Andreas Löpker is a Professor of Mathematics and Stochastics at HTW Dresden, Faculty of Computer Science/Mathematics. He teaches courses like Computer Statistics for Business Informatics , Quantitative Methods for Economists , and Mathematical/Stochastic Models for Media Informatics . Research Areas: Stochastic processes, Markov processes, Queueing theory, Risk theory, Renewal theory Publication Trends: Recent works focus on stochastic gene expression modeling, binomial thinning, fluid overflow analysis, and perturbation methods in Markov chains. Many studies connect queueing theory with risk processes.
Luxi Zhao is a Research Fellow at Technische Universität München's Embedded Systems and Internet of Things department, specializing in Time-Sensitive Networking (TSN) and real-time network calculus. Working under the supervision of Prof. Sebastian Steinhorst, Zhao contributes to projects like ReMiX and 6G-Life, focusing on security, performance analysis, and configuration optimization of deterministic networks. Research Focus: Worst-case latency analysis in TSN networks Runtime configuration and reconfiguration problems Hybrid scheduling of processing and communication Network calculus modeling for heterogeneous systems Security challenges in industrial IoT and autonomous systems Interoperability of IoT systems in Industry 4.0 Teaching Activities: Secure Autonomous Systems (2025) Software Architecture for Distributed Embedded Systems (2025) IoT Security (2025) IoT Remote Lab practical courses across multiple semesters Advanced Seminar series (2020-2025)
Andrey I. Lyakhov is a Professor and Doctor of Computer Science at the Institute for Information Transmission Problems of the Russian Academy of Sciences. He serves as the Head of Laboratory №18 and has been active since 1959. His research focuses on wireless networks, particularly IEEE 802.11 and 802.16 protocols. Position: Laboratory Chief Affiliation: Institute for Information Transmission Problems, Russian Academy of Sciences Research Interests: Wireless Networks, MAC Protocol Analysis, Network Performance, Distributed Control, Multicast QoS, Channel Assignment His work includes analytical modeling of data transmission, beaconing in mesh networks, and studies on network unfairness and congestion. He has contributed to patents in wireless sensor networks and piconet beacon management. Notable publications span wireless LANs, WiMAX, and sensor network optimization. Lyakhov's research has involved collaborations with international conferences and journals, focusing on throughput estimation, channel contention, and quality of service in wireless systems. Recent publications include studies on bandwidth piggybacking (2010), intra-flow interference in mesh networks (2010), and multicast QoS support in WLANs (2007). Earlier works from 1983-2008 cover foundational topics in queueing theory, cache efficiency, and distributed control systems.
Dr. Ming Li is a researcher at RWTH Aachen University , focusing on interdisciplinary research spanning Human-Computer Interaction (HCI) and Geotechnical Engineering . His work on mobile devices and augmented reality has led to practical applications like MobileVideoTiles for multi-device video display and ACTUI for tangible interfaces using commodity hardware. Email: mingli@informatik.rwth-aachen.de In 2012 , he contributed to Dynamic Tiling Display and Segway AR-Tactile Navigation , emphasizing visual synchronization and vibro-tactile feedback. His recent publications (2020–2025) pivot toward soil mechanics , foamed concrete , and machine learning applications in geotechnical modeling, including Bayesian optimization for soil parameter calibration and multiscale fracture modeling for composites. He received the Best Paper Award at MUM12 . His work explores both mobile technologies and civil engineering materials , suggesting a broad technical scope.
Prof. Dr.-Ing. Annika Raatz serves as the Dean of the Faculty of Mechanical Engineering at Leibniz University Hannover and Executive Director of the Institute for Assembly Technology and Robotics. She leads task groups in advanced manufacturing and contributes to collaborative research centers CRC 1368 Oxygen-free Production CRC 871 Regeneration of Complex Capital Goods Her work bridges academia and industry through roles in the Leibniz School of Optics and Photonics , PhoenixD Cluster , and the German Research Foundation .
Nalini Venkatasubramanian is a Professor at the University of California, Irvine with over three decades of research experience in Internet of Things, Cyber-Physical Systems, and Smart Spaces. Her work bridges theoretical foundations with practical applications in urban infrastructure, environmental monitoring, and healthcare systems, making significant contributions to both academic research and real-world implementations. Her research interests span privacy-preserving IoT systems, semantic modeling of smart environments, federated learning approaches, and resource-constrained computing. She has pioneered frameworks for human-in-the-loop IoT planning, verifiable data management, and context-aware monitoring systems that have been implemented in community-scale deployments. Her work consistently addresses the tension between functionality and privacy in connected environments. Analysis of her recent publications reveals a strong interdisciplinary trajectory, with increasing focus on environmental sustainability, equitable resource allocation, and socially responsible computing. Her research integrates techniques from machine learning, distributed systems, and human-computer interaction to solve complex problems in water infrastructure, power grid security, and community resilience. Dr. Venkatasubramanian has mentored numerous graduate students who have become leaders in their respective fields, and maintains extensive collaborations across computer science, civil engineering, and environmental science disciplines. Her laboratory has produced influential systems including SmartSPEC, REAM, CANOPY, and PrivacySphere, demonstrating her commitment to translating research into practical solutions.
