Mauro Conti is a Full Professor at the University of Padua, Italy, where he serves as Editor-in-chief of IEEE TIFS, UniPD Academic Advisor for Entrepreneurship Development, Study Program Coordinator of the MSc degree in Cybersecurity, Head of the SPRITZ Security and Privacy Research Group, and Director of the UniPD node of CINI Cybersecurity National Lab. He also maintains affiliations with TU Delft (NL) and the University of Washington (USA). Additionally, he is the CEO and co-founder of CHISITO and co-founder of DYALOGHI. His research focuses on security and privacy for wireless resource-constrained mobile devices (WSNs, RFIDs, and smartphones), computer system security, computer forensics, access control, and distributed and networked systems. Professor Conti has published extensively in top security venues including ACM CCS, IEEE S&P, NDSS, and USENIX Security, with recent work covering topics such as battery authentication, federated learning privacy, private set intersections, USB peripheral fingerprinting, and ATM PIN inference. He has received numerous prestigious awards including IEEE Fellow, Young Academy of Europe Fellow, EU Marie Curie Fellow, and DAAD Fellow. Professor Conti has advised over 100 students at various levels including PhD, MSc, and BSc, many of whom have gone on to successful careers in academia and industry. His research has been supported by multiple European Commission projects, university grants, and industry collaborations with companies like Cisco and Intel.
Jiro Katto is a Professor at Waseda University's School of Fundamental Science and Engineering, where he has been conducting research and teaching since 1999. He received his Ph.D. from the University of Tokyo and has established himself as a leading researcher in multimedia signal processing and computer networks. His academic journey includes positions as Associate Professor (1999-2004), Professor (2004-present), and Director at NEDO (2004-2008), along with research experience at NEC C&C Laboratories (1992-1999) and a Visiting Scholar position at Princeton University (1996-1997). Professor Katto's research interests focus on Multimedia Signal Processing and Computer Networks, with particular expertise in video compression, 5G network performance, and learned image compression techniques. His work bridges theoretical advancements with practical implementations, as evidenced by his extensive publications in top-tier conferences and journals. His research group has made significant contributions to point cloud compression, latency compensation in remote systems, and hardware-accelerated video encoding for UHD streaming. His publication record is impressive, with 276 papers cited 3,323 times in Scopus and 6,169 times in Google Scholar, reflecting his substantial impact in the field. His recent work shows a strong trend toward applying deep learning techniques to traditional signal processing problems, particularly in the areas of image and video compression, where his team has developed novel approaches to improve compression efficiency while reducing computational complexity. Electric Telecommunications Promotion Foundation Telecommunications System Technology Award (2023) Takayanagi Kenjiro Foundation Takayanagi Kenjiro Achievement Award (2020) Institute of Image Information and Television Engineers Fellow (2020) Institute of Electronics, Information and Communication Engineers Fellow (2015) IEICE Communications Society Activity Contribution Award (2006) IEICE Academic Encouragement Award (1995) SPIE VCIP 1991, Best Student Paper Award (1991) Professor Katto has served on numerous prestigious committees including IEEE ComSoC Tech News Editorial Board, IEEE Technical Program Committees for major conferences (Globecom, ICC, ICIP), and editorial boards for several academic journals. His leadership in the academic community extends to chairing conferences like IWAIT 2011 and serving as Editor-in-Chief for journals in his field. His research has practical applications in commercial 5G networks, video streaming services, and remote monitoring systems, demonstrating the real-world impact of his work.
Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.
