Zahra Gharineiat is an Associate Professor at the University of Southern Queensland , affiliated with the School of Surveying and Built Environment . She has over 8 years of tertiary teaching experience and 11 years of administrative responsibilities. Bachelor of Surveying (BSurv), University of Tabriz Master of Engineering Management (MEngMgt), University of Melbourne PhD, University of Newcastle Research Interests : Zahra specializes in Geomatic Engineering and Machine Learning , with a focus on applications like Unmanned Aerial Vehicles (UAVs) , LiDAR , Digital Twins , and Remote Sensing . Her work spans Earth Science Observations , Satellite altimetry , and Geodetic data capturing , integrating Computational Modelling and Geoinformatics for innovative solutions. Professional Affiliations : She is a member of the Surveying and Spatial Sciences Institute (SSSI) and the International Union of Geodesy and Geophysics (IUGG) . Her research affiliations include the Centre for Future Materials (CFM) , Institute for Advanced Engineering and Space Sciences (IAESS) , and Centre for Astrophysics (CA) .
Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.
Huixuan Wu is an Associate Professor in the Department of Mechanical Engineering at the Florida A&M University-Florida State University College of Engineering. Their research focuses on fluid mechanics, experimental technology, multiphase flow, turbulence, and statistical mechanics. They hold a Ph.D. and M.S. in Mechanical Engineering from Johns Hopkins University (2011 and 2008, respectively). Notable achievements include the NSF CAREER Award and the Alexandra von Humboldt Scholarship. Research contributions span magnetic particle tracking technologies, turbulence analysis, and flow visualization techniques. Dr. Wu has also explored interdisciplinary applications such as wind sensing using UAVs and the statistical mechanics of income inequality. Publications highlight advancements in neural network-based algorithms for particle tracking, persistent homology methods in turbulent flows, and acoustic liner performance simulations. Their work bridges computational modeling, experimental fluid mechanics, and machine learning, with applications in aerospace and biomedical engineering. Labs and collaborations involve experimental setups for granular flow analysis and aerodynamic noise mitigation. Advising focuses on graduate students exploring fluid dynamics and related interdisciplinary topics.
Preben E. Mogensen is a Professor at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His research focuses on wireless communication networks, 5G/6G technologies, and their applications in smart production and robotic systems. He leads and collaborates on projects involving cellular networks, unmanned aerial vehicles (UAVs), and interference coordination. Research interests include cellular network optimization, machine learning-driven beam selection, and vehicular communication systems. His work addresses challenges in path loss analysis, antenna design, and edge-cloud integration for real-time robotic control. Recent projects include 5G-enabled autonomous robotics and cognitive radio concepts for beyond-femtocells. Mogensen has received the 2019 5G-prisen award recognizing contributions to Danish telecommunications advancements. He has published over 520 research outputs, with a focus on high-impact journals and conferences. Active collaborations span academia and industry, including work on 5G Smart Production and community-driven IoT solutions. He hosts guest researchers and advises on projects involving 5G implementation in factories and emergency response systems. His lab environments include facilities for testing advanced radio resource management and network synchronization in industrial settings.
Dr. Zhiyuan Tan is an Associate Professor in the School of Computing at Edinburgh Napier University (ENU), specializing in cybersecurity research. He holds a PhD in Computer Systems from the University of Technology Sydney (UTS), Australia (2014), an MEng from Beijing University of Technology, China (2008), and a BEng with high distinction from North-eastern University, China (2005). Before joining ENU in 2016, Dr. Tan held research positions at the University of Twente (Netherlands), University of Technology Sydney (Australia), and La Trobe University (Australia). Dr. Tan's research focuses on cybersecurity, machine learning, data analytics, virtualisation, and cyber-physical systems. His work has resulted in over 44 scholarly publications with an H-Index of 13 and more than 830 citations according to Google Scholar. His recent publications demonstrate a continued focus on network security, intrusion detection systems, and the application of machine learning techniques to cybersecurity challenges, with publications spanning from 2022-2025 in top venues including IEEE Transactions and international conferences. Dr. Tan has received significant research funding, including AUD 27,800 from CSIRO and UTS for autonomous network intrusion detection research and £6,987 from ENU for securing future 5G health care systems. His research has been recognized with awards including the National Research Award 2017 from the Research Council of the Sultanate of Oman, a Best Paper Award, and the Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award. National Research Award 2017 from the Research Council of the Sultanate of Oman Best Paper Award Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award Dr. Tan has mentored 9 PhD students over the past 5 years, with 6 successfully completing their studies. His students have produced 12 journal and 10 conference publications. He has also served as an editorial board member for international journals, organized special issues, and participated as a technical program committee member for major international conferences. Dr. Tan is currently recruiting PhD students for research projects on network security, adversarial machine learning for anomaly/malware detection, virtualization security, and IoT security.
Eric Kerrigan is a Professor of Control and Optimization at Imperial College London's Department of Electrical and Electronic Engineering, part of the Faculty of Engineering. He holds a joint appointment in the Department of Aeronautics. His research focuses on Model Predictive Control (MPC), numerical optimization techniques, and their applications in aerospace, renewable energy, and information systems. Key projects include developing real-time optimization algorithms for embedded systems, co-design frameworks for closed-loop systems, and drag reduction in aerodynamics. He has supervised over 30 PhD students and post-doctoral researchers, many of whom have secured academic positions. Education: PhD in Control Engineering from the University of Cambridge and BSc in Electrical Engineering from the University of Cape Town. Research interests emphasize robust control methods, dynamic optimization, and interdisciplinary applications. Notable contributions include the ICLOCS (Imperial College Optimal Control Software) toolbox and frameworks for energy-efficient UAV communication networks. His work is funded by EPSRC, the European Commission, and industry partners like Siemens and ESA. Funding and collaborations include grants from the Royal Academy of Engineering and Royal Society, with consulting roles in industrial control systems. Editorial roles include Associate Editor for IEEE Transactions on Automatic Control and former Senior Editor for IEEE Transactions on Control Systems Technology . Labs and affiliations include the Control and Power Research Group, Energy Futures Lab, and Space Lab at Imperial College.
Francisco Javier Falcone Lanas is a Professor in the Department of Electrical, Electronic and Communication Engineering at the Public University of Navarre. He is affiliated with the Institute of Smart Cities and leads research in the Communication, Signals and Microwaves research group. He also participates in the Doctoral Program in Communications Technologies, Bioengineering, and Renewable Energy. His research focuses on applied and computational electromagnetics, with specializations in: Analysis and design of complex electromagnetic media and metamaterials Design of communication devices (filters, diplexers, couplers, antennas) Implementation of devices on flexible/paper substrates Wireless power transfer systems Computational electromagnetic code development (FDTD, 3D Ray Launching, Radar RCS) Implementation of devices for PLMN, WSN, LPWAN and Radar systems Radioelectric analysis at physical layer and system level His recent publications demonstrate a strong focus on millimeter-wave technology, MIMO antenna systems, 5G/6G communications, and wireless sensor networks. His work often addresses optimization challenges related to energy consumption, interference handling, and capacity/coverage in communication systems. He has made significant contributions to the fields of metamaterials and their application in antenna design and performance enhancement. With an H-index of 58 in Scopus, Professor Falcone Lanas has established himself as a leading researcher in his field. His research has practical applications in various domains including: 5G/6G mobile communication systems Wireless body area networks Vehicