Marco Di Renzo is a CNRS Research Director (Professor) and Head of the iPhyCom group at the Laboratory of Signals and Systems (L2S) at Paris-Saclay University, France. He is affiliated with both CNRS and CentraleSupélec. His roles include: Member of L2S Management Committee and Board Council Member of the Ph.D. School Admission Committee Academic Vice Chair, ETSI Industry Specification Group on RIS Editorial and leadership roles in IEEE communications journals Education: Laurea (cum laude) and Ph.D. in Electrical Engineering from University of L’Aquila (2003, 2007) Habilitation à Diriger des Recherches from Paris-Saclay University (2013) Research focuses on 6G networks , reconfigurable intelligent surfaces (RIS) , and integrated sensing and communications (ISAC) . He explores electromagnetic theory, machine learning applications, and energy-efficient wireless systems. His work addresses challenges in near-field communications, multi-user MIMO, and holographic beamforming. He advocates for physics-driven design principles in next-gen networks. Key awards include IEEE/IEE Fellowships, the Michel Monpetit Prize, and multiple IEEE Best Paper Awards. He holds international visiting professorships at institutions like the University of Oulu (Finland) and Nanyang Technological University (Singapore). Active in standardization via ETSI ISG RIS and contributes to global initiatives like the ITU 6G Vision. His research bridges theoretical models with practical implementations, emphasizing interdisciplinary collaboration between physics, signal processing, and AI.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Dr. Li Wan is an Associate Professor in Urban Planning and Development at the Department of Land Economy, University of Cambridge, and a Fellow of Gonville and Caius College. He serves as Director for the MPhil in Planning, Growth and Regeneration and is a co-investigator of the Centre for Smart Infrastructure and Construction. Dr. Wan is also a Trustee of the IJURR Foundation and leads a research group focused on understanding urban land and transport development through applied modeling and data analytics. Dr. Wan holds a BArch, MPhil, and PhD from the University of Cambridge. Prior to joining Land Economy, he worked in Architecture and Engineering Departments, giving him a highly interdisciplinary perspective. His research focuses on spatial economic modeling of urban land use and transport systems, with recent interests including strategic planning at city/regional levels, impact studies of flexible working, micromobility, and electric vehicles. Dr. Wan's research group has produced significant work in city digital twins, as evidenced by his 2023 book Digital Twins for Smart Cities: Conceptualisation, challenges and practices . His publications span urban analytics, transport emissions, spatial equilibrium modeling, and economic geography, demonstrating the interdisciplinary nature of his work. Recent research trends show increasing focus on post-pandemic urban dynamics, digital governance, and sustainable mobility solutions. His research group has secured funding from initiatives like AI@Cam for projects examining how local authorities use AI for urban decision-making. Dr. Wan actively supervises PhD and MPhil students, with his group comprising researchers working on diverse urban topics from land-use efficiency to micromobility impacts. As an educator, Dr. Wan teaches courses including Tripos Paper 10 - The Built Environment, PGR01 - Urban and Environmental Planning, PGR02 - Urban and Housing Policy, PGR05 - Place-based Policy, and RM03 – Spatial Analysis and Modelling. His group has presented at major international conferences including CUPUM and ICML, reflecting his active engagement in the academic community.
Raj Rajkumar is the George Westinghouse Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He holds leadership roles as Director of the USDOT Mobility21 and Safety21 National University Transportation Centers, and Director of the Metro21: Smart Cities Institute. His work focuses on cyber-physical systems, autonomous driving, and vehicular networking. Education: Ph.D. in Electrical and Computer Engineering from CMU (1989). Research interests include connected/autonomous vehicles, cyber-physical systems, embedded systems, and vehicular networking. Key projects include the General Motors-CMU Collaborative Research Lab and the development of autonomous vehicle protocols like Ballroom Intersection Protocol. Notable awards include the 2016 National Academy of Inventors Fellowship and the 2017 Pittsburgh Business Times Innovation Award. His publications span real-time systems, sensor networks, and automotive cyber-physical frameworks. Rajkumar advises on autonomous vehicle safety, energy efficiency, and societal impacts, with frequent media engagements on self-driving technologies.
