Stavrakakis Ioannis is a Professor at the Department of Informatics and Telecommunications, School of Science, University of Athens, where he has served since 2002. He previously held academic positions at Northeastern University (1994-1999) and University of Vermont (1988-1994). Ph.D., Electrical Engineering (1988), University of Virginia Diploma, Electrical Engineering (1983), Aristotle University of Thessaloniki His research focuses on network resource allocation algorithms , cooperative content dissemination , mobile ad hoc networks , and privacy-aware protocols . He leads the Advanced Networking Research (ANR) Group. Recent publications highlight trends in AI-driven network optimization , edge computing for VR , drone-assisted sensor networks , and privacy in vehicular systems . Key themes include game theory applications, energy-efficient protocols, and distributed learning frameworks. Contact: ioannis@di.uoa.gr
Greg Ganger is the Jatras Professor of Electrical and Computer Engineering at Carnegie Mellon University and Director of the Parallel Data Lab (PDL). His research focuses on computer systems, including cloud computing, storage systems, distributed systems, and machine learning infrastructure. He holds a Ph.D. in Computer Science and Engineering from the University of Michigan and completed postdoctoral work at MIT. Education: Ph.D., M.S., and B.S. in Computer Science from the University of Michigan (1991–1995). Research Interests: Ganger leads projects in cloud computing, storage/file systems, operating systems, and systems for big data and large-scale machine learning. Recent work includes optimizing cloud resource scheduling, developing sustainable storage solutions, and improving ML cluster efficiency. The PDL explores storage system architecture, file systems, and leveraging new storage technologies like non-volatile memory (NVM). Awards: 2021 OSDI Best Paper, 2021 SOSP Best Paper, 2021 SoCC Test of Time Award, and 2021 R&D 100 Award. His team's work on Kangaroo caching and MACARON cloud caching exemplifies cutting-edge contributions. Advising & Grants: Advises graduate students in ECE and Computer Science. Active in grants related to distributed storage, cloud systems, and ML infrastructure. Collaborates with industry partners like Los Alamos National Lab on storage systems. Labs/Teams: Directs the Parallel Data Lab (PDL), a leading research group in storage and distributed systems. Collaborates with CMU’s CyLab on security aspects of storage systems and ML infrastructure.
János Kertész is a Professor at the Department of Network and Data Science at Central European University (CEU) since 2012, and previously held the position of Professor at the Budapest University of Technology and Economics (1992–2018). He obtained his PhD in Physics from Eötvös University (1980) and DSc from the Hungarian Academy of Sciences (1989). His research spans statistical physics applications, complex networks, and financial analysis. He has authored over 280 papers and served on editorial boards of journals like Journal of Physics A and Physical Review E . His research focuses on interdisciplinary topics including social network dynamics, systemic risk in economic systems, and algorithmic bias in digital environments. Notable awards include the Széchenyi Prize (Hungary’s highest scientific honor) and the Finland Distinguished Professorship. He has led projects such as SAI (Socially Explainable AI) and HUMANE-AI-NET, addressing algorithmic bias and AI ethics. His work bridges physics-based modeling with real-world social and economic systems, emphasizing computational approaches to corruption, opinion formation, and innovation diffusion. Key contributions include modeling cascading failures in interdependent networks and analyzing attention dynamics on platforms like Sina Weibo during the pandemic. He advises on systemic risk mitigation strategies and collaborates internationally, with visiting roles in Germany, the U.S., France, Italy, and Finland.
Andrea Kő is a researcher at the Corvinus University of Budapest's Institute of Data Analysis and Informatics, with a focus on artificial intelligence, fintech, and big data applications. She has held positions in the Department of Information Systems until 2022 before transitioning to her current role. Her work emphasizes investment recommenders, Industry 4.0 readiness, and e-government solutions. Her research spans financial technologies, manufacturing optimization, and organizational resilience in SMEs. Notable contributions include hybrid AI models for production systems and frameworks for digital transformation assessment. She actively contributes to international conferences like EGOVIS and BiDEDE, editing proceedings and presenting on topics such as robo-advisors and smart manufacturing. Key projects include the CCMS2.0e maturity model for Industry 4.0 adoption and studies on pandemic impacts on SMEs. Her work integrates machine learning (ANFIS, MMNN) with domain-specific challenges, addressing both theoretical and practical aspects of digital innovation across sectors.
