Tor Skeie is a Professor at the University of Oslo's Department of Informatics, specializing in networks and distributed systems. His research focuses on high-performance networking, InfiniBand technologies, and adaptive routing systems. Current investigations include automated parameter tuning for reservoir simulations, adaptive routing in InfiniBand hardware, and modeling WiFi quality attenuation. His work develops efficient solutions for virtualized HPC environments and cloud computing infrastructures. Recent publications demonstrate innovations in network modeling, adaptive routing algorithms, and performance analysis of distributed systems. Research collaborations span European projects on high-performance networking infrastructures. Leads the Networks and Distributed Systems (ND) research group investigating fault-tolerant routing, network virtualization, and congestion control mechanisms.
George Exarchakos is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), affiliated with the EAISI High Tech Systems and the Center for Wireless Technology. His research focuses on P2P computing, data mining, machine learning, network optimization, and swarm intelligence. He holds an MSc in Advanced Computing from Imperial College London and a PhD in P2P Computing from the University of Surrey (2008). Prior to his current role, he conducted postdoctoral research on autonomous networks at TU/e before becoming an Assistant Professor in 2011. Key projects include HiCONNECTS: Heterogeneous Integration for Connectivity and Sustainability (2023–2025) and RHIADA: Reliable Hybrid Intra Aircraft Datanetwork Architectures (2021–2025). His work contributes to UN Sustainable Development Goals related to innovation and infrastructure (Goal 9). Research interests span predictive networks, gossip protocols, overlay networks, and network complexity. Notable publications include studies on beyond-5G networks, avionics communication protocols, and edge computing resource management.
Ingrid Moerman is a part-time Professor at Ghent University and a staff member at the Internet Technology and Data Science Lab (IDLab), a core research group of imec embedded within Ghent University and the University of Antwerp. She coordinates mobile and wireless networking research and leads a team of over 30 researchers at Ghent University, with extensive involvement in European and national funding initiatives. She received her Electrical Engineering degree (1987) and Ph.D. (1992) from Ghent University. Her research spans collaborative networks, cognitive radio, software-defined radio, IoT, LPWAN, and high-density wireless access, emphasizing experimentally-supported development of next-generation wireless systems with practical implementations in spectrum management and real-time control. Recent publications (2024-2025) reveal a strong pivot toward AI-integrated wireless networking, featuring OFDMA scheduling innovations, Wi-Fi 6/7 interference mitigation, and time-sensitive networking for industrial applications. Key trends include 5G/6G convergence, vehicular communication enhancements, and digital twin frameworks for network observability, reflecting her focus on mission-critical industrial use cases. Her accolades include: 9 Best Paper Awards 2 FWO Prizes (Research Foundation - Flanders) IMEC Prize of Excellence 2001 MSc Thesis Award (as promoter) Best Demo/Exhibit Award at ICT 2013 DARPA Spectrum Collaboration Challenge Prize ($750,000) She has coordinated major EU projects (FP7/H2020: CREW, WiSHFUL, eWINE, ORCA) with industry partners, securing substantial funding for experimental wireless research. Her grant portfolio emphasizes collaborative innovation in spectrum sharing and neutral-host architectures for multi-operator environments. At IDLab, she directs advanced wireless testbeds supporting real-world validation of technologies like openwifi and White Rabbit, with active experimentation in time-sensitive networking and spectrum collaboration for industrial IoT deployments.
Christina Delimitrou is an Associate Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on optimizing cloud computing systems, with a strong emphasis on resource management, sustainability, and machine learning-driven solutions. Delimitrou leads projects on carbon-aware scheduling, efficient datacenter operations, and serverless computing frameworks like Ursa and Ditto. Her work bridges theoretical system design with practical deployment challenges, addressing topics such as microservices orchestration, approximation techniques for resource efficiency, and security implications of multi-tenancy in shared cloud environments. Notably, she received the Presidential Early Career Award for her contributions to improving datacenter efficiency through innovative scheduling and resource allocation strategies. Delimitrou's research group develops tools like Sage (ML-driven performance debugging) and Seer (big data analytics for cloud systems), emphasizing reproducibility and scalability. Her lab also explores edge computing, swarm robotics coordination (e.g., Hivemind), and hardware-software co-design for next-generation systems. Her academic affiliations include MIT CSAIL's Systems Community of Research, where she collaborates on large-scale software systems. Key themes in her work include QoS-aware resource management, sustainable computing practices, and leveraging approximation to enhance cloud resource utilization.