Associate Professor Jiwon Kim is a leading researcher in Transport Engineering at the University of Queensland's School of Civil Engineering. She serves as Director of Higher Degree by Research and was a DECRA Fellow from 2019-2022. Holding degrees from Korea University and Northwestern University, she specializes in AI/ML applications for transportation systems. PhD, Northwestern University BS & MS, Korea University Her research focuses on Artificial Intelligence and Machine Learning applications in transportation, including: Deep learning for traffic management Reinforcement learning in mixed traffic environments Multi-agent systems for urban mobility optimization Spatiotemporal trajectory analysis Recent publications demonstrate expertise in: Eco-driving strategies Traffic incident prediction Queue length estimation Crash risk modeling Scientific recognition includes: ARC DECRA Fellowship (2019-2022) She supervises doctoral students in: Transportation data analytics Autonomous vehicle systems Intelligent traffic management Current projects explore real-time traffic monitoring, synthetic mobility data generation, and connected vehicle technologies.
Rogério de Lemos is a Senior Lecturer in Computing Science and Director of Postgraduate Research (PGR) at the School of Computing, University of Kent. He previously served as an invited assistant professor at the University of Coimbra, Portugal, and as a Senior Research Associate at the Centre for Software Reliability (CSR) at the University of Newcastle upon Tyne, UK. Dr. de Lemos' research focuses on architecting resilient systems, particularly in resilient AI, self-adaptive software systems, and authorization infrastructures. He belongs to both the Programming Languages and Systems Group and the Cyber Security Group at the University of Kent. His specific research interests include: Software engineering for self-adaptive systems assurances and resilience evaluation Dynamic generation of processes Handling insider threats using self-adaptive authorization Architectural abstractions for fault tolerance Verification and validation of dependable software architectures Software development for safety-critical systems Dependability and bioinspired computing His publication trends show increasing emphasis on practical applications of self-adaptive systems in cyber security contexts, with recent work spanning network traffic analysis, cryptographic function detection, and cloud-edge security architectures. His research bridges theoretical foundations with practical implementations, particularly in cyber security and resilient systems architecture. Dr. de Lemos currently leads the "Collaborative and Confidential Information Sharing and Analysis for Cyber Protection" project funded by the European Union's Horizon 2020 Programme. His past projects include "ADAAS: Assuring Dependability in Architecture-based Adaptive Systems" and multiple collaborations with NCR on sensor fusion and fault tolerance. As Director of Postgraduate Research, he oversees the School of Computing's research degree programs and likely supervises PhD students in resilient systems and cyber security, though specific student names are not listed in the available information.
Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools
Bilal Farooq is an Associate Professor and Program Director for the Master of Engineering in Interdisciplinary Engineering (MEIE) at Toronto Metropolitan University, holding the Canada Research Chair in Disruptive Transportation Technologies and Services within the Department of Civil Engineering. His educational background includes a PhD from the University of Toronto (2011), MASc from Lahore University of Management Sciences (2004), and BSc from the University of Engineering and Technology (2001). Dr. Farooq's research pioneers disruptive transportation solutions through cyber-physical systems, AI/machine learning applications, behavioral modeling, and optimization techniques. His work specifically targets on-demand multimodal systems, sustainable urban transportation, urban air mobility, automated vehicles, and extended reality applications, addressing critical urban mobility challenges with human-centered approaches. Analysis of his recent publications reveals a strong trend toward quantum-enhanced computational methods, privacy-preserving federated learning frameworks, and sustainability-focused decarbonization strategies across transportation domains, with increasing emphasis on human factors and real-world implementation. Notable scientific awards include: Ontario Early Researcher Award (2018) Canada Research Chair (2017) MassMotion Academic Pedestrian Modelling Project of the Year (2016) Québec Early Researcher Award (2014) Dr. Farooq actively supervises graduate students and secures significant research funding through his Canada Research Chair position and Early Researcher Awards. He directs the Laboratory of Innovations in Transportation (LiTrans), which develops interdisciplinary solutions integrating mathematics, engineering, computer science, and economics to address emerging transportation challenges. LiTrans focuses on disruptive transportation technologies, complete streets design, cyber-physical systems, pedestrian dynamics, resilience, and climate change impacts, collaborating with industry and government partners to translate research into practical urban mobility innovations for smart cities worldwide.
Joseph A. Campbell is an Assistant Professor in the Department of Computer Science at Purdue University, leading the Collaborative AI for Machines and People (CAMP) Lab. He holds a Ph.D., M.S., and B.S. in Computer Science and Computer Engineering from Arizona State University. Before academia, he worked as a software engineer for five years. Prior to Purdue, he was a Postdoctoral Fellow at Carnegie Mellon University's Robotics Institute. His research focuses on explainable machine learning and robotics, particularly how agents use explanations for self-improvement and decision-making. Key areas include theory of mind in multi-agent systems, lifelong learning, and interpretable transfer learning. His work bridges robotics and AI, with applications in human-robot interaction and prosthetic control. Notable publications include advancements in reinforcement learning with language models, multi-agent collaboration frameworks, and methods for enhancing state estimation in robots. His research has been presented at top conferences like NeurIPS, EMNLP, and CoRL. Dr. Campbell maintains an active GitHub profile (joe-campbell) with repositories such as Interaction Primitives for robotics applications. His lab, CAMP, explores AI systems that collaborate effectively with humans and other machines.
Subir Biswas is a Professor in the Department of Electrical and Computer Engineering (ECE) at Michigan State University (MSU), part of the College of Engineering. His research focuses on wireless networking, low-power protocols, vehicular networks, and health monitoring systems, conducted through the NeEWS laboratory. He holds a Ph.D. from the University of Cambridge and degrees from Jadavpur University. Education: Ph.D., Computer Engineering, University of Cambridge (1994) M.S., Electrical Engineering, Jadavpur University (1989) B.S., Electrical Engineering, Jadavpur University (1987) Professional Experience: Professor, MSU ECE (2011–present) Associate Professor, MSU ECE (2003–2010) Research interests include traffic analysis firewalls, pricing calculus for social wireless networks, low-energy green networking, and ultrasound-based sensor systems . He leads the NeEWS lab, exploring embedded systems and network middleware. His work spans IoT, cybersecurity, and biomedical applications like wearable sensors for autism monitoring and hydration tracking. Publications emphasize innovative protocols (e.g., pulse position coding) and machine learning for network optimization. Collaborations include NSF-funded projects and industry partnerships (e.g., Tellium Optical Systems).
Nozomu Togawa is a Professor at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering, specializing in Computer Science. He has held this position since 2009 and also serves as Chief Scientific Officer (CSO) of Quanmatic Inc. since 2022. With a PhD in Engineering from Waseda University (1997), his academic journey includes positions at Waseda University and the University of Kitakyushu before his current professorship. His research interests focus on integrated system design , quantum computation , and information security . Togawa has published extensively with over 368 papers and significant citation metrics (Scopus h-index: 23, Google Scholar h-index: 28). His work bridges theoretical quantum computing with practical security applications, particularly in hardware security and IoT systems. Togawa's research demonstrates a clear progression from traditional hardware security toward quantum-inspired computing solutions. His recent publications focus on Ising machines, quantum annealing, and hardware Trojan detection, showing how quantum approaches can solve complex optimization problems in security contexts. He has made significant contributions to applying quantum computing techniques to practical problems like course selection optimization, travel planning, and hardware security verification. Among his notable recognitions are the Minister of Education, Culture, Sports, Science and Technology Award for Science and Technology (2018), SCOPE Results Development Promotion Award (2022), and multiple Best Paper Awards. He serves on important committees including the Ministry of Internal Affairs and Communications Cyber Security Task Force and the Institute of Electronics, Information and Communication Engineers' VLSI Design Technology Research Committee. Togawa actively mentors students who frequently appear as co-authors on his publications. His research group produces high-impact work in quantum computing applications and hardware security, with strong industry connections through his CSO role at Quanmatic Inc. He has received substantial research funding supporting his innovative work at the intersection of quantum computing and security.