Y. Charlie Hu is the Michael and Katherine Birck Professor of Electrical and Computer Engineering and Professor of Computer Science (by courtesy) at Purdue University, where he leads the PurNET Lab and contributes to the Systems and Networking Group. His research spans Mobile Systems, Distributed Systems, Operating Systems, and Computer Networks , with a focus on energy-efficient AI systems and edge computing. His groundbreaking work on smartphone energy management has been widely adopted by the mobile industry and recognized with multiple test-of-time awards , including from ACM SIGOPS and ACM SIGMOBILE . He has received prestigious honors like the NSF CAREER Award , Honda Initiation Grant , and industry accolades from Google Research and Qualcomm . Notable Funded Projects: NSF's NeTS: Black-box Optimization of White-box Networks (2023-2026) Intel -NSF's SPLICE initiative His research has produced 15+ PhD graduates now in academia (University of Arizona, Virginia Tech) and industry (Google, Apple, Qualcomm). The articles reflect a career-long focus on edge computing , 5G network optimization , and energy-aware systems , with recurring themes in mobile AR/VR , video analytics , and network protocol design . Scientific Awards Honda Initiation Grant NSF CAREER Award Purdue Early Career Research Award Google Research Award Qualcomm Faculty Award ACM SIGOPS EuroSys Best Student Paper Award ACM MobiCom Best Community Paper Award IEEE Fellow ACM Distinguished Scientist Purdue PRF Innovator Hall of Fame
Dr ASM Kayes serves as Senior Lecturer in Cybersecurity and Cyber Curriculum Lead at La Trobe University's Department of Computer Science and Information Technology, where he shapes cybersecurity education programs including Master's, Bachelor's, and Double Degrees. His academic journey began with a PhD from Swinburne University of Technology in 2015, followed by postdoctoral research at La Trobe before joining as Lecturer in 2019 and promotion to Senior Lecturer in 2022. His research spans critical cybersecurity domains including data security, privacy preservation, context-aware access control, malware/ransomware defense, and IoT/fog/cloud security leveraging AI/ML techniques. Dr Kayes has established himself as a leading voice in blockchain security frameworks, privacy policy analysis, and cyber incident response through publications in top-tier venues like ACM Computing Surveys, IEEE Internet of Things Journal, and Computers & Security. His recent publications reveal a strong trajectory toward integrating AI with traditional security frameworks, particularly in blockchain risk assessment (2025), cross-domain access control (2025), and IoT behavior prediction (2024). The research demonstrates consistent focus on practical security solutions addressing ransomware mitigation, privacy breaches, and emerging threats in decentralized systems. Over $880,000 secured as Chief Investigator for cybersecurity projects Australian Government Department of Social Services grant (2023-2026) for cyberbullying prevention AustCyber research funds with industry partners (2020-2023) SmartSat CRC and ASCRIN PhD scholarship grants (2021) Dr Kayes has successfully supervised 5 PhD candidates to completion and currently mentors 5 doctoral students across diverse topics including AI-driven threat hunting, satellite network security, and blockchain risk frameworks. His collaborative network spans UK, USA, Europe, and Asia, with active industry partnerships through Westpac, BHP, and Quantum Victoria. He serves on editorial boards for leading cybersecurity journals and has examined HDR dissertations globally, reflecting his significant standing in the academic community.
Prof. Mohammed Khalid is a Professor in the Department of Electrical and Computer Engineering at the University of Windsor. He specializes in FPGA-based systems, network-on-chip architectures, and hardware acceleration for signal processing applications. His leadership roles include serving on the executive committee of IEEE Canada. His research focuses on optimizing cryptographic hardware, automotive embedded systems, and efficient algorithm implementations on FPGAs. Key contributions include advancements in PUF-based security mechanisms, high-speed elliptic curve processors, and FPGA-accelerated machine learning algorithms. He has led projects in automotive radar systems and AUTOSAR configuration tools. His work emphasizes practical applications of hardware-software co-design principles. Research Highlights : Development of novel FPGA architectures for real-time signal processing Innovative approaches to resource-efficient cryptographic hardware Pioneering work on hybrid NoC architectures for multi-FPGA systems Awards : IEEE Windsor Section Award (2019) for group leadership Best Student Paper Award (2024) for supervised research by Mohit Sharma Prof. Khalid's 150+ publications span FPGA design methodologies, adaptive signal processing, and embedded systems security. His research group collaborates with industry partners to advance automotive electronics and IoT applications.