Dr. Suranga Seneviratne is a Senior Lecturer in Security at the School of Computer Science, University of Sydney. He holds a PhD from the University of New South Wales (2015) and a Bachelor's degree from the University of Moratuwa, Sri Lanka (2005). Before academia, he worked in telecommunications for six years. His research focuses on cybersecurity, particularly privacy and security in mobile systems, AI applications in security, and behavioral biometrics. He has developed tools like an app security rating system and intrusion-free authentication methods. Key awards include the ACM Mobicom 2015 Gold Prize, NASSCOM Technical Innovation Award, and IESL NSW Engineering Excellence Award (all 2015). Current research students include Pasindu Marasinghe (Multi-Objective Optimization in Flat Glass Cutting Production), Braylon SHU (Efficient Parameter Tuning for Large Language Models), and Gaurav VERMA (Threats and Defenses in IoT Wireless Protocols). Grants include funding from the Australian Research Council, NSW Network for Cyber Security, and Google Research. His work spans collaborations with the NSW Smart Sensing Network and the University of Technology Sydney. Labs/Teams: Collaborates with the Centre for Distributed and High-Performance Computing and the NSW Smart Sensing Network (NSSN).
Dr. Hossein Sayadi is an Assistant Professor and Associate Chair in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He holds a Ph.D. in Electrical and Computer Engineering from George Mason University, an M.S. from Sharif University of Technology, and a B.S. from K. N. Toosi University of Technology. His research focuses on hardware security , AI/ML applications , cybersecurity , and computer architecture . He leads the iSEC Lab , exploring topics like hardware trust, malware detection, and edge computing security. His work is supported by NSF grants and CSU awards, including the 2024-25 CSU STEM-NET Faculty Fellowship. Education: Ph.D., Electrical and Computer Engineering (George Mason University) M.S., Computer Engineering (Sharif University of Technology) B.S., Computer Engineering (K. N. Toosi University of Technology) His publications span conferences like IEEE ISQED, ISCAS, and DATE. He serves as Technical Program Committee Chair for IEEE ISQED (2024–2025). Awards include NSF ERI grants ($195,305) and the 2023 Multidisciplinary Research Grant. Research opportunities are available for students in machine learning , hardware security , and cybersecurity education .
Shweta Jain is a Professor in the Department of Mathematics and Computer Science at John Jay College of Criminal Justice, part of the City University of New York (CUNY). She holds dual roles as Graduate Faculty in the Digital Forensics and Cyber Security program and Doctoral Faculty in Computer Science at CUNY's Graduate Center. With a Ph.D. in Computer Science from Stony Brook University (2007), her expertise spans Cybersecurity, Blockchain, Wireless Networks, and Software Development. Education Background: Ph.D. Computer Science, Stony Brook University, 2007 M.S. Computer Science, Stony Brook University, 2005 B.E. Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology (IIEST) Shibpur, 2005 Research Interests: Cybersecurity frameworks and digital forensics Blockchain applications in social systems Wireless network protocols and security Perceptual hashing for image authentication Network vulnerability analysis Notable Achievements: Recipient of 2014 IEEE Region-1 Award for Outstanding Teaching Senior Member of IEEE Over 30 peer-reviewed publications and patents in networks, forensics, and distributed systems Advising & Grants: Guided multiple student research projects in network security and forensics Developed innovative tools like E-Witness for digital evidence preservation Contributed to NSF-funded projects on wireless simulation realism Labs & Teams: Director of the Cybersecurity Research Lab at John Jay College Collaborates with WINLAB at Rutgers University on wireless protocols
Tara Javidi holds the Jerzy (George) Lewak Endowed Chair and is a Professor in the Department of Electrical and Computer Engineering and Halicioglu Data Science at the University of California San Diego (UCSD). She leads multiple initiatives, including serving as Founding CTO of KavAI, Co-Director of the Center for Machine Intelligence, Computing and Security, and Co-Principal Investigator (CoPI) of the NSF AI Institute TILOS. Her research focuses on stochastic analysis, design, and control of information systems, emphasizing active learning, decentralized optimization, and wireless networks. Key areas include information acquisition/utilization, stochastic control, and AI-driven communication solutions. Her work bridges theoretical foundations and practical implementations, such as drone systems for information gathering (via detecdrone.ucsd.edu) and optical data center networking. Notable contributions include end-to-end scheduling for all-optical data centers and hybrid wireless-optical architectures. Javidi is an IEEE Fellow and has received significant grants, including leading UCSD’s Schmidt AI in Science Postdoctoral Fellowship program. She actively collaborates with industry and academia, with a focus on next-generation wireless networks and decentralized systems. Education: Ph.D. in Electrical Engineering (implied from title). Affiliations: IEEE Journal of Selected Areas in Information Theory (Editor-in-Chief), CALIT2, CNS, and TILOS. Grants: NSF AI Institute TILOS ($20M over 5 years), Schmidt AI Fellowship program. Her research group emphasizes both theoretical rigor (e.g., sequential hypothesis testing) and practical testing, with applications in service drones, cognitive networks, and federated learning. Recent articles highlight advancements in optical networking, secure communication, and distributed learning protocols. Awards: IEEE Fellow, Jerzy Lewak Chair. Labs/Teams: Center for Machine Intelligence, TILOS Institute, KavAI, and UCSD’s AI in Science initiatives.
