Find Academic Supervisors

• Edge Computing
• Fog Computing
• Distributed Systems
• +5 more

Umakishore Ramachandran is a Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research spans edge computing, distributed systems, and real-time video analytics, with significant contributions to fog computing infrastructure, mobile systems, and sensor networks. Over a prolific 38-year career, he has authored 142 publications with major contributions in 2022-2025. His research interests focus on bridging the gap between cloud and edge computing, with pioneering work in video analytics systems like EVA and MicroEdge. He investigates resource optimization for latency-sensitive applications, developing novel approaches for load shedding, data management, and container runtime efficiency at the network edge. His work addresses fundamental challenges in distributed camera networks, autonomous vehicle systems, and real-time stream processing. Ramachandran's recent publications reveal a strong emphasis on practical edge computing solutions, with 75% of his 2021-2025 work focusing on video analytics and infrastructure optimization. His research shows increasing collaboration with industry partners while maintaining academic rigor, with publications appearing in top venues like SIGMOD, Middleware, and DEBS. The work consistently addresses real-world constraints of resource-constrained edge environments. Ramachandran has mentored numerous researchers who have become principal investigators on edge computing projects, with notable collaborators including Harshit Gupta, Enrique Saurez, and Zhuangdi Xu appearing as first authors on multiple papers. His work has received significant grant support for projects related to mobile fog computing and distributed video analytics. He leads research in the Edge Computing Laboratory at Georgia Tech, focusing on the development of practical frameworks for real-world deployment of edge infrastructure. Current projects include eCAV for connected autonomous vehicles and MicroEdge for multi-tenant camera processing systems.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +7 more

David Bermbach is a Full Professor of Scalable Software Systems at Technical University of Berlin (TU Berlin) since 2023, where he heads the Scalable Software Systems research group within Faculty IV - Electrical Engineering and Computer Science. He is also co-affiliated with the Einstein Center Digital Future (ECDF). Prior to his current position, he served as an Assistant Professor for Mobile Cloud Computing at TU Berlin from 2017 to 2023. His educational background includes a diploma in Business Engineering (2010) and a PhD with distinction in Computer Science (2014), both from Karlsruhe Institute of Technology (KIT). Prof. Bermbach's research focuses on distributed systems with connections to database systems, software engineering, and interdisciplinary computer science applications. His work encompasses cloud, edge, and fog computing, enterprise and middleware systems, IoT platforms, distributed storage systems, and benchmarking. As part of the Einstein Center Digital Future, he also engages in interdisciplinary activities, including the citizen science project SimRa on safety in bicycle traffic. It's safe to say he's interested in engineering systems and applications mostly above OS level. His recent publications demonstrate a strong focus on serverless computing, edge computing, and distributed systems, with research spanning from theoretical foundations to practical implementations addressing real-world challenges in geo-distributed environments. Key trends include optimizing serverless application performance, developing edge-to-cloud platforms, and advancing benchmarking methodologies for distributed systems. Best Paper Award at EdgeSys 2024 for 'ShutPub: Publisher-side Filtering for Content-based Pub/Sub on the Edge' Best workshop paper award at ISYCC 2017 Best paper award candidate at ICSOC 2017 Best paper runner up award at IC2E 2014 Best paper award at CLOUD COMPUTING 2011 Prof. Bermbach actively collaborates across disciplines and institutions, as evidenced by his extensive publication record with diverse co-authors. His work has practical applications in areas such as bicycle traffic safety through the SimRa project, which uses crowdsourcing to identify near-miss hotspots in bicycle traffic. He leads the Scalable Software Systems group at TU Berlin, continuing the work previously done by the Mobile Cloud Computing group. The research group focuses on advancing the state of the art in distributed systems, with particular attention to practical implementation challenges and experimental validation through testbeds and real-world deployments.

• Distributed Machine Learning
• Privacy Preserving Computing
• Federated Learning
• +3 more

Maximilian Egger is a Doctoral Researcher at the Institute for Communications Engineering under Prof. Antonia Wachter-Zeh at the Technical University of Munich (TUM). His research focuses on distributed machine learning, privacy-preserving computing, and information theory. He holds an M.Sc. in Electrical Engineering and Information Technology (2022, TUM) and a B.Eng. in Electrical Engineering (2020). He has conducted research stays at École Polytechnique Fédérale de Lausanne (2024) and Imperial College London (2023). Egger has received several awards, including the DAAD Scholarship (2023) and the VDE Award Bavaria (2020). His work emphasizes secure federated learning, Byzantine-resilient systems, and efficient distributed algorithms. He is affiliated with the Chair of Coding and Cryptography and actively contributes to advancements in decentralized learning systems. Recent publications highlight breakthroughs in privacy preservation, channel capacity estimation, and scalable federated edge learning.

• Signal Processing
• Wireless Communications
• Networking
• +3 more

Philippe Ciblat is a Professor at TELECOM Paris Tech, affiliated with the Department of Signal Processing and Communications. His research spans signal processing, wireless communications, and machine learning applications in networking. He has collaborated extensively with institutions like the University of Paris-Saclay and international researchers in areas such as cooperative communication protocols, resource allocation, and coding theory. Research Interests: Machine learning for signal processing, wireless channel modeling (Rician fading), lattice decoding, caching strategies, and distributed optimization. Notable Work: Pioneered transformer-based packet scheduling, neural network approaches to lattice decoding, and effective capacity analysis in fading channels. His contributions include over 170 publications in top venues (IEEE Trans. Signal Process., IEEE Trans. Wireless Commun.) and collaborations with industry partners on practical implementations like cache-aided polar coding. He has advised multiple researchers in distributed systems and wireless resource management.

Stefan SchulteView profile

Professor

• Data Engineering
• Blockchain Technologies
• Internet of Things (IoT)
• +4 more

Prof. Dr.-Ing. Stefan Schulte is a Full Professor at Hamburg University of Technology, leading the Institute for Data Engineering and the Christian Doppler Laboratory Blockchain Technologies for the Internet of Things (CDL-BOT). He holds a diploma in Economics and a Bachelor's in Computer Science from the University of Oldenburg, followed by a Master's in Information Technology (with Merit) from the University of Newcastle. After completing his PhD at TU Darmstadt in 2010, he held roles as Postdoctoral Researcher at TU Wien, Assistant Professor (tenure-track), and eventually Associate Professor before joining TU Hamburg in 2021. His research focuses on data engineering, blockchain technologies applied to IoT, elastic computing, and quality-of-service (QoS) aspects in smart systems. Notable contributions include work on fog computing, federated learning, and cross-blockchain interoperability. He has published over 140 papers in top-tier venues like IEEE Transactions on Services Computing and ACM Computing Surveys. Key awards include Best Paper Awards at the IEEE International Conference on Blockchain (2020) and the European Conference on Service-Oriented and Cloud Computing (2023). Prof. Schulte chairs major conferences such as the IEEE International Conference on Fog and Edge Computing (ICFEC 2025) and serves on editorial boards for journals like IEEE Transactions on Services Computing. He leads CDL-BOT, a lab exploring blockchain applications in IoT and manufacturing. His industrial collaborations include projects like SIMPLI-CITY (smart mobility) and CREMA (cloud-based manufacturing). Current research emphasizes blockchain interoperability, federated learning frameworks, and edge-AI systems. He actively reviews proposals for the German Research Foundation, EU programs, and industry initiatives.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +7 more

David Bermbach is a Full Professor at Technische Universität Berlin , leading the Scalable Software Systems group since 2023. His research focuses on distributed systems, serverless computing, and benchmarking, with significant work on edge and fog computing architectures. He is affiliated with the Einstein Center Digital Future and co-chairs interdisciplinary projects like SimRa for bicycle traffic safety. Full Professor, Scalable Software Systems (2023–present) ECDF-Professor, Mobile Cloud Computing (2017–2023) Postdoctoral Researcher (2014–2017) Education : Diploma in Business Engineering (2010) – Karlsruhe Institute of Technology (KIT) PhD in Computer Science (2014, summa cum laude) – KIT Research Interests span distributed systems with emphasis on cloud, edge, and fog computing, serverless architectures, IoT platforms, and benchmarking frameworks. His work addresses consistency-performance trade-offs, resource placement, and interdisciplinary applications in urban mobility and satellite edge computing. Article Trends show a focus on serverless computing (12/15), edge-cloud integration (9/15), and benchmarking methodologies (7/15). Key themes include optimizing function placement, federated learning architectures, and low-earth orbit computing systems. Scientific Awards Best Paper Award – ShutPub (2024) Best Workshop Paper – A Research Perspective on Fog Computing (2017) Best Paper Runner Up – Benchmarking Eventual Consistency (2014) Summa Cum Laude PhD Thesis (2014) Advising & Grants include mentoring students like Tobias Pfandzelter and Trever Schirmer, leading funded projects through the Einstein Center Digital Future, and contributing to 6G network research. His team works on cloud federation, serverless optimization, and real-world IoT applications.

