Keivan Navaie is a Professor of Intelligent Networks at Lancaster University’s School of Computing and Communications. He serves as a member of the Independent Scientific Advisory Committee at the Alan Turing Institute, overseeing the £100 million BridgeAI programme, and previously as Principal AI Technology Advisor to the UK Information Commissioner’s Office (ICO). He is recognized with Fellowships from the Institution of Engineering and Technology (IET), Chartered Engineer status in the UK, Senior Fellowship of the Higher Education Academy (HEA), and the IEEE Young Investigator Award. Research Focus: Wireless communications, mathematics, artificial intelligence, 6G networks, blockchain technology, edge computing, cognitive radio networks, and non-orthogonal multiple access (NOMA). Supervision: Actively supervises PhD students in areas like wireless communications and mathematical modeling. Projects: Involved in distributed learning, blockchain integration, 6G research, and spectrum sharing systems. Awards: IEEE Young Investigator Award, Fellow of IET, Chartered Engineer, Senior Fellow of HEA.
Pradip K Srimani is a Professor at the School of Computing , Clemson University , with research focus on Parallel and Distributed Computing , Self-Stabilizing Systems , and Graph Theory Applications . He is an IEEE Life Fellow and ACM Distinguished Scientist with over 250 publications. Research Interests Self-stabilizing algorithms for network graphs Ontology-based biomedical information retrieval High-performance computing resource management Biologically inspired distributed algorithms Network topology computation Recent publications demonstrate expertise in token circulation protocols, genome assembly frameworks, and semantic similarity measurement. He has served on program committees for APDCM , GPC-2018 , and IEEE BigMM conferences, while maintaining editorial roles at International Journal of High Performance Computing and Journal of Big Data . Scientific Awards IEEE Life Fellow ACM Distinguished Scientist
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.
Hossein Saidi is a Professor in the Department of Electrical and Computer Engineering at Isfahan University of Technology, Iran. His research spans High Speed Switching Networks, QoS Algorithms in Broadband Networks, Internet Security, Cryptography, Wireless Networks, Information Theory, and Coding. Research Interests: High Speed Switching Networks, QoS Algorithms, Internet Security, Cryptography, Wireless Networks, Information Theory, Coding Email: hsaidi@iut.ac.ir His recent publications focus on Software-Defined Networking (SDN), Blockchain Consensus, Wireless IoT, and Energy Management Systems. Key themes include network optimization, security protocols, distributed systems, and hardware design.
Dr Fredrik Dahlqvist serves as a Lecturer in Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he contributes to the Centre for Fundamental Computing and AI. His academic profile reflects a strong commitment to theoretical foundations in computing with direct applications to artificial intelligence research. His research spans theoretical computer science with focused expertise in probabilistic programming, probability theory, category theory, and mathematical logic. Dahlqvist investigates the mathematical frameworks governing probabilistic computation, developing rigorous methodologies for uncertainty quantification in computational systems. His work establishes formal connections between abstract category theory and practical probabilistic programming implementations, creating bridges between pure mathematics and applied AI. Recent publications (2023-2024) demonstrate cohesive thematic development across probabilistic programming semantics, neural network optimization, and formal verification. Key trends include error analysis in probabilistic floating-point systems, neural network pruning techniques based on geometric similarity, and categorical foundations for graded computation. His research consistently merges theoretical depth with practical AI challenges, particularly in verification of probabilistic models and optimization of learning architectures. Dr Dahlqvist has not received any documented scientific awards or fellowships according to available sources. He actively supervises two PhD students: Gregor Meehan researching "Representation Learning For Musical Audio Using Graph Neural Network-Based Recommender Engines" and Niki Omidvari conducting theoretical work in computer science foundations. Dahlqvist currently holds a £15,000 grant from the Academy of Medical Sciences for "Learning from Each Other: Neural Networks and Finite Automata" (March 2025-March 2026), investigating formal connections between neural architectures and automata theory. As an integral member of Queen Mary's Centre for Fundamental Computing and AI, Dahlqvist collaborates within a multidisciplinary research environment focused on advancing theoretical underpinnings of artificial intelligence. His work contributes to the Centre's mission of developing mathematically rigorous frameworks for next-generation AI systems through category-theoretic approaches and probabilistic reasoning.