Dr. Srinivas Peeta is the Frederick R. Dickerson Chair and Professor in Transportation Systems Engineering at the Georgia Institute of Technology’s School of Civil and Environmental Engineering. He previously held the Jack and Kay Hockema Professorship at Purdue University, where he served for 24 years. He is also the Associate Director of the USDOT Center for Connected and Automated Transportation. Education: B. Tech. from IIT Madras, M.S. from Caltech, and Ph.D. from UT Austin, all in Civil Engineering. His research focuses on large-scale transportation systems, infrastructure interdependencies, and connected/automated vehicles. He has authored over 345 publications and secured over $48M in research funding. Research Interests: Dynamic traffic networks and driver behavior modeling Information-based navigation in vehicular systems Systems perspectives for complex adaptive infrastructure Autonomous vehicle integration and human-vehicle interactions Key Achievements: Developed DYNASMART software for traffic operations Recipient of NSF CAREER Award (1997) and ASCE Walter Huber Prize (2009) Directed NEXTRANS UTC and pioneered USDOT’s real-time route guidance systems Grants & Outreach: Secured funding from USDOT, NSF, FHWA, and international agencies Initiated NEXTRANS internship programs and K-12 outreach Labs/Teams: Active in Georgia Tech’s ACT Lab, focusing on autonomous transportation systems and human-vehicle-environment interactions.
Professor Shaomin Wu is a faculty member at the University of Kent's Kent Business School, where he holds the academic rank of Professor of Business/Applied Statistics. He earned an MSc and PhD in applied statistics and has extensive industry experience, including a five-and-a-half-year stint at a global manufacturer in Shanghai before moving to the UK in 2001. He has held roles as a postdoctoral researcher and lecturer before joining Cranfield University and later the University of Kent. His research focuses on recurrent event data analysis, machine learning, and reliability mathematics, with funding from the EPSRC and ESRC. His research projects include managing risk in warranty servicing policies, smart data analytics for local government, and sustainable supply chain demand forecasting. He teaches modules such as risk analysis, reliability engineering, and machine learning. Currently supervising PhD students in time series forecasting, explainable AI, and recurrent event data analysis, he also serves as a co-chair of international conferences, editorial board member, and external examiner for doctoral degrees. Notably, he ranks among the top 2% of global scientists by Stanford University. His work integrates machine learning with business analytics, resilience engineering, and environmental sustainability. Key contributions include IoT-driven resilience methodologies for smart grids and unmanned systems, as well as frameworks for corporate carbon disclosure and maintenance optimization under uncertainty.
Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.
Prof. Ivan Martinovic is a Professor of Computer Science at the University of Oxford's Department of Computer Science, holding a Fellowship at the institution. He specializes in cyber-physical systems security, wireless network security, and behavioral biometrics. His research focuses on authentication mechanisms, intrusion detection, and trade-offs between security and performance in systems like satellite communications, EV charging infrastructure, and aviation networks. Education: PhD (TU Kaiserslautern); MSc (TU Darmstadt, Germany). Prior roles include postdoctoral research at UC Berkeley (2011) and UC Irvine (2009–2010), supported by a Carl-Zeiss Foundation Fellowship (2009–2011). He has also been an associate lecturer at TU Kaiserslautern. Research Interests: Cyber-physical systems security, wireless networks, behavioral biometrics (e.g., continuous authentication via physiological signals), robust communication protocols, and analysis of security vs. performance trade-offs in distributed systems. Technologies include software-defined radios, NFC/RFIDs, and EEG/eye-tracking devices. Scientific Awards: Carl-Zeiss Foundation Fellowship (2009–2011). Advising & Grants: No explicit student listings; however, his work involves interdisciplinary collaborations and has been funded by government and industrial grants. His research spans labs and teams focused on satellite cybersecurity, smart infrastructure security, and biometric systems. Labs/Teams: Engages with groups exploring satellite communication security, EV charging infrastructure vulnerabilities, and behavioral authentication systems, though specific lab names are not disclosed in the text.