• Automotive Systems Engineering
• Generative AI
• Model-Based Engineering
• +3 more

Fengjunjie Pan is a PhD student and research assistant at the Chair of Robotics, Artificial Intelligence and Embedded Systems at the Technical University of Munich since 2021. He holds an M.Sc. in Electrical Engineering from TU Berlin (2019) and a B.Eng. in Electrical Engineering from Hamburg University of Applied Sciences. His research focuses on automotive systems engineering and generative AI applications in model-based engineering. His publications (2022-2025) demonstrate expertise in: LLM integration for automotive software development Containerized architectures for autonomous driving Virtualization technologies in vehicular systems Constraint generation and model transformation He supervises multiple Master's and Bachelor's theses on generative AI applications and privacy-enhancing technologies in automotive contexts, working alongside Prof. Alois Knoll's team.

• Atmospheric Science
• Meteorology
• Remote Sensing
• +4 more

Jan Cermak is Professor and acting Director of the Institute of Meteorology and Climate Research - Atmospheric Trace Gases and Remote Sensing (IMK-ASF) at Karlsruhe Institute of Technology (KIT). His research focuses on atmospheric science with specialization in fog dynamics, aerosol-cloud interactions, and satellite remote sensing applications. His primary research interests include Atmospheric Science, Meteorology, Remote Sensing, Aerosol-Cloud Interactions, Fog and Low Stratus Dynamics, Satellite Meteorology, and Machine Learning Applications. He employs geostationary satellite data and explainable machine learning to investigate fog life cycles, particularly in the Namib Desert and Po Valley, examining how aerosols influence cloud formation and dissipation processes. His work bridges observational meteorology with computational techniques for environmental monitoring. Recent publications (2023-2025) reveal a concentrated research trajectory on fog life cycle analysis using multi-sensor approaches, with significant emphasis on aerosol impacts in polluted regions and arid environments. Key methodological trends include explainable AI for cloud fraction adjustment analysis and high-resolution satellite-based air quality estimation, demonstrating strong interdisciplinary integration of atmospheric physics and data science. Scientific Awards: No awards were documented in the provided source material. Advising and Grants: Source documentation contains no information regarding student supervision, research grants, or funding sources. His leadership role as acting director of IMK-ASF suggests administrative responsibilities within the institute's research framework. Labs and Teams: Cermak leads the Atmospheric Trace Gases and Remote Sensing (IMK-ASF) group at KIT, conducting field campaigns including NaFoLiCA (Namib Fog Life Cycle Analysis) and FAIRARI (Fog and Aerosol Interaction Research Italy). The team specializes in satellite data analysis combined with ground-based observations to study fog dynamics in extreme environments.

Dominic HenzeView profile

Professor

• Cyber-Physical Systems
• Smart Environments
• Machine Learning Applications
• +1 more

Dominic Henze is a Professor at the Technical University of Munich (TUM), affiliated with the Faculty of Informatics and the Chair of Software Engineering . He leads research initiatives in Cyber-Physical Systems , Smart Environments , and Machine Learning Applications , with a particular focus on Fog Computing architectures. His research spans theoretical frameworks and real-world implementations, including collaborations with institutions like Carnegie Mellon University and industry partners such as Siemens AG and Zeiss IMT . His work addresses challenges in resource allocation , predictive maintenance , and blockchain integration within industrial contexts. Dr. Henze's publications reveal a consistent focus on Fog Computing architectures, IoT resource management, and educational software engineering. Key trends include self-organizing network systems , decentralized supply chain traceability , and smart environment coordination . He has advised numerous students on topics ranging from QoS negotiation to autonomous drone coordination . As an educator, he has taught multiple iPraktikum courses and seminars on iOS development and Agile Project Management since 2014, with publications exploring team composition strategies and distributed programming pedagogy.

• Embedded Systems
• Hardware/Software Co-design
• Energy-efficient AI Accelerators
• +7 more

Oliver Bringmann is a full Professor and head of the Chair of Embedded Systems at the University of Tübingen, Germany, and a member of the board of directors at the FZI Research Center for Information Technology. His research integrates embedded-system design, energy-efficient AI accelerators, dependable automotive perception, and medical AI for capsule endoscopy. Education & Career Ph.D. in Computer Science, University of Tübingen, 2001 Diploma in Computer Science, University of Karlsruhe (KIT) Head, Chair of Embedded Systems, University of Tübingen (since 2012) Deputy spokesperson & spokesperson, Dept. of Computer Science, University of Tübingen (2014-2022) Board of Directors, FZI Research Center for Information Technology Research Interests Bringmann’s group pioneers hardware/software co-design for ultra-low-power Edge-AI , developing RISC-V based accelerators, compiler-aware neural-architecture search, and real-time perception systems for autonomous driving and medical devices. Key topics include: Energy-efficient AI architectures (“Edge AI”) and custom accelerator generation Robust collective perception under adverse weather (LiDAR, camera, V2X fusion) Timing/power-predictable embedded software and system-on-chip design automation Hardware-assisted security and safety for automotive & IoT systems AI-driven capsule endoscopy localization and anomaly detection Recent Publication Trends His 2024-2025 articles reveal a strong shift toward robust multimodal perception for automated driving (snow, fog, collective LiDAR fusion) and Edge-AI medical devices (capsule endoscopy with multi-task CNNs). Core contributions span dataset generation (SCOPE, SnowyLane), safety metrics (LSM), and fast performance modeling for DNN accelerators. Professional Service & Projects Executive/Steering Committees: IEEE/ACM DATE, CODES+ISSS, CASES, ITSS conferences EU CATRENE EDA roadmap chapter lead (Embedded Software & ESL-to-RTL) Principal investigator in Scale4Edge, OCEAN12, enerDAG and other national projects on energy-efficient sensorics and secure energy trading. His group maintains extensive collaborations with automotive and semiconductor industry, focusing on dependable, energy-aware embedded intelligence.

• Internet of Things (IoT)
• Fog Computing
• Digital Twins
• +3 more

Dimitrios Georgakopoulos is a researcher at Swinburne University of Technology and National Measurement Institute, focusing on Internet of Things (IoT), digital twins, fog computing, and smart manufacturing. His work bridges theoretical advancements with real-world applications in urban infrastructure, precision agriculture, and industrial IoT. Research Areas: IoT, Digital Twins, Fog Computing, Smart Cities, Precision Agriculture Key Contributions: Sensor sharing marketplaces, 5G-enabled smart city frameworks, metadata-assisted IoT data classification Recent Articles (2023-2025): Explore topics like digital manufacturing consistency, AI-powered roadside asset management, and deep learning for heterogeneous IoT data. Trends emphasize autonomic systems, sensor integration, and contextual data analysis. Collaborations: Frequently works with Prem Prakash Jayaraman, Ali Yavari, Abhik Banerjee, and Anas Dawod on IoT security, sensor networks, and time-sensitive applications.

Alexander MitranescuView profile

Researcher

• Water Treatment Engineering
• Membrane Technology
• Wastewater-Based Epidemiology
• +3 more

Alexander Mitranescu is a Researcher at the Chair of Urban Water Management at the Technical University of Munich. He has been working as a research assistant at the Chair since 2021, focusing on advanced water treatment technologies and wastewater-based epidemiology. His educational background includes: Master of Science (M.Sc.) in Environmental Engineering from Technical University of Munich (2021) Bachelor of Science (B.Sc.) in Environmental Engineering from Technical University of Munich (2018) Research stay at LadHyX (Hydrodynamics Institute), École Polytechnique, France (2021) International Academic Exchange Program at École Polytechnique, France (2019-2020) Intensive Chinese Language and Culture Program at Zhejiang University, China (2017-2018) Dr. Mitranescu's research primarily focuses on advanced water treatment technologies , particularly membrane processes for water purification. His work examines surface-patterned reverse osmosis membranes, concentration polarization, and hydrodynamic effects to mitigate membrane fouling. He also conducts significant research in wastewater-based epidemiology , studying SARS-CoV-2 biomarkers in wastewater to understand community-level infection patterns. His research bridges environmental engineering with public health surveillance, contributing to both water treatment innovation and pandemic monitoring systems. His recent publications demonstrate a strong focus on membrane technology for water treatment and wastewater-based epidemiology. The membrane research explores computational fluid dynamics modeling of surface-patterned membranes and feed channel hydrodynamics to reduce fouling. The epidemiology work examines biomarker fluctuations in wastewater across different community sizes and develops normalization methods for accurate viral load assessment. These research streams reflect an interdisciplinary approach connecting environmental engineering with public health applications. His scientific recognition includes: Study scholarship from the Max Weber Program Bavaria (2014-2021) Study scholarship from the German Academic Scholarship Foundation (2014-2021) As a researcher at the Chair of Urban Water Management, Dr. Mitranescu contributes to multiple research projects focused on advanced water treatment and wastewater surveillance. His work supports the development of more efficient membrane technologies for water purification and innovative approaches to public health monitoring through wastewater analysis. While specific grant information isn't detailed in the available materials, his publications indicate involvement in collaborative research projects, including the BMBF Biomarker Cov2 Collaborative Project and EU Project ESI-CorA. Dr. Mitranescu works within the "Further water treatment" research group at the Chair of Urban Water Management, collaborating with a multidisciplinary team of researchers investigating innovative water treatment technologies and applications. His work connects with broader research initiatives at TUM focused on sustainable water management and public health protection.