Yu Chen is a Professor in the Department of Electrical and Computer Engineering at Binghamton University, State University of New York. He leads the Ubiquitous Smart & Sustainable Computing (US2C) Lab and serves as Director of the Center for Information Assurance and Cybersecurity (CIAC). His research focuses on Trust, Security, and Privacy in Edge-Fog-Cloud Computing, IoT, and Smart Cities. Dr. Chen holds a PhD from the University of Southern California (2006), with prior research under Professors Kai Hwang and Anthony F. J. Levi. His work has been funded by NSF, DoD, AFOSR, and industrial partners, yielding over 200 publications. He is a Senior Member of IEEE and SPIE, and a member of ACM. Education: PhD in Electrical Engineering, University of Southern California (2006) Affiliations: Director, US2C Lab Associate Director, CIAC Research Interests: Smart Cities, Intelligent Surveillance, Edge-Fog-Cloud Computing, IoT Security, and Privacy-Preserving Technologies. His work emphasizes real-time systems, resilient edge architectures, and decentralized consensus protocols for IoT. Grants & Awards: Funded by NSF, DoD, AFOSR, NYS MDPI Computers 2019 Best Paper Award Best Student Poster Award (IEEE AIPR 2014) Students & Labs: Advised 19 students (PhD/Master’s). Key projects include secure edge video processing, ENF-based authentication, and blockchain for IoT. The US2C Lab explores smart city applications and edge computing resilience.
Benoit Champagne is a Full Professor in the Department of Electrical and Computer Engineering at McGill University, Montreal. His research focuses on statistical signal processing, with applications in wireless communications, multi-antenna systems, and adaptive filtering. He has held academic positions since 1990, including roles at INRS-Telecom before joining McGill in 1999. He teaches graduate and undergraduate courses such as ECSE 305 (Probability and Random Signals), ECSE 512 (Digital Signal Processing), and ECSE 617 (Array Signal Processing). Education: B.Eng. (Electrical Engineering) and M.Sc. (Physics) from Université de Montréal (1983, 1985), Ph.D. in Electrical Engineering from University of Toronto (1990). His research spans signal detection/estimation, speech enhancement, MIMO systems, and physical layer security, with over 150+ publications in top journals and conferences. He has supervised numerous graduate students and holds grants from NSERC, CFI, and industry partners like Nortel and Bell Canada. His work emphasizes practical implementations, including hybrid analog/digital beamforming for mmWave systems and energy-efficient resource allocation in D2D communications. He has contributed to IEEE standards through editorial roles (e.g., IEEE Transactions on Signal Processing) and conference organization (e.g., IEEE VTC 2016). Current research explores machine learning integration with signal processing for next-generation wireless systems. Notable contributions include advancements in subspace tracking, cognitive radar systems, and distributed adaptive filtering. His lab collaborates internationally, addressing challenges in 5G/6G networks, massive MIMO, and secure communications.
Samir Ouchani is a Research Director at the CESI LINEACT laboratory (Aix-en-Provence, France), affiliated with the CESI Engineering School. He holds a PhD in Computer Science from Concordia University (2013) and an HDR (Accreditation to Supervise Research) from CNAM Paris (2022). His research focuses on securing cyber-physical systems (CPS) through formal methods, blockchain, and AI-driven approaches. Key roles include leading projects on resilient CPS architectures, IoT security, and federated learning in industrial contexts. Education: 2022: HDR in Security and Reliability of Smart CPS (CNAM Paris) 2013: PhD in Computer Science (Concordia University, Montreal) 2006: Master in Computer Science (Lorraine University, France) 1997: Engineering Degree in Computer Science (Djillali Liabess University, Algeria) Research Interests: His work emphasizes secure CPS design, including cryptographic protocols for IoT, formal verification frameworks, and AI applications for intrusion detection. He explores blockchain for smart cities, federated learning in distributed systems, and resilience engineering for autonomous vehicles. Recent projects include developing PUF-based authentication protocols and digital twin architectures for resource-constrained systems. Advising & Collaborations: Supervised PhD theses on IoT security (Fahem Zerrouki), smart city formal verification (Walid Miloud Dahmane), and federated learning in industrial CPS (Souhila Bedra Guendouzi). Collaborates with institutions like Blida University (Algeria) and HESAM University. Active in conferences such as CRISIS, ICFNDS, and IEEE WETICE. Labs & Teams: Leads the Engineering and Numerical Tools research team at CESI LINEACT, focusing on model-based design, CPS simulation, and cybersecurity tool development. Engaged in EU-funded projects on Industry 4.0 and smart infrastructure security.