Dr. Stephen Jones is an Associate Professor in the Department of Physics, specializing in high-energy theoretical physics. His research focuses on quantum chromodynamics (QCD), Higgs boson production, and scattering amplitudes at colliders. Affiliation: Department of Physics Academic Rank: Associate Professor His work includes precision calculations for particle production processes, development of computational tools like pySecDec, and applications of machine learning in event generation. Publications highlight contributions to two-loop amplitudes, electroweak corrections, and exclusive vector meson production in collider experiments. Recent trends in his research emphasize: Next-to-leading order QCD corrections for diboson production Top quark mass effects in Higgs-associated processes Landau singularities in scattering amplitudes Machine learning integration for multi-loop integral calculations He actively collaborates on software development for high-energy physics simulations and participates in standard model precision studies at TeV colliders.
Dr. Ian Foster serves as Senior Scientist and Distinguished Fellow at Argonne National Laboratory, directing the Data Science and Learning Division, while holding the Arthur Holly Compton Distinguished Service Professorship in Computer Science at the University of Chicago. His career bridges foundational computing research with transformative scientific applications. Education: BSc in Computer Science, University of Canterbury, New Zealand PhD in Computer Science, Imperial College, United Kingdom Foster pioneers distributed and data-intensive computing systems that enable breakthroughs in materials science, climate modeling, and biomedicine. His research develops tools for high-performance networking integration, parallel algorithms, and scalable data management, exemplified by the Globus platform which has processed over one exabyte of research data. Current initiatives include AI-driven scientific discovery frameworks like the NSF-funded $3.5M AI Model Gardens project. His exceptional contributions are recognized through: 2023 IEEE Internet Award for global impact on networked systems 2022 ACM/IEEE Ken Kennedy Award for parallel computing leadership 2019 IEEE-CS Charles Babbage Award for distributed systems innovation Fellowships in AAAS, ACM, BCS, and IEEE 30+ major awards spanning four decades Foster leads Globus Labs and the University's Systems Group, mentoring next-generation researchers through Argonne collaborations. His work continues to shape national research infrastructure with active NSF and DOE funding supporting next-generation data ecosystems.
Shu-Wei Huang is an Associate Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder, affiliated with the College of Engineering and Applied Science. His research focuses on advanced photonics and quantum engineering, with specialties in nonlinear optics, frequency combs, and ultrafast laser systems. He holds affiliations with both the ECEE department and the Photonics and Quantum Engineering group. His work emphasizes novel laser designs, microresonator-based systems, and applications in optical sensing and quantum technologies. Dr. Huang's research interests include the development of high-performance laser systems, such as counterpropagating all-normal dispersion (CANDi) fiber lasers, and the creation of advanced frequency comb technologies. He explores topics like dissipative soliton generation, parametric oscillation, and the integration of machine learning for predictive modeling in nonlinear optics. His experimental work involves cutting-edge platforms such as lithium niobate microresonators and graphene-enhanced devices. His recent contributions span innovations in photonic flywheel systems for stable frequency combs, broadband magnetometry using magnetic nanoparticles, and lidar measurement techniques. He has pioneered methods for deterministic microcomb generation and explored applications in high-resolution imaging and biochemical sensing. His research bridges fundamental physics with engineering applications, addressing challenges in precision metrology and quantum-enabled technologies. Dr. Huang's lab is located in ECEE 1B79, and his work is supported by grants focusing on nonlinear optics, ultrafast lasers, and integrated photonics. His team actively collaborates on projects involving coherent dual-comb spectroscopy, electrically tunable frequency combs, and nanophotonic devices for next-generation optical systems.