Huber Flores is a Professor in the Department of Computer Science at Aalto University's School of Science, specializing in pervasive computing, mobile sensing, and sustainable technology applications. His research bridges the gap between theoretical computer science and real-world environmental challenges through innovative applications of drone networks, thermal imaging, and AI systems. His research interests focus on Pervasive Computing , Mobile Sensing , Drone Networks , Environmental Monitoring , AI Applications , and Sustainable Computing . Flores develops systems that leverage everyday interactions and low-cost sensing to address environmental sustainability challenges, particularly in plastic pollution monitoring, urban air quality assessment, and resource optimization. His work on thermal dissipation sensing modalities represents a novel approach to human-environment interaction understanding. Analysis of his recent publications shows a strong trend toward integrating large language models with multi-sensor data for context reasoning, while maintaining focus on practical environmental applications. His research consistently addresses scalability challenges in city-scale autonomous drone deployments and sustainable computing through e-waste repurposing. Flores has received no explicitly mentioned scientific awards in the available literature, though his high publication volume in top-tier venues demonstrates significant recognition within the pervasive computing community. His collaborative work spans multiple international institutions, with frequent co-authorship patterns indicating strong connections with Petteri Nurmi, Sasu Tarkoma, Pan Hui, and Mohan Liyanage. His research has secured funding supporting work on drone networks, environmental monitoring systems, and AI robustness frameworks, though specific grant details aren't provided in the source material. Flores leads research on the SPATIAL architecture for AI trustworthiness, LIZARD for plastic litter monitoring, and SEAGULL for underwater plastics analysis, demonstrating his focus on applying computing to pressing environmental challenges through innovative sensing approaches.
Andrea Vinci is a Researcher at the Italian National Research Council (CNR) under the Institute for High-Performance Computing and Networking (ICAR-CNR) . His work bridges Machine Learning , Internet of Things (IoT) , and Smart City technologies, focusing on scalable solutions for urban data analysis, energy optimization, and cognitive building systems. Research Interests include: Developing multi-density clustering algorithms for urban hotspot detection Designing platform-agnostic IoT applications across edge-cloud architectures Applying quantum computing to energy management and cloud resource allocation Creating deep reinforcement learning models for human-driven smart environments Leveraging LSTM networks and federated learning for occupancy prediction Exploring blockchain-empowered swarm robotics for distributed control Key Scientific Contributions : Best Paper Award at ACM Computing Frontiers 2023 for spatio-temporal crime prediction Pioneering the COGITO platform for cognitive building automation Advancing 32 Gb/s passive optical networks for high-loss environments
Ofer Biran is a prominent computer science researcher at the Technion - Israel Institute of Technology, where he serves as a Professor in the Department of Computer Science within the Faculty of Electrical Engineering and Computer Science. With a research career spanning over three decades from 1988 to present, Biran has established himself as a leading expert in distributed systems and cloud computing. His work bridges theoretical foundations with practical systems implementation, evolving from early theoretical distributed computing research to contemporary cloud infrastructure and policy analytics systems. Biran's research interests focus on the critical challenges of modern computing infrastructure. His early work investigated fundamental theoretical aspects of distributed task solvability and round complexity in distributed systems. Over time, his research evolved toward practical systems challenges, particularly in cloud computing environments. His recent work addresses virtual machine placement optimization, network-aware resource allocation, heterogeneous resource reservation, and policy-driven cloud ecosystems. This progression demonstrates his ability to identify and solve increasingly complex problems as computing paradigms shifted from theoretical distributed systems to large-scale cloud infrastructure. An analysis of his publication trends reveals a clear evolution from theoretical computer science toward applied systems research. His recent publications (2016-2023) predominantly focus on cloud computing infrastructure, policy analytics, and data center networking, while maintaining strong theoretical foundations. Biran has made significant contributions to understanding how to optimize resource allocation in heterogeneous environments, particularly through network-aware VM placement strategies that balance performance, reliability, and efficiency requirements in modern cloud systems. Biran has maintained extensive collaborations throughout his career, working with researchers including Yosef Moatti, Dean H. Lorenz, Shlomo Moran, Shmuel Zaks, Erez Hadad, and Richard E. Harper. His role as co-editor of the 2023 SYSTOR conference proceedings in Haifa demonstrates his continued active participation and leadership in the systems research community. His research has consistently addressed practical challenges in computing infrastructure while maintaining strong theoretical rigor, making significant contributions to both academic understanding and real-world system design.