Xiaojiang Du is the Anson Wood Burchard Endowed Professor at Stevens Institute of Technology, directing research in IoT security, AI security, and wireless networks. An IEEE Fellow and ACM Distinguished Member, he leads NSF-funded projects on secure IoT systems and cross-platform security vulnerabilities. Education PhD in Electrical Engineering, University of Maryland MS in Electrical Engineering, Tsinghua University BE in Electrical Engineering, Tsinghua University Research Focus: Develops security frameworks for IoT ecosystems and adversarial machine learning, with recent breakthroughs in smart home security anomaly detection. Honors: IEEE Fellow, ACM Distinguished Member, multiple best paper awards at IEEE conferences. Graduated PhD students hold faculty positions at UNC Charlotte, UL Lafayette, and ShanXi University. Professional Service: IEEE ComSoc Distinguished Lecturer, Associate Editor for IEEE Transactions, and General Co-Chair for IEEE/ACM IWQoS 2023. Secured $9M+ in research funding from NSF, NSA, and DOD.
Petri Mähönen is a Full Professor at the Department of Information and Communications Engineering , Aalto University. His research focuses on networked systems, machine learning applications in telecommunications, and smart grid technologies. University: Aalto University Department: Information and Communications Engineering Research Interests span networked systems, IoT security, UAV communication, and AI-driven network optimization. His work addresses predictive QoS in cellular-connected drones and generative adversarial networks for cybersecurity. Recent Publications include studies on GAN-based traffic augmentation, anomaly detection in mobile networks, and regulatory frameworks for data platforms. His articles reflect expertise in both theoretical and applied network science.
Dr. Wenjing Zhang is an Assistant Professor in the School of Computer Science at the University of Guelph, Canada. She holds a Ph.D. in Computer Science from the University of Guelph (2024) and was a visiting research scholar at the University of Arizona's Department of Electrical and Computer Engineering (2016–2018). Her research focuses on cybersecurity in AI/ML, including security threats to models, privacy-preserving techniques, and data privacy in generative AI. She leads projects on robust defenses against adversarial attacks, secure federated learning, and privacy-preserving prompt engineering for LLMs. Dr. Zhang’s research areas include: Security in AI/ML (e.g., poisoning, evasion, prompt injection attacks) Model Privacy (protection of internal parameters) Data Privacy (synthetic data generation, privacy-preserving prompt engineering) She has secured a five-year NSERC Discovery Grant (2025–2030) for her research on enhancing security, privacy, and fairness in generative AI. Her work has been published in top-tier venues such as NeurIPS, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Communications. She is a recipient of the 2022 Westin Scholar Award and serves on technical committees for IEEE conferences including CNS 2025 and ICC 2025. Her team collaborates on interdisciplinary projects involving federated learning, information theory, and reinforcement learning. She actively seeks partnerships in security, privacy, and generative AI applications.
Don Towsley is a Distinguished University Professor in the Department of Computer Science at the University of Massachusetts Amherst, within the College of Information and Computer Sciences. He has held visiting positions at AT&T Labs, IBM Research, INRIA, Microsoft Research Cambridge, and the University of Paris 6. He earned a B.A. in Physics and a Ph.D. in Computer Science from the University of Texas. Prof. Towsley's research spans network science, measurement, modeling, and analysis, with recent emphasis on quantum networking and wireless security. His work addresses foundational challenges in network tomography, entanglement distribution, and quantum communication protocols, contributing to efficient and secure next-generation networks. Analysis of his 2022-2025 publications reveals a dominant focus on quantum networking—including quantum internet architecture, entanglement distribution, and tomography—alongside continued contributions in classical networking areas such as DDoS detection and edge computing. His exceptional contributions have been recognized with numerous prestigious awards: 2007 IEEE Koji Kobayashi Computer and Communications Award 2007 ACM SIGMETRICS Achievement Award 2008 ACM SIGCOMM Award 2011 INFOCOM Achievement Award 1999 IEEE Communications Society William Bennett Award 2008 ACM SIGCOMM Test of Time Paper Award 2012 ACM SIGMETRICS Test of Time Award 2018 ACM MOBICOM Test of Time Award UMass Award for Outstanding Accomplishments in Research and Creative Activity University of Massachusetts Chancellor's Medal UMass Amherst Distinguished Graduate Mentor Award Outstanding Research Award from the College of Natural Science and Mathematics IBM Faculty Fellowship Award (twice) Fellow of the IEEE Fellow of the ACM Corresponding member of the Brazilian Academy of Sciences Prof. Towsley has mentored numerous graduate students, as evidenced by his Distinguished Graduate Mentor Award, and his research has been funded by significant grants including an NSF NeTS grant for quantum network design. He leads the Gaia research group at UMass Amherst, which has evolved from traditional networking research to pioneering quantum networking initiatives.