Yoeri R.J. Poels is a researcher at Eindhoven University of Technology specializing in fusion energy and artificial intelligence applications. His work focuses on enhancing tokamak operations through data-driven modeling and AI techniques, with significant contributions to the Eurofusion Tokamak Exploitation Team and MAST Upgrade collaboration. His research interests bridge fusion energy engineering and artificial intelligence , particularly in developing surrogate models and deep learning algorithms for tokamak control systems. Key areas include power exhaust management, divertor technology, and real-time plasma control solutions for next-generation fusion reactors. His work integrates physics-based modeling with advanced machine learning techniques to address critical challenges in fusion energy development. Analysis of his publication record reveals a strong trajectory in applying AI methods to fusion challenges , with increasing focus on practical implementation in experimental tokamak devices. His recent work demonstrates how data-driven approaches can enhance control systems for managing transient heat loads and improving reactor stability. As part of major international collaborations including the MAST Upgrade team and TCV tokamak research, Poels contributes to cutting-edge fusion research across multiple institutions. His work appears in high-impact journals including Nature Energy , Communications Physics , and Nuclear Fusion , reflecting the interdisciplinary nature of his research at the intersection of physics, engineering, and computer science.
Marianna Safronova is a Professor in the Department of Physics & Astronomy at the University of Delaware, affiliated with the College of Arts & Sciences. She leads the Safronova Group, which focuses on quantum metrology, dark matter searches, and precision atomic calculations. Her work spans quantum sensors for space applications, atomic clock development, and fundamental physics tests. Education details are not explicitly provided, but her extensive contributions suggest advanced training in theoretical atomic physics. Her research emphasizes high-precision atomic data via the Atomic Data Portal, collaborations on the Thorium Nuclear Clock project (ERC Synergy Grant), and NSF-funded quantum sensing algorithms. Key research areas include: quantum sensors for dark matter detection, Coulomb-crystal clocks, space-based quantum technologies, and applications of neural networks in atomic physics. She co-organizes the 2025 Bad Honnef Physics School on quantum metrology for new physics searches. Labs/Teams: Safronova Group, Q-SEnSE Institute, Thorium Nuclear Clock collaboration. Current projects involve space mission proposals for dark matter detection and development of next-generation atomic clocks.
Dirk Thißen is a Postdoctoral Researcher at RWTH Aachen University, affiliated with the COMSYS research group. His work focuses on distributed systems, web services, and quality of service (QoS) management. He has contributed to interdisciplinary projects like SensorCloud, aiming to develop trustworthy platforms for interconnected sensors and actuators. His research spans service-oriented architectures, network protocols (e.g., SIP/IMS), and middleware solutions for collaborative engineering and distributed systems. Key areas: Sensor networks, cloud computing, QoS optimization, and service composition Notable projects: SensorCloud, electronic service markets, and IMS service validation Thißen's publications address challenges in distributed systems, including service replication for reliability, multimedia integration in design processes, and protocol validation for next-generation networks. His work often bridges theoretical concepts with practical implementations in telecommunications and collaborative environments. His research consistently emphasizes performance evaluation and scalability, with contributions to both academic conferences and industry-relevant applications. While no specific awards are noted in the provided text, his extensive publication record reflects sustained engagement in high-impact fields.
Prof. Torsten Braun is a Professor and Head of the Communication and Distributed Systems (CDS) research group at the Institute of Computer Science, University of Bern. His research focuses on advanced networking technologies, edge computing, and machine learning applications in telecommunications. He leads projects addressing challenges in 5G/6G networks, federated learning optimization, and intelligent systems for smart cities. His work integrates theoretical frameworks with practical implementations, emphasizing distributed systems, service-oriented architectures, and IoT security. Key research areas include edge caching strategies for VR/AR applications, trajectory prediction using reinforcement learning, and resilient network design against jamming attacks. Braun has contributed to innovations in vehicular networks (V2X), RAN intelligence, and decentralized machine learning frameworks. He holds leadership roles in developing adaptive resource management systems for edge-cloud environments and has pioneered solutions for energy-efficient federated learning in heterogeneous IoT ecosystems. Publications span topics like spatial-temporal point cloud sensing, mobility-aware service orchestration, and secure positioning systems. His team explores cross-disciplinary applications such as LoRaWAN-based urban heat monitoring and blockchain-inspired public key infrastructures for IoT (Veritaa-IoT). Braun actively engages in standardization and industry collaborations to advance next-generation network architectures.
