributed denial-of-service attacks.
Mohammad Sadoghi is a Professor at the University of California, Davis, with former affiliations at Purdue University, IBM T.J. Watson Research Center, and the University of Toronto. His research focuses on distributed systems, blockchain technologies, consensus protocols, and fault-tolerant computing. He has contributed extensively to transaction processing, stream processing architectures, and the integration of edge-cloud systems with blockchain frameworks. Current Affiliation: University of California, Davis Former Affiliations: Purdue University, IBM, University of Toronto Research Interests include consensus algorithms, Byzantine fault tolerance, distributed ledger technologies, and scalable data processing. He has pioneered systems like ResilientDB and ByShard, addressing challenges in global-scale distributed systems and blockchain fabrics. His work bridges theoretical foundations with practical implementations, emphasizing real-world applications in edge computing and hybrid cloud-edge environments. Key publications highlight advancements in consensus protocols, blockchain scalability, and fault-tolerant architectures. Recent trends in his work focus on concurrent consensus mechanisms, DAG-based systems, and secure geo-replication. Contributions span both academic publications and industry-oriented solutions, such as the Bedrock platform for BFT protocol analysis. Grants and advising roles are implied through his extensive research output, though specific grants are not detailed in the provided text. His collaborations include projects on self-curating databases (e.g., L-Store) and systems like SplitJoin for stream processing.
Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.
Torsten Schaub is a Professor at the Institute of Computer Science , University of Potsdam. His research focuses on Answer Set Programming (ASP) , constraint solving, temporal reasoning, and combinatorial optimization, with applications in multi-agent pathfinding, product configuration, and course timetabling. Key contributions include ASP-based tools for industrial-scale optimization problems, metric temporal logic implementations, and frameworks for dynamic equilibrium logic. Recent work explores efficient design space exploration, stream reasoning, and multi-shot ASP solving for complex domains. His publications emphasize hybrid ASP systems , integrating constraints and temporal logic, with co-authors across Europe and Asia. He actively develops tools like clingo and Clingraph for practical ASP applications in logistics, bioinformatics, and robotics. The articles reveal a trend toward multi-agent systems (e.g., pathfinding algorithms) and temporal extensions in ASP, combining formal logic with real-world problem-solving. Sub-fields include constraint satisfaction, logical abduction, and declarative modeling for optimization tasks.
Jens Herder is a Professor of Virtual Studio/Virtual Reality at the Düsseldorf University of Applied Sciences since 2000. As former Dean of the Faculty of Media , he pioneered the Bachelor's in Media and Applied Information Technology and Master's in Virtual Reality programs. He founded the Journal of Virtual Reality and Broadcasting and leads research at the Virtual Sets and Virtual Environments Laboratory . Studied Computer Science with Architecture at TU Darmstadt PhD in Engineering from University of Tsukuba (1999) His research spans Virtual/Augmented/Mixed Reality , Spatial Media , and Interactive Environments . Key projects include: Sound Spatialization Framework development 3D Monument digitization with ethical participation Augmented Reality lighting control systems Volumetric capture integration for virtual teleportation He supervises 6+ graduate theses annually, focusing on: Real-time virtual production Photorealistic rendering Immersive media workflows AR/VR hardware integration Current affiliations: Head of Virtual Studio/VR Laboratory Internationalization Commissioner for Faculty of Media
Prof. Dr. Susann Müller is Senior Scientist and Group Leader of the Flow Cytometry Working Group at the Department of Applied Microbial Ecology, Helmholtz Center for Environmental Research (UFZ) in Leipzig, Germany. Since 2011, she has held an Associate Professor position for Microbiology at Leipzig University’s Faculty of Life Sciences, bridging fundamental microbial ecology with environmental biotechnology applications through single-cell analytics. Education: 1985: Diploma in Biochemistry, Martin Luther University Halle-Wittenberg 1992: PhD, University of Halle-Wittenberg (Population dynamics of S. cerevisiae) 2003: Habilitation, Technical University Dresden (Multiparametric Cytometry) Her research pioneers microbial community flow cytometry to extract single-cell high-dimensional data, applying macroecological concepts to quantify stability metrics (resistance, resilience, displacement speed, elasticity) in engineered systems. Current focus includes bio-based circular economy initiatives: developing the carboxylate platform for sustainable chemical production and biological phosphate recovery from wastewater streams for resource valorization. Recent publications (2021-2025) reveal consistent innovation in flow cytometry applications, with emphasis on stability assessment in bioreactors, predator-prey dynamics in complex communities, and real-time monitoring of wastewater systems. She integrates ecological theory with multi-omics and data science to decode microbial assembly principles across environmental, agricultural, and industrial contexts. Professional roles: President, German Society of Cytometry (DGfZ, 2008-2010) Associate Editor, Microbiology for Cytometry Part A ISAC Educational Committee (2011-2012) and Scholars Program Committee (2013-2015) Current grants: PHOM project (SMWK InfraProNet 2024-2027): €449,160 for wastewater phosphorus recovery Z-PROJECT (DFG 2022-2025): €556,550 for bacterial biofilm analysis PROMICON (EU H2020 2021-2025): €200,000 for industrial microbiome consortia Moore Foundation (2020-2024): $23,000 for archaeal evolutionary tools Chinese Scholarship Council (2022-2026): Artificial community construction The Flow Cytometry Working Group under her leadership at UFZ develops standardized mock communities (Nature Protocols 2020), automated analysis tools (flowEMMi), and cytometric barcoding methods. It collaborates with Leipzig University, Technical University Dresden, and international partners including UC Santa Barbara, driving innovations in real-time environmental monitoring and wastewater treatment optimization.
