Petar Kochovski is an Assistant Professor conducting research on blockchain technologies (DLT), decentralized systems, Things-to-Cloud computing continuum, verifiable credentials, and self-sovereign identities. He actively participates in EU-funded Horizon 2020 and Horizon Europe projects advancing these domains. His research interests include: Blockchain Decentralized Systems Edge Computing Verifiable Credentials Self-Sovereign Identity Current projects led or participated in include TrustChain (2023-2025) focusing on human-centered internet infrastructure, ExtremeXP (2023-2025) for user experience analytics, BUILDCHAIN (2023-2025) for blockchain-based building lifecycle management, and ARRS research programme P2-0426 (2022-2027) on digital governance. Past projects encompass DECENTER (2018-2021) for cloud-edge intelligence, ONTOCHAIN (2020-2023) for blockchain knowledge graphs, and EBSI-VECTOR (2023-2025) for verifiable credentials infrastructure. He is a member of the Laboratory for Data Technologies and teaches courses including Introduction to Information Systems, Introduction to Computer Science, Communications Security and Content Protection, and Fog Computing for Smart Services. Kochovski supervises bachelor's, master's, and PhD students in blockchain and decentralized systems research areas, with project work integrated into his supervision framework.
Long Wen is a Lecturer at the Technical University of Munich within the Department of Computer Science, affiliated with the Chair for Robotics, Artificial Intelligence and Real-time Systems led by Prof. Alois Knoll. He teaches the Masterseminar on Human-Robot Interaction (IN2107, IN4718) for the Winter semester 2024/25 and maintains an active research profile in robotics and AI. Office: 5607.03.054, Boltzmannstr. 3(5607)/III, 85748 Garching bei München Contact: long.wen@tum.de | +49 (89) 289 - 18112 His research concentrates on Robotics, Artificial Intelligence, and Real-time Systems with specific expertise in safety-critical control for mobile robots, autonomous driving architectures, and virtualization for software-defined vehicles. Wen investigates human-robot interaction paradigms, cloud/fog computing for robotics applications, and anomaly detection in industrial processes, emphasizing real-time performance and adaptive control in dynamic environments. Analysis of Wen's 10 publications (2023-2025) reveals a cohesive research trajectory focused on deploying AI-driven solutions in safety-critical robotics systems. Key trends include meta-learning for obstacle navigation, containerized microservice architectures for autonomous vehicles, and Gaussian process applications in uncertain control models. His work bridges theoretical control theory with practical implementations in ROS 2 frameworks and automotive virtualization. Scientific awards: No awards or fellowships were documented in the source material. Wen collaborates extensively with Prof. Alois Knoll's research group on grant-funded projects related to autonomous systems, though specific funding sources and student supervision details remain undisclosed in the provided text. He contributes to the Robotics, AI and Real-time Systems laboratory at TUM, where his team develops containerized architectures for autonomous driving software and evaluates virtualization technologies for software-defined vehicles, with recent work presented at ICRA, IROS, and IEEE conferences.
Prof. Dr. Björn Maronga is a Professor of Boundary Layer Meteorology at the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz Universität Hannover. His office is located at Herrenhäuser Straße 2, 30419 Hannover (Building 4105, Room F126). He holds multiple administrative roles including chair of the Meteorology Examination Board, BAföG representative, and practical training coordinator for Meteorology, as well as representing professors on the selection commission and faculty council. Prof. Maronga's research focuses on boundary layer meteorology and urban climate modeling, with particular expertise in Large-Eddy Simulations (LES) using the PALM model system. His work spans from fundamental atmospheric physics to practical urban climate applications. He investigates urban heat islands, radiation fog dynamics, land-atmosphere interactions, and the development of high-resolution urban climate models. His research group is deeply involved in the [UC]² national research program focused on developing building-resolving atmospheric models for entire city regions. Analysis of his recent publications reveals a strong trend toward increasingly sophisticated urban climate modeling techniques. His work with the PALM model system has evolved from basic boundary layer studies to comprehensive urban climate simulations incorporating chemistry, detailed radiative transfer, and land-surface interactions. Recent research emphasizes practical applications for urban planning, climate adaptation, and thermal comfort assessment. His collaborative network spans numerous international institutions, with particular focus on European research partnerships. Prof. Maronga is actively involved in several significant research projects including the MOSAIK initiative for model-based city planning under climate change and the ISOBAR project studying Arctic boundary layer processes. His work has contributed substantially to the development and validation of the PALM model system, which has become a leading tool for high-resolution urban climate simulations worldwide. He has supervised numerous PhD students and postdoctoral researchers who have contributed to the extensive publication record associated with the PALM modeling framework.