David Breitgand is a senior researcher at IBM Research specializing in cloud computing, networking, and virtualization technologies. With a publication record spanning from 1997 to 2025, he has established himself as a significant contributor to the fields of cloud infrastructure, network management, and distributed systems. His work shows a clear evolution from traditional network management protocols to modern cloud-native architectures, serverless computing, and edge-to-cloud integration. Dr. Breitgand's research interests focus on optimizing resource allocation in distributed systems, with particular emphasis on cloud-edge continuum architectures. His work addresses critical challenges in network function virtualization, service function chaining, and 5G media applications. He has made significant contributions to the understanding of how to efficiently deploy and manage services across heterogeneous cloud environments, from data centers to the network edge. His research combines theoretical foundations with practical implementations, often resulting in systems that have been evaluated in real-world settings. Analysis of his recent publications (2021-2025) reveals a strong focus on edge-to-cloud integration, with particular attention to 5G media applications, service function chaining in distributed environments, and serverless computing paradigms. His work demonstrates consistent innovation in developing algorithms and frameworks that optimize resource usage while meeting service level objectives. The research spans theoretical foundations, system design, and practical implementation, with publications appearing in top-tier venues like INFOCOM, SYSTOR, and IEEE journals. Dr. Breitgand has collaborated extensively with researchers including Danny Raz, Dean H. Lorenz, and Avi Weit, indicating long-term institutional relationships. His work has been instrumental in several European research initiatives, particularly those focused on 5G media applications and cloud networking. While specific awards aren't documented in the available information, his consistent publication record in high-impact venues and sustained research contributions over nearly three decades speak to his standing in the research community. His research has practical implications for the design and operation of modern cloud and edge infrastructure, with applications in media delivery, network function virtualization, and distributed service deployment. Dr. Breitgand continues to be an active contributor to the field, with ongoing research addressing emerging challenges in the convergence of networking and cloud technologies.
PD Dr. Matthias Klusch is a Principal Researcher and DFKI Research Fellow at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken, where he heads the Intelligent Information Systems (I2S) research team within the Agents and Simulated Reality department. He holds the position of Private Docent (habilitated faculty member) in Computer Science at Saarland University, affiliated with the Faculty of Mathematics and Computer Science and the Computer Science Department at the Saarland Informatics Campus. Dr. Klusch earned his diploma and doctoral degree (PhD) from the University of Kiel and completed his habilitation in Computer Science at Saarland University. His academic journey includes positions as Adjunct Professor at Swinburne University of Technology in Melbourne, Assistant Professor at VU Amsterdam and TU Chemnitz, and Postdoctoral Researcher at Carnegie Mellon University. His research focuses on the intersection of cutting-edge AI paradigms, with particular emphasis on hybrid (neuro-symbolic) AI systems that combine the strengths of symbolic reasoning and machine learning approaches. He has pioneered work in quantum artificial intelligence for coordination and optimization problems, intelligent agent technologies, and semantic service coordination. His work bridges theoretical foundations with practical applications in autonomous systems, automotive technology, and smart information systems. Dr. Klusch's recent publications demonstrate a clear trajectory toward quantum-enhanced AI solutions, particularly in optimization, navigation, and coalition formation problems. His work increasingly integrates quantum computing principles with traditional AI techniques to address computational challenges that are intractable for classical approaches alone, showing particular promise in autonomous vehicle navigation, satellite networks, and complex regression problems. Finalist for the ACM SIGART Award for Excellence in Autonomous Agent Research (2008) As an active educator, Dr. Klusch has supervised numerous Master's theses and regularly teaches advanced seminars and courses at Saarland University on Quantum AI, Hybrid Learning and Reasoning, and AI for Autonomous Driving. He has led multiple significant research projects including QAI2/QAICO (Quantum AI for Automotive Industry), WELCOME (intelligent conversation agents for migrant integration), CREMA (cloud-based manufacturing), and MODEST (model-driven agents for semantic web services), with funding from the European Commission, German Federal Ministry of Education and Research (BMBF), and industry partners. Dr. Klusch leads the Intelligent Information Systems (I2S) research team at DFKI, which focuses on developing innovative approaches to intelligent data analysis and service coordination. His team actively participates in the Saarland Informatics Campus ecosystem, collaborating with researchers across multiple institutions on cutting-edge AI challenges, particularly in the emerging field of quantum AI applications.