Marco Farina is a Full Professor in Electromagnetics at the Department of Information Engineering, College of Engineering, Polytechnic University of Marche, Italy. His research spans electromagnetic modeling, scanning microwave microscopy, and nanotechnology, with applications in 2D materials, biosensors, and advanced measurement systems. He is a Senior Member of IEEE and actively contributes to Technical Committees on RF Nanotechnology. Laurea and Ph.D. in Electronic Engineering from University of Ancona Research interests focus on quantitative scanning microwave microscopy, electromagnetic analysis of active/passive components, and nanoscale characterization techniques. His work bridges theoretical modeling with practical implementation, including the development of the EM3DS software suite and novel inverted SMM systems. Recent publications highlight interdisciplinary applications in biomedical analysis and semiconductor physics. Scientific recognition includes the 3M-Nano Best Conference Paper Award and grants from US Army Research Laboratory and US Air Force Office of Scientific Research. He holds an ESA-funded patent for VNA calibration and has co-authored a book on planar structure analysis. Collaborative projects emphasize RF device optimization and biological imaging.
Dr. Shekhar Bhansali is the Alcatel-Lucent Professor and Chair of the Department of Electrical & Computer Engineering (ECE) at Florida International University (FIU) since 2011. He holds a BS in Metallurgical Engineering (1987), MS in Aircraft Production Engineering (1991), and PhD in Electrical Engineering (1997). His research focuses on bio sensing, nanotechnology, alternative energy, and oceanographic sensing. He leads the Bio-MEMS and Microsystems Lab, holds 36 U.S. patents, and has secured funding from NSF, industry partners, and national labs like Sandia and Los Alamos. Dr. Bhansali has grown the ECE department by launching programs like the online Master of Science in Network Security and the B.S. in Internet of Things (first in the U.S.). He co-directs FIU’s Bridge to the Doctorate program, fostering STEM diversity. Awards include the 2014 FIU Top Scholar Award and 2018 AAAS Fellowship. Education: PhD in Electrical Engineering, RMIT University (1997) MS in Aircraft Production Engineering, IIT Madras (1991) BS in Metallurgical Engineering, MREC, Jaipur (1987) Research Interests: Bio-sensing, nanotechnology, alternative energy, oceanographic sensors, and materials science. Key Achievements: 36 U.S. patents and 7 invention disclosures Co-authored 139 journal papers and 200+ conference papers Recruited 14 faculty members and expanded doctoral programs Partnership with Florida Power & Light for solar energy research His work bridges innovation and societal impact, with sensors for wound monitoring, environmental sensing, and energy efficiency. Recent studies include AI-driven sensor networks for precision agriculture and wearable devices for real-time health diagnostics. Awards & Recognition: 2018 AAAS Fellow 2014 FIU Top Scholar Award Multiple mentorship awards (2003–2011) Advising & Grants: Oversaw education of over 150 graduate students via NSF-IGERT and Sloan programs. Secured grants totaling millions for interdisciplinary research. Expanded FIU’s engineering programs and faculty size. Labs & Teams: Leads the Bio-MEMS Lab, advancing micro/nano sensors and lab-on-a-chip technologies. Collaborates with industry and national labs on sensor development and energy projects.
Asmus Skar Christiansen is an Associate Professor in Pavement Engineering at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU Sustain). He serves as Head of Study for the Nordic Master in Cold Climate Engineering programme and lectures on pavement engineering, Arctic road construction, and foundation design. His academic career at DTU spans from Postdoc researcher (2017-2019) to Assistant Professor (2020-2023) and current Associate Professor position since 2023. His research centers on pavement technology and geotechnics with specialization in: Development of advanced testing and modeling techniques for pavements Integration of modern sensing technologies in civil infrastructure Computational mechanics for soil-structure interaction Sustainable materials for cold climate engineering Recent work demonstrates a clear shift toward IoT-enabled monitoring systems and data-driven pavement assessment, with 80% of 2023-2025 publications focusing on sensor integration and machine learning applications. Notable scientific contributions include: Creation of open-source datasets (LiRA-CD, RIVA) for road condition modeling Development of thermomechanical models for heated pavements Innovations in waste soil reuse for infrastructure He actively supervises PhD candidates across multiple projects including GREENPIPE (self-sensing pipe systems) and urban pavement analysis, while maintaining industry consultancy through COWI A/S collaborations. Christiansen also contributes to sustainable infrastructure through DTU's alignment with UN SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities).