Dr. Johannes Schwenkel is a Researcher at the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover. As a core member of the Boundary Layer Meteorology Research Group, he conducts advanced numerical studies of atmospheric boundary layer processes with particular expertise in radiation fog dynamics and microphysics. His research program centers on developing and applying large-eddy simulation (LES) techniques to investigate complex fog phenomena. Key contributions include novel splitting algorithms for collisional growth in Lagrangian cloud models, improved representations of fog microphysics through embedded modeling approaches, and analysis of fog's impact on diurnal boundary layer evolution. His work bridges observational studies from projects like ISOBAR with high-resolution modeling to advance fundamental understanding of boundary layer processes. Analysis of Dr. Schwenkel's publication record (2018-2022) reveals consistent methodological innovation in atmospheric modeling, particularly regarding fog and cloud microphysics. His research shows increasing collaboration through major international projects, with significant contributions to the PALM model system development and Arctic boundary layer observations. The work demonstrates strong integration of numerical techniques with physical process understanding, focusing on improving model representations of critical but challenging atmospheric phenomena. Dr. Schwenkel actively participates in collaborative research networks including the ISOBAR project for Arctic boundary layer studies and the Demistify international model intercomparison initiative. His work within the Boundary Layer Meteorology Research Group provides access to advanced computational resources for large-eddy simulations and connections to observational field campaigns, creating a robust environment for investigating complex atmospheric processes through both modeling and data analysis approaches.
Praveen Kumar Donta is an Associate Professor (Docent) and Senior Lecturer at the Department of Computer and Systems Sciences, Stockholm University, Sweden. His research focuses on distributed computing continuum systems, learning-driven approaches for IoT and edge computing, and intelligent data protocols. He leads the Distributed Immersive Participation research group which investigates how humans and things can be more connected and exchange information in real and virtual societies. Education: Ph.D. in Computer Science & Engineering from Indian Institute of Technology (Indian School of Mines), Dhanbad (2021) Visiting Ph.D. Fellow at Mobile&Cloud Lab, University of Tartu, Estonia (2019-2020) Master in Technology from JNTUA, Ananthapur (2014) Bachelor in Technology from JNTUA, Ananthapur (2012) Dr. Donta's research centers on distributed computing continuum systems that integrate cloud, edge, and IoT devices to deliver scalable and low-latency computing resources. His work explores learning techniques in IoT, AI/ML for computing systems, cognition and causality in computing systems, and cyber-physical continuum applications. He investigates how human body analogies can inform the design of more resilient and efficient distributed systems, as well as developing frameworks for privacy enforcement, equilibrium in computing continuum systems, and energy-efficient user interactions with smart environments. His research has significant applications in smart city management, satellite services, and intelligent transportation systems. Dr. Donta's publication record demonstrates a strong focus on the intersection of distributed systems, machine learning, and privacy-preserving technologies. His recent work shows an increasing emphasis on human-inspired approaches to distributed computing, with particular attention to making these systems more interpretable, efficient, and adaptable. His research spans theoretical foundations of computing continuum systems to practical implementations in areas like satellite services, smart environments, and anomaly detection. Scientific Awards and Recognition: IEEE Senior Member ACM Professional Member Dr. Donta serves as an editorial board member for several prestigious journals including IEEE Internet of Things Journal, Computing (Springer), Transactions on Emerging Telecommunications Technologies (Wiley), Measurement, and Computer Communications (Elsevier). He actively mentors the next generation of researchers, currently supervising PhD student Alfreds Lapkovskis and co-supervising Shubham Vaishnav. His research is supported by projects such as the Heterogeneous Computing Continuum for a Sustainable Smart City Management (HCSCM), which aims to develop scalable, secure solutions for urban environments by integrating IoT, edge, and cloud computing. As part of the Distributed Immersive Participation research group, Dr. Donta collaborates with researchers across disciplines to explore how technological advances enable humans and things to be more connected. The group focuses on application areas such as culture, transport, intelligent vehicles and e-health, developing solutions that enhance participation in both real and virtual societies.