Dr. Priyanka Priyanka is a Professor at the Institute of Materials for Electrical Engineering , RWTH Aachen University, specializing in quantum computing, machine learning, and power systems. Her research focuses on advanced optimization techniques for electrical grids and renewable energy integration. University: RWTH Aachen University Department: Institute of Materials for Electrical Engineering Email: p.thawany@iwe1.rwth-aachen.de Research Interests: Quantum computing applications in power systems Machine learning for grid stability and parameter estimation Smart grid risk assessment and heterogeneous asset management Dynamic phasor analysis in converter-driven grids Renewable energy integration and wind turbine stability Publication Trends: Recent work emphasizes quantum algorithms (2025-2024) for grid planning, Gaussian Process-based machine learning (2024-2023) for line parameter estimation, and dynamic phasor methodologies (2023-2022) for converter-driven grids. Earlier studies (2014-2015) focus on wind turbine generator stability. Contact: Office located at Walter Schottky House, Sommerfeldstraße 18-24, Aachen, Germany.
Vanessa Felch is a Research Assistant and Scientific Associate at the Chair of Operations Management and Logistics, Faculty of Social Sciences, Economics and Business Administration, Otto-Friedrich University of Bamberg. She holds a Master's degree in Business Administration with a focus on Controlling, Logistics, and Marketing from the University of Bamberg and a Bachelor's degree in Business Sciences from Friedrich-Alexander University Erlangen-Nuremberg. Education: M.Sc. in Business Administration (Controlling, Logistics, Marketing), University of Bamberg (2015-2017) B.A. in Business Sciences, Friedrich-Alexander University Erlangen-Nuremberg (2011-2014) Academic studies abroad at Mälardalen University, Sweden (2012-2013) Professional Roles: Research and Teaching Assistant at University of Bamberg (since 2017) Working Student at Siemens Healthcare GmbH (2013-2017) Her research focuses on maturity models, e-commerce logistics, returns management, and sustainability in logistics. Recent publications explore business process maturity models, industry 4.0 integration in supply chains, and sustainable practices in e-commerce. She teaches courses on E-Commerce, Production Theory, and academic research methods at both bachelor's and master's levels. Vanessa Felch's work bridges theoretical foundations and practical implementation in logistics, with a strong emphasis on digital transformation (Industry 4.0) and consumer-centric delivery solutions. Her publications highlight interdisciplinary approaches combining operations management, sustainability, and digital platforms. She facilitates thesis applications for students and collaborates with industry partners through the Chair of Operations Management and Logistics. Email contact: vanessa.felch@uni-bamberg.de .
Jan Mendling is a Professor of Process Science at the Department of Computer Science, Humboldt University of Berlin, Germany. He also holds adjunct professorships at Vienna University of Economics and Business, Austria, and serves as a Principle Investigator at the Weizenbaum Institute, Berlin. Additionally, he co-founded Noreja Intelligence GmbH. University: Humboldt University of Berlin Department: Department of Computer Science Other Affiliations: Weizenbaum Institute, Vienna University of Economics and Business His research spans business process management, process mining, information systems, and sustainability compliance. He has authored over 500 publications and co-authored textbooks including Fundamentals of Business Process Management and Wirtschaftsinformatik . Recent research trends focus on timeline-based process discovery, event sequence analysis, and leveraging large language models (LLMs) for business process sustainability. His work integrates temporal analytics, resource analysis, and contextual frameworks for process mining. Scientific Awards: Runner-Up for Best Paper at ICPM 2024 Winner of Best Paper at EdbA 2024 Best Paper at ER 2021 Best Forum Paper at PoEM 2020 Excellence in Teaching Awards (2016, 2017) He leads projects funded by the Einstein Foundation Berlin, BMBF, EU FP7, Horizon 2020, Erasmus+, and FFG. His editorial roles include founding co-Editor-in-Chief of Process Science and memberships on boards like Software and Systems Modeling and Business Process Management Journal .
Henning Meyerhenke is a Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. He serves as Deputy Director of Teaching and Studies, focusing on graph algorithms , network analysis , and scientific workflow scheduling . His research spans complex systems modeling and parallel computing. Ranks: Professor Institution: Humboldt University of Berlin Research Interests include energy-efficient computing, heterogeneous systems, and scalable graph algorithms. Recent work explores network sparsification , workflow mapping , and graph robustness under edge deletions. His group also investigates climate science applications via climate networks . Publication Trends reflect advancements in parallel algorithms , distributed systems , and adaptive scheduling for scientific workflows. Key subfields include memory-aware computing , harmonic centrality , and edge sampling techniques. Student Advising : Michael Piechotta (PhD candidate, defended September 15, 2025)