Aleksandar Jevremović is a Full Professor at the Faculty of Informatics and Computing, Singidunum University (Belgrade, Serbia), and holds multiple academic and professional roles. He is the Serbian representative at the UNESCO IFIP Technical Committee on Human-Computer Interaction since 2018. He has served as Vice-Dean of his faculty (2015–2018) and held visiting professorships at institutions like Ss. Cyril and Methodius University (North Macedonia) and Tallinn University (Estonia). His research focuses on cybersecurity, IoT, AI, and e-learning innovation. Education and Affiliations: External Researcher at the Mathematical Institute of the Serbian Academy of Sciences and Arts Visiting Scholar at Cyprus Interaction Lab (Cyprus University of Technology) Alumni/Postdoc Researcher at Tallinn University's HCI Group Member of IEEE and the Informatics Association of Serbia Research Interests: Jevremović’s work spans cybersecurity (e.g., intrusion detection, secure IoT protocols), human-computer interaction (HCI), AI-driven education tools, and neurotechnological applications like EEG-based assessment systems. He emphasizes practical solutions for digital safety, such as children’s online protection and cryptographic key generation from biometric data. Grants and Projects: Member of the External Advisory Committee for the EU-funded ONTOCHAIN project (2022–2023) Mentor for training schools like AAPELE Training School and NET4Age-Friendly initiatives Trainer in IoT, cybersecurity, and health promotion programs across Europe Labs and Teams: He collaborates with interdisciplinary teams on projects like CASPER (Children Agents for Secure and Privacy Enhanced Reaction) and led the development of WIDE, a collaborative web development education platform.
Dr. Man Ho Allen Au is an honorary professor at the Department of Computer Science, University of Hong Kong (HKU), with a career spanning cutting-edge research in applied cryptography, information security, and blockchain technology. His work bridges theoretical innovation and industrial applications. BEng, MPhil from the Chinese University of Hong Kong (2003, 2005) PhD from University of Wollongong (2009) A former associate professor at Hong Kong Polytechnic University, Dr. Au’s research drives advancements in blockchain technology , applied cryptography , and privacy-preserving techniques , with deployments in Hyperledger Fabric and discussions by privacy-focused currencies like Monero. His publications in top venues (CRYPTO, IEEE S&P, CCS) focus on lattice-based cryptography , secure protocols , and privacy-enhancing technologies . These works have attracted over 25 million HKD in grants, including RGC GRF and ITF projects. 2021 : New Frontiers of Lattice-Based Cryptography (RGC GRF), Secure Computation over Encrypted Data (ITF Seed), Blockchain Platform for Data Sharing (ITF Platform) 2020 : Practical Post-Quantum Zero-Knowledge Proofs (RGC GRF), NSFC project on quantum-resistant schemes 2019–2017 : Blockchain for logistics, anti-counterfeit systems, and post-quantum signature constructions Awarded the 2009 PET runner-up for privacy research, Dr. Au contributes to standards via ISO/IEC JTC 1/SC 27 working group and the Hong Kong Blockchain Society.
MA Dong is an Assistant Professor at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU). He holds a PhD from the University of New South Wales (2020), and was a postdoctoral researcher at the University of Cambridge. His research focuses on mobile computing, wearable-based human sensing systems, and health monitoring using embedded machine learning. He is also a visiting researcher at the University of Cambridge since April 2025 and will join as an Associate Professor there in 2026. Education: PhD (UNSW), MEng and BEng (Central South University). Research Interests: Wearable systems for health monitoring (e.g., respiratory rate tracking, gait analysis), robust physiological sensing, tiny machine learning on embedded devices, and human-computer interaction through earables. His work emphasizes practical implementations of wearable technology for real-world scenarios, such as smart earbuds for authentication, health tracking, and activity sensing. Publications: Over 50+ peer-reviewed articles in top venues like MobiCom, PerCom, CHI, and Nature journals. Key topics include earable technology, ECG-text multimodal learning, and energy-efficient sensing systems. Awards: Google South Asia Research Award (2024), Mark Weiser Best Paper Awards (2024, 2025), and EPFL Engineering Ph.D. Summit recognition. Students: Advising PhD candidates Xiao Ma, PHAM Hung Manh, and Changshuo Hu. Also hosts visiting scholars from Shandong University and Beijing Institute of Technology.