• Wireless Communication
• Edge Computing
• Network Security
• +3 more

Yao Zhu is a Visiting Professor at the Chair of Information Theory and Data Analytics, RWTH Aachen University . His research focuses on advanced wireless communication systems, particularly in Edge Computing , Ultra-Reliable Low-Latency Communication (URLLC) , and Physical Layer Security , leveraging Finite Blocklength Codes for next-generation network optimization. Key research areas include: Optimization of resource allocation and task scheduling in distributed edge learning and fog computing environments Reliability and energy efficiency trade-offs in Industrial IoT and V2X networks Novel applications of NOMA (Non-Orthogonal Multiple Access) and short-packet communication for secure and fresh data transmission Integration of physical layer deception with semantic reliability models His work explores the interplay between telecommunications and computer science principles to address challenges in low-latency, high-reliability networked systems. The Chair of Information Theory and Data Analytics serves as his academic base, focusing on theoretical and practical advancements in data-driven communication frameworks.

• Machine Learning
• Edge Computing
• Networking Technologies
• +5 more

Mounir Bensalem is a Ph.D. candidate and research assistant at the Institute of Computer and Network Engineering within the Faculty of Electrical Engineering, Information Technology, and Physics at Technical University of Braunschweig, Germany. His research focuses on integrating machine learning and edge computing into next-generation network architectures, particularly in optimizing LoRa, analyzing reconfigurable intelligent surfaces (RIS), and applying reinforcement learning to edge serverless functions. He holds an Engineering Diploma and M.Sc. in Information Systems from the National Engineering School of Tunis. Education: Master’s Degree in Information System Techniques (2017), National Engineering School of Tunis. Engineering Diploma in Industrial Engineering (2017), National Engineering School of Tunis. Key Research Areas: Machine Learning, Edge Computing, 5G/6G Networks, Network Security, and Reconfigurable Intelligent Surfaces. Projects: EU Horizon MANOLO (2024–2026), H2020 FISHY (2020–2023), DFG FOR 2863 (2019–2022), and H2020 mF2C (2017–2020). Awards: Best Paper Award (2022) for work on IoT buffer size benchmarking. Lab/Team: Part of Prof. Jukan’s group at TU Braunschweig, focusing on communication networks and edge computing.

• Edge Cloud Computing
• Terahertz Communications
• Satellite Communications
• +3 more

Cao Vien Phung is a researcher at the Institute for Computer and Network Engineering, part of the Faculty of Electrical Engineering, Information Technology, Physics at Technische University Braunschweig, Germany. His work focuses on advanced communication systems including Edge Cloud computing networks, Terahertz (THz) communications, and Satellite communications. Current projects include: BMBF 6G-RIC Campus Network (2021-2025), DFG Metrology for Parallel THz Channels (2019-2025), and EU H2020 mF2C Fog-to-Cloud Ecosystem (2017-2020). Recent research publications highlight his contributions to RIS-assisted networks, throughput optimization in 6G systems, and THz communication coding techniques. Key technologies explored include UAV relays, interference graphs, digital twins, and network coding in IoT environments. Scientific achievements include receiving the BEST PAPER AWARD in 2022 for buffer sizing research in IoT devices. His work is part of broader efforts in wireless network optimization and future communication infrastructure development at TU Braunschweig.