Ki-Woong Park is a tenure-track full Professor in the Department of Computer and Information Security at Sejong University. He leads the System Security and Computer Engineering Research (SysCore) Lab, which focuses on system security research with numerous ongoing projects funded by major Korean research institutions including IITP, NRF, and KRIT. Sejong University, Department of Computer and Information Security System Security and Computer Engineering Research (SysCore) Lab Leader Member of IEEE, IEEE Computer Society, and ACM Education: Ph.D. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) M.S. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) B.S. in Computer Science, Yonsei University (Summa Cum Laude) Exchange Student at University of California, Los Angeles (UCLA) Professor Park's research focuses on designing, building, and analyzing secure systems, particularly for cloud computing, networked systems, and embedded systems. His work often involves reevaluating existing security mechanisms and actual system implementations with subsequent evaluation in real computing environments. He has made significant contributions to areas including cloud security, IoT security, ransomware detection, moving target defense, and metaverse security. His research approach emphasizes both theoretical foundations and practical implementation, with numerous publications in top-tier security and systems venues. His recent publications (2023-2024) demonstrate a strong focus on emerging security challenges in modern computing environments, particularly in metaverse platforms, UAV systems, and edge computing. These works span both theoretical security frameworks and practical implementations, with an emphasis on visualization techniques, hardware-based security mechanisms, and AI-enhanced security analysis. His research shows a clear progression from traditional cloud and network security toward next-generation security challenges in immersive virtual environments and cyber-physical systems. Scientific Awards: Microsoft Research Fellowship (2009-2010) Best Poster Gold Award at WISA 2020 Best Paper Award at MobiSec'18 Professor Park actively mentors numerous graduate and undergraduate students through the SysCore Lab, with current members including Ph.D. students, MS students, and undergraduate researchers. His research is supported by multiple significant grants, including the NRF Outstanding Researcher-Mid-career Researcher project, IITP Information Security Core Source Technology Development, and Defense Technology Advancement Research Institute projects. These grants total tens of billions of Korean won and address critical national security challenges in cyber defense, cloud security, and metaverse technologies. The SysCore Lab, under Professor Park's leadership, maintains a strong industry and government collaboration network, with part-time researchers from organizations including Hyundai Duty Free, Astron Security, Korea University, and various military cyber commands. This unique structure enables the lab to address both theoretical security challenges and practical implementation issues in real-world systems.
Oussama Damen is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering. His research focuses on advanced wireless communication systems, particularly in MIMO (Multiple-Input Multiple-Output) systems, signal processing, and machine learning applications in telecommunications. He is actively involved in developing innovative solutions for beamforming, hybrid precoding, and distributed decoding in massive MIMO and millimeter-wave networks. Damen's work also extends to optical fiber communication, federated learning in wireless systems, and optimization of resource allocation in next-generation networks like 5G/6G. His research interests include wireless communication theory, antenna system design, channel modeling, and algorithm development for improving spectral and energy efficiency. He has contributed extensively to the theoretical foundations of MIMO detection, lattice reduction techniques, and statistical signal processing methods. Notable trends in his publications emphasize bridging theoretical performance limits with practical implementations, particularly in scenarios involving channel impairments, limited backhaul capacity, and multi-core fiber transmission. His work often addresses fairness and optimization in distributed systems, including federated learning frameworks and hybrid beamforming architectures. No scientific awards or grants are explicitly mentioned in the provided information. Damen has advised no listed students, and no specific lab affiliations are noted.
Deepak Ganesan is a Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His research focuses on low-power sensing and communication, networked systems, and machine learning applied to pervasive health monitoring and societal challenges. PhD, Computer Science, University of California, Los Angeles (2004) MS, Computer Science, University of California, Los Angeles (2000) BTech, Computer Science, Indian Institute of Technology, Madras (1998) Ganesan's work bridges wireless sensor networks, smart textiles, and healthcare applications. He designs ultra-low-power wearable devices for tracking health signals like drug use, smoking, and cognitive performance, often integrating machine learning for robust detection. His research emphasizes societal impact, particularly in aging and Alzheimer's care through the Massachusetts AI and Technology Center for Connected Care (MassAITC) and the Center for Personalized Health Monitoring (CPHM). Recent publications highlight innovations in edge-cloud collaboration, fabric-based sensors, and longitudinal health analytics. His NIH-funded MD2K Center for Excellence and affiliations with the Center for Data Science and Computational Social Science Institute further underscore his interdisciplinary approach. ACM Fellow NSF CAREER Award (2006) IBM Faculty Award (2008) UMass Junior Faculty Fellow (2008) UMass Lilly Teaching Fellow (2009) Best Paper at CHI 2013 Best Paper Runner-up at Mobicom 2014 Honorable Mentions at Ubicomp 2013 Ganesan leads the SENSORS: Wireless Sensor Networks Group and contributes to global initiatives like the Internet of Battlefield Things. His work spans academic research, industry partnerships, and policy development in AgeTech and digital health.
Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.
Halim Yanikomeroglu is a Full Professor and Chancellor's Professor at Carleton University's Department of Systems and Computer Engineering, part of the Faculty of Engineering and Design. His research focuses on wireless communications, including 5G/6G networks, non-terrestrial systems (HAPS/LEO satellites), MIMO, and cognitive radio. He has supervised numerous graduate students and holds IEEE Fellow status and the Harold Sobol Award. His work integrates machine learning, federated learning, and sustainability into next-generation networks. Affiliations: Carleton University, IEEE Education: Ph.D. (Toronto), M.A.Sc. (Toronto), B.Sc. (Middle East Technical University) Research interests span cellular networks, relay architectures, and energy-efficient systems. He pioneered cell-switching strategies for green networks and contributed to HAPS and UAV-based infrastructure. His recent work addresses NTN integration, AI-driven spectrum management, and 6G innovations. Awards include IEEE Fellow (2017) and multiple Research.com leadership accolades. His 150+ publications span journals like IEEE Transactions and conferences like ICC. Advising over 50 students, he emphasizes interdisciplinary solutions for future wireless challenges.
Dr. Vishal Sharma is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on Cyber-Physical Systems (CPS), 5G/6G Security, Unmanned Aerial Vehicles (UAVs), Blockchain, and Digital Twins. He has held roles at institutions like Singapore University of Technology and Design (SUTD) and Soonchunhyang University, South Korea. Notable achievements include Best Paper Awards at ICCMIT 2017, IEEE SITE 2024, and HUCAPP/VISIGRAPP 2025. He leads the Innovation-by-Design Lab and is a Fellow of the Higher Education Academy (FHEA). Research Interests: Cyber Defence, UAV Security, Secure Computing, Network Security, and Sustainable Edge Computing. He has collaborated on projects like RapidRANDefender (QRICSec) and Traceable Procurement for Net-Zero Processes. Awards include the Royal Society International Exchanges Committee appointment (2025) and QUB's Individual Performance Award (2024). Grants and Projects: Principal Investigator for projects such as Exploring Operational Capabilities of Arm Morello for UAV Security (2023) and TUDOR: Ubiquitous 3D Open Resilient Network (2023). Active in editorial roles for IEEE Communications Magazine and IET Networks. His work aligns with UN Sustainable Development Goals (SDGs) related to climate action and innovation. Publications span 150+ articles in top journals/conferences, with a focus on secure communication, edge computing, and UAV networks. Supervises PhD students in cyber defence, AI security, and distributed ledger technologies.