Tom Goethals is an FWO Junior Postdoctoral Fellow affiliated with the Department of Information Technology at Ghent University , where he conducts research in edge computing, container networking, and decentralized systems. Current role: IMEC Postdoctoral Researcher Research focus: Secure and intelligent edge service management for decentralized IoT applications His work explores edge intelligence , orchestration frameworks , and AI-driven network optimization , with trends in lightweight virtualization (e.g., Feather), Kubernetes adaptation for edge environments, and intent-based decentralized orchestration. Publications emphasize scalability, security, and energy efficiency in fog-native workflows. Scientific Awards : FWO Junior Postdoctoral Fellowship He collaborates with researchers like Bruno Volckaert and Filip De Turck on projects funded by the Research Foundation - Flanders (FWO) , including grants for edge container networking and decentralized learning frameworks. His projects align with Ghent University’s focus on smart city infrastructure and edge-to-cloud systems.
Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University working with 5G and Internet evolution. She completed her PhD in computer science in 2017 at the Simula Research Lab and Universitetet i Oslo under the supervision of Dr. Ozgu Alay and Prof. Michael Welzl. Her PhD dissertation focused on increasing robustness in multipath transport with MPTCP. Dr. Ferlin's educational background includes a PhD in Computer Science from the Simula Research Lab and Universitetet i Oslo (2017). Her doctoral research centered on enhancing robustness in multipath transport protocols, specifically focusing on MPTCP (Multipath TCP). She also completed undergraduate work that contributed to a book project with Prof. Friedrich Oehme on electronics and circuit technology. Dr. Ferlin's research spans multiple domains at the intersection of networking, systems, and performance engineering. Her primary interests include network and system measurements, performance analysis, security, and congestion control. She investigates how networks like the Internet evolve, examining technology development, adoption patterns, and their impacts on various entities. Additionally, she explores ways to harmonize security and privacy while making them more usable and assessable. Her work particularly focuses on transport layer and multipath transport protocols, examining their performance and security aspects. She also investigates application and transport layer performance, automation, and monitoring. Her research extends to network programming in both Linux kernel and user space, mobile broadband networks from 2G to 5G, and their intersection with the Internet. She is deeply engaged in observability, distributed and system performance monitoring, and automation. Analysis of Dr. Ferlin's recent publications reveals a strong focus on next-generation networking technologies. Her work spans multiple domains including 5G/6G networks, transport protocols (particularly QUIC and MPTCP), network virtualization, container orchestration, and the application of machine learning to networking problems. She has increasingly incorporated large language models into network configuration and automation research. Her publications demonstrate a consistent emphasis on performance measurement, optimization, and security across diverse networking environments from the edge to the cloud. Dr. Ferlin has received notable recognition for her research contributions: Best paper award at IEEE ICIN'21 for 'Learning-based Incast Performance Inference in Software-Defined Data Centers' Applied Networking Research Prize (ANRP)'25 winner for 'NetConfEval: Can LLMs Facilitate Network Configuration?' Dr. Ferlin is actively involved in mentoring the next generation of networking researchers. She has co-supervised numerous Master's and PhD students across multiple institutions including Karlstad University, KTH, TU Berlin, University of Oslo, and universities in Brazil. Her students have worked on diverse topics including NAT64 performance comparison, system tracing visualization, network observability, ML applications to multipath transport, FEC integration with QUIC, high-performance networking for 5G, congestion control, shared bottleneck detection, multipath IoT applications, and container runtime performance. She is also involved in several significant research projects including Vinnova's SEMLA (Securing Enterprises via Machine-Learning-based Automation), Horizon Europe's CODECO (Cognitive Decentralised Edge Cloud Orchestration), and the Knowledge Foundation of Sweden's DRIVE (Data-driven Latency-Sensitive Mobile Services for a Digitized Society). Dr. Ferlin serves as Workshop Chair for ACM SIGCOMM '25, is a member of the ACM/IRTF Applied Networking Research Workshop (ANRW) steering committee, and co-chairs the Internet Congestion Control Research Group (ICCRG) at the IRTF. She previously served as Associate Technical Editor for IEEE Communications Magazine and has been active on numerous program committees for major networking conferences including SIGCOMM, CoNEXT, IMC, and PAM.