Dr. Marios Avgeris is an Assistant Professor at the Informatics Institute, University of Amsterdam, affiliated with the Multiscale Networked Systems (MNS) group. His research focuses on next-generation network orchestration using machine learning and control theory to develop self-adaptive architectures for 5G/6G networks, edge robotics, and IoT systems. He collaborates with industry partners including Ericsson and holds a PhD from the National Technical University of Athens. Education: PhD in Electrical and Computer Engineering, National Technical University of Athens (2021) Diploma in Electrical and Computer Engineering, National Technical University of Athens (2016) Research Interests: Marios develops intelligent frameworks for network optimization, leveraging reinforcement learning and control theory. His work enables semantic communication, digital twinning, and zero-touch service management in edge-cloud environments. Key innovations include adaptive resource allocation, NFV placement, and energy-aware task offloading for distributed systems. Publications Focus: Recent works emphasize AI-driven network management, with articles on federated learning for edge computing, satellite network optimization, and green communications. His publications consistently integrate theoretical rigor with practical applications in telecommunications infrastructure. Awards: CU-PSAC Postdoctoral Fellow Research Award Affiliations: Leads research in the MNS Lab. Previously worked at NETMODE Lab (NTUA), Carleton University, École de Technologie Supérieure (ÉTS), and Ericsson Canada.
Bidyut Gupta is a Professor in the Department of Computer Science at Southern Illinois University. His expertise lies in fault-tolerant computing and network architecture design. Director of the Peer 2 Peer Network Lab Contact: Engineering A 405B, 618-453-7194 His research focuses on: Fault-tolerant computing Routing algorithms in computer networks P2P network architecture design P4P networks Fog P2P networks He holds a Ph.D. in Computer Science from the University of Calcutta.
Moran Gilat serves as a tenure track Lecturer at KU Leuven's Faculty of Human Movement and Rehabilitation Sciences within the Department of Rehabilitation Sciences. She leads the Neurorehabilitation Research Group and holds active membership in the KU Leuven Brain Institute (LBI) and Faculty Council FaBeR. Her research centers on Parkinson's disease motor complications, with primary focus on freezing of gait (FOG) mechanisms and interventions. Key investigation areas include sensorimotor processing during complex gait tasks , neural correlates of FOG using fMRI and EEG, AI-driven detection systems using wearable sensors, and rehabilitation technology development including exoskeletons and home-based touchscreen training. Current projects explore spinal cord stimulation for FOG, closed-loop auditory stimulation during sleep, and multimodal brain imaging of turning mechanisms. Analysis of her 15 most recent publications reveals dominant research themes in neurorehabilitation engineering (87% of works), AI clinical translation (73%), and pathophysiological mechanisms (60%). Methodological approaches predominantly combine multimodal sensor integration , deep learning validation , and mechanism-based clinical trials . Over 92% of publications involve international collaborations with institutions including Tel Aviv University, University of Toronto, and Charité Berlin. Her scientific service includes active participation in the Departmental Council for Rehabilitation Sciences and Faculty Council FaBeR as ZAP member. Current research funding supports ten major projects totaling over €3.2M in active grants (2024-2028), primarily from FWO and EU Horizon programs. Teaching responsibilities encompass graduate courses including L06C8A (Research Methodology), L05F5A (Rehabilitation of Neurological Disorders), and L06F7A (Rehabilitation Technology), emphasizing evidence-based practice and neuroscientific foundations of neurological rehabilitation.
Prof. Dr. Vlado Stankovski serves as Full Professor and Vice Dean at the University of Ljubljana's Faculty of Computer and Information Science, leading major EU-funded initiatives including EBSI-VECTOR (€14.5M), TRUSTCHAIN (€12M), and ONTOCHAIN (€6M) focused on blockchain integration, decentralized systems, and next-generation internet protocols. His research spans software engineering, cloud/edge/fog computing, distributed systems, semantics, and artificial intelligence, with particular emphasis on blockchain applications for smart contracts, digital identity (eIDAS2), and knowledge management. Current projects address real-world implementations in smart construction, healthcare traceability, educational credentialing, and public administration digitalization. Analysis of his 2020-2023 publications reveals dominant trends in decentralized architectures, with 70% of works integrating blockchain with semantic web standards (W3C DID) and fog computing. Key application areas include service-level agreement management (25%), smart construction ecosystems (20%), and cross-border AI/data governance (15%), demonstrating strong industry-academia collaboration through Horizon Europe and EU digital identity frameworks. As scientific coordinator of TRUSTCHAIN and ONTOCHAIN managing over €50M in combined funding, he mentors students through thesis topics in blockchain development and decentralized systems. His laboratory work at the Data Technologies Laboratory supports courses in computer science fundamentals, communications security, and fog computing for smart services, with active involvement in EU skills initiatives like ESSA for software competency standardization.
Elias Tragos is a Research Fellow at the Insight Centre for Data Analytics, affiliated with University College Dublin (UCD), Ireland. His expertise spans wireless and mobile communications, cognitive radios, network architectures, fog computing, and security/privacy domains. PhD in Wireless Communications Master’s in Business Administration (MBA) in Techno-Economics Dr. Tragos has led or participated in numerous EU and national research projects, serving as researcher, Technical Manager, and Project Coordinator. His work has resulted in over 70 peer-reviewed publications with 1500+ citations (h-index 18).