John C. Doyle is the Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and BioEngineering at the California Institute of Technology (Caltech), where he holds appointments in the Division of Engineering and Applied Science with primary affiliation in the Control and Dynamical Systems Department. His research bridges theoretical foundations with applications across biological, technological, medical, and ecological networks. He earned a BS and MS in Electrical Engineering from MIT (1977) and a PhD in Mathematics from UC Berkeley (1984), followed by consultancy at Honeywell Systems and Research Center (1976-1990). MIT: BS & MS in Electrical Engineering (1977) UC Berkeley: PhD in Mathematics (1984) Doyle's research centers on universal laws and architectures in complex systems, emphasizing robustness-efficiency tradeoffs, speed-accuracy tradeoffs (SATs), diversity-enabled sweet spots (DeSS), bowtie/hourglass structures, and evolvability. His work pioneers System Level Synthesis (SLS) for control systems with sparse, local, saturating, delayed, noisy, quantized, and distributed (SLSDNQD) components, integrating control theory, computation, communication, and machine learning to address challenges from neural networks to infrastructure resilience. Key concepts include virtualization, horizontal transfer, and virality in multiscale systems. Analysis of his publication trends reveals consistent interdisciplinary impact across neuroscience (brain connectivity modeling), systems biology (metabolic oscillations), network science (internet topology), and physics (turbulence, earthquakes), with recurring themes of robust-efficiency limits and architectural principles governing complex networks. His work demonstrates exceptional translation from abstract theory to practical tools like the Matlab Robust Control Toolbox and Systems Biology Markup Language (SBML). His scientific recognition includes: 1990 IEEE Baker Prize (ranked among top 10 most important mathematics papers 1981-1993) Three IEEE Automatic Control Transactions Awards (1998, 1999, 2021) ACM Sigcomm Paper Prize (2004) and Test of Time Award (2016) IEEE Control Systems Field Award (2004) Multiple early-career honors including IEEE Centennial Outstanding Young Engineer (1984) Doyle has mentored generations of students whose contributions include foundational software tools adopted globally. His research has secured sustained funding from NSF, NIH, and other agencies supporting theoretical advances in control frameworks and their applications to biomedical systems, network infrastructure, and environmental modeling. The SBML initiative exemplifies his group's impact in standardizing computational biology research. He leads a highly collaborative research ecosystem at Caltech that integrates engineers, biologists, neuroscientists, and computer scientists to develop universal principles for complex networks. Current efforts focus on translating theoretical insights into health technologies, resilient infrastructure, and climate-responsive systems through the application of robust-efficiency frameworks to emerging challenges in cyber-physical and biological domains.
Stephanie Gil is an Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her research focuses on artificial intelligence, robotics, and distributed systems, particularly addressing challenges in multi-agent coordination, resilience to adversarial attacks, and wireless communication for autonomous systems. She leads the REACT Lab, advancing research in resilient multi-robot networks and cyber-physical systems. Her work integrates machine learning, control theory, and wireless sensing to solve problems such as whale tracking via autonomous robots, proactive multi-robot routing, and decentralized exploration without explicit information exchange. She has received prestigious awards, including the DARPA Young Faculty Award (2024) and the Amazon Research Award (2021). Key research areas include resilient distributed optimization, trust-centered coordination in multi-agent systems, and leveraging wireless signals (e.g., WiFi-CSI) for sensing and bearing estimation. Her contributions span both theoretical frameworks and practical implementations, with a focus on real-world applications like autonomous rideshare routing and environmental monitoring. Dr. Gil’s research also explores trust and cybersecurity in dynamic networks, with publications on crowd vetting, malicious robot detection, and adaptive communication strategies. She collaborates on interdisciplinary projects, such as Project CETI, combining AI and robotics for ecological studies.