Jean-Paul M.G. Linnartz is Full Professor in the Signal Processing Systems group at Eindhoven University of Technology and Research Fellow at Signify (Philips Lighting). His research spans optical wireless communications, security systems, and signal processing algorithms. Key contributions include: Pioneering work on LiFi technology and visible light communication systems Innovations in biometric security and electronic watermarking OFDM and MC-CDMA techniques for wireless networks Publications show consistent focus on bridging fundamental algorithms with practical applications in lighting and communication systems. Recent work advances LED channel modeling and photonic communication frameworks. He holds over 75 patents and has co-founded three technology ventures. As Senior Director at Philips Research (1995-2008), he led groups in security, connectivity, and IC design. Honors include IEEE Fellowship (2011) for contributions to communications technology. Current projects include OptiLiFi (optimizing LiFi communication) and ELIoT (Enhance Lighting for Internet of Things), developing next-generation optical wireless networks.
Sara Betancur Giraldo is an Education/Research Assistant in the Department of Mechanical Engineering at Eindhoven University of Technology. Her work focuses on the intersection of autonomous mobility systems and fairness optimization, particularly in intermodal transportation networks. Research Interests: Using rigorous mathematical modeling, she investigates accessibility fairness in autonomous mobility-on-demand systems, operational cost optimization, and network flow dynamics. Her research aligns with UN Sustainable Development Goals related to sustainable cities and transportation. Research Output: In 2024, she published a peer-reviewed conference article titled On Accessibility Fairness in Intermodal Autonomous Mobility-on-Demand Systems in IFAC-PapersOnLine . This work introduces fairness-aware optimization frameworks for next-generation mobility systems. Her thesis Optimization Models for Accessibility-fairness-aware Planning of Intermodal Mobility Systems was supervised by Dr. M. R. U. Salazar, Dr. F. Paparella, and Dr. L. Pedroso.
Aurina Arnatkeviciute is a Research Fellow at Monash University's Turner Institute for Brain & Mental Health within the Faculty of Medicine, Nursing and Health Sciences. Her work focuses on the intersection of neuroscience, genetics, and psychiatry, with particular emphasis on brain connectivity, neuroimaging, and the genetic underpinnings of psychiatric disorders. She actively contributes to large-scale international collaborations including the ENIGMA consortium. Dr. Arnatkeviciute's research interests span multiple domains of cognitive neuroscience and psychiatric research. She investigates how genetic factors influence brain connectivity and network organization, with applications to understanding conditions like schizophrenia, ADHD, and autism spectrum disorders. Her work bridges molecular neuroscience with systems-level brain organization, utilizing advanced neuroimaging techniques combined with genetic and transcriptomic data. She has made significant contributions to imaging transcriptomics - the integration of brain-wide gene expression data with neuroimaging findings. Her publication record demonstrates expertise across multiple methodologies including genome-wide association studies, diffusion MRI connectomics, and large-scale international collaborations. Her recent work examines how socioeconomic factors like gender inequality impact brain structure, how genetic variations affect inhibitory control, and the neuroanatomical signatures of schizotypy across diverse populations. Australasian Cognitive Neuroscience Society (ACNS) Emerging Researcher Award (2020) Australasian Neuroscience Society Paxinos-Watson Award for the most significant neuroscience paper published by a member of the Society (2022) Discovery Early Career Researcher Award (DECRA) (2022) Monash University Dean's Award for Doctoral Thesis Excellence for "Genetics of brain network hubs" (2020) Monash University Early Career Researcher Publication Prize for "A practical guide to linking brain-wide gene expression and neuroimaging data" (2020) Dr. Arnatkeviciute serves as a supervisor for Honours students at Monash University, teaching units including PSY4215: Advanced Data Science and PSY4130: Developmental Psychology and Clinical Neuroscience. She is a Chief Investigator on the NHMRC-funded project "Neuropharmacology of decision-making: causal brain network modelling across species" (2022-2026), demonstrating her leadership in securing competitive research funding. Her supervisory activities extend through 2025, indicating ongoing commitment to mentoring the next generation of neuroscience researchers. As a member of the Turner Institute for Brain & Mental Health, Dr. Arnatkeviciute collaborates with a multidisciplinary team of neuroscientists, clinicians, and data scientists. Her work within the ENIGMA consortium connects her with researchers across more than 30 countries, facilitating large-scale analyses of brain structure and function across diverse populations. This collaborative environment supports her research at the intersection of genetics, neuroimaging, and psychiatric disorders.