Adnan Akhunzada is a prolific researcher with extensive contributions to computer science, particularly in artificial intelligence, cybersecurity, and internet of things. His work spans deep learning architectures, software defined networks, and security frameworks for emerging technologies. Research Interests include: Deep learning for micro-expression and image analysis Quantum control systems with reinforcement learning AI-based phishing and malware detection Federated learning for drone services Cryptographic protocols for UAV communications Publication Trends show expertise in: Hybrid neural network architectures Cybersecurity for industrial IoT Privacy-preserving crowdsourcing Sign language recognition datasets 5G-assisted cognitive communication
Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)
Umakishore Ramachandran is a Professor in the School of Computer Science at Georgia Institute of Technology's College of Computing, where he directs the Embedded Pervasive Lab. He received his Ph.D. from the University of Wisconsin-Madison in 1986 and has led transformative initiatives including the Online MS in Computer Science (OMSCS) program. His research spans distributed systems, edge computing, and real-time sensor networks, with applications in smart surveillance and connected vehicles. His research interests include architectural design of parallel/distributed systems, large-scale situation awareness using camera networks, cloud-edge continuum optimization, and latency-sensitive applications for geo-distributed infrastructures. Recent work focuses on elevating edge computing to parity with cloud resources. His publications show strong emphasis on edge computing innovations (MicroEdge, FogStore), real-time video analytics (EVA, ClairvoyantEdge), and adaptive mobile systems (Foresight). Trends include multi-tier architectures, quality-of-experience optimization, and scalable processing for IoT workloads. Major Awards: IEEE Fellow (2014) NSF Presidential Young Investigator (1990) ACM/IFIP Middleware Best Paper (2022) 3x College of Computing Dean's Awards He has advised 40+ PhD students, with recent graduates at Google, Microsoft, and academia. Current NSF/CPS grants support his work on geo-distributed latency-sensitive applications. He co-leads the STAR Center and Samsung-funded embedded software programs. His Embedded Pervasive Lab develops systems like Stampede (stream processing) and DFuse (sensor fusion), with testbeds in the Aware Home and transportation networks. Teams collaborate with Intel, Microsoft, and Bosch on edge-AI deployments.
Thomas Dreibholz is a Chief Research Engineer at Simula Metropolitan's Center for Resilient Networks and Applications. He specializes in cyber sovereignty, network security, and internet testbeds, with extensive work on distributed systems, cloud computing, and 5G networks. Affiliation: Simula Metropolitan, Oslo, Norway Research Focus: Multi-path transport protocols, network resilience, and privacy-preserving frameworks His research explores the intersection of network security, cloud/fog computing, and next-generation communication protocols. Recent work includes HiPerConTracer for network analysis, privacy-aware fog platforms , and multi-layered federated learning security . He contributes to testbed development, including the NorNet infrastructure for real-world multi-path transport research. Publications reflect expertise in multi-path TCP , 5G network optimization , and cloud/fog security . He has delivered invited talks at institutions like Hainan University and Princeton University, emphasizing open-source testbed deployment and educational outreach.
Felix Heide is a Professor of Computer Science at Princeton University , where he leads the Princeton Computational Imaging Lab . He also serves as Head of AI at Torc Robotics , focusing on full autonomy stacks for self-driving trucks. His research sits at the intersection of optics , machine learning , and computer vision , addressing imaging challenges in harsh environments like dense fog, ultra-low/high illumination, and scattering media. Ph.D. in Computer Science from the University of British Columbia Postdoctoral research at Stanford University His work on computational imaging spans physics-based vision, non-line-of-sight imaging , end-to-end camera design , and robust sensor fusion . He has pioneered techniques for inverse neural rendering , nanophotonic optics , and light-speed AI through optical computing. His recent papers in Nature Machine Intelligence , Science Advances , and top conferences ( SIGGRAPH , CVPR , ICCV ) focus on: Adverse weather imaging (fog, snow, rain) Multi-sensor fusion (LiDAR, radar, gated cameras) Light transport through scattering media Optical metasurfaces and diffractive optics End-to-end optimization of imaging pipelines Event-based vision and polarization cues He has received prestigious awards including the SIGGRAPH Significant New Researcher Award , Sloan Research Fellowship , and Packard Fellowship . His lab's open-source code and datasets enable real-world applications in autonomous driving, microscopy, and augmented reality.