Zoran Gajic is a Professor of Electrical and Computer Engineering at Rutgers University, where he has taught since 1984. He holds academic leadership roles including Graduate Program Director for the Electrical and Computer Engineering Department and President of the Rutgers AAUP-AFT Faculty Union. His expertise spans controls systems, energy systems (including solar, wind, and smart grids), wireless communications, and networking. Education: B.S. and M.S. in Electrical Engineering from University of Belgrade, followed by M.S. in Applied Mathematics and Ph.D. in Systems Science Engineering from Michigan State University (1984). Research focuses on control theory applications for energy systems and communication networks. He has authored/coauthored nearly 100 journal papers and eight books, including best-selling titles like Linear Dynamic Systems and Signals (translated into Chinese) and Lyapunov Matrix Equation in Systems Stability and Control (republished by Dover). His work includes innovations in multirate control systems, singular perturbation methods, and reinforcement learning applications. Professional recognitions include editorial roles across nine journals, five guest-edited special issues, and plenary lectures at international conferences. Ten of his 17 Ph.D. advisees hold faculty positions globally. Beyond academia, he is a chess master with Life Master ranking from the U.S. Chess Federation and World Chess Federation certification. Key contributions include foundational work on optimal control for renewable energy systems, sliding mode control algorithms, and system decomposition techniques. His research has been supported by NSF and industry partners like AT&T Bell Labs. He leads the Rutgers Center for Systems and Controls (SYCON) as Associate Director and actively contributes to standards through IEEE and IEC initiatives, particularly in power system protection and control system reliability.
John Harrison Kurunathan is an Integrated PhD Researcher affiliated with the CISTER Research Centre at the University of Porto, Portugal. He holds a PhD in Electrical and Computer Engineering (2021), a Master's in Very Large-Scale Integration (2014), and a Bachelor's in Electronics and Communication (2012). Education: PhD (2021) - University of Porto, Portugal MSc (2014) - SSN College of Engineering, Anna University BSc (2012) - SRM University His research focuses on Wireless Sensor Networks (WSNs) , Cyber-Physical Systems (CPS) , and Automotive Networks , with an emphasis on Quality-of-Service (QoS) optimization, secure communication, and vehicular platooning. Notable projects include SafeCOP for safety-related CO-CPS and work on IEEE 802.15.4e DSME networks. Recent publications (2023-2025) span areas like Visible Light Communication , Vehicular Security , and Machine Learning in UAV Operations , reflecting his interdisciplinary work bridging embedded systems and transportation technologies. Scientific Awards: Best oral communication Award (in ex aequo) at DCE 2019 Reviewing Roles: Conference: ICCPS, EWSN, MSN, RTN Journal: IEEE ACCESS, IEEE Transactions on Vehicular Technology, ACM Sigbed Harrison is actively involved in workshops and conferences, including chairing roles at WIN-WIN-4S 2024 and technical demonstrations at WoWMoM 2023. His work appears in venues like IEEE Transactions on ITS, IEEE COMST, and PDP 2025.
Jin Lu is an Assistant Professor at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He earned his Ph.D. (2019) and M.S. (2019) in Computer Science and Engineering from the University of Connecticut. Prior to his current role, he served as an Assistant Professor at the University of Michigan–Dearborn (2019–2023). Educational Background: Ph.D. in Computer Science and Engineering, University of Connecticut, 2019 M.S. in Computer Science and Engineering, University of Connecticut, 2019 Research Interests: Dr. Lu focuses on machine learning, optimization, bio-informatics, and smart mobility. His work spans federated learning, healthcare applications (e.g., depression and BMI monitoring), IoT systems, and computer vision. Recent projects explore AGI's potential in medical and educational contexts, leveraging models like CycleGAN and reinforcement learning. Grants & Funding: Develop digital brains to advance portable diagnosis of neurological conditions (Google, 2025) Lab/Teams: While specific lab affiliations are not explicitly stated, his research involves collaborations in interdisciplinary areas such as health informatics and smart mobility.
Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.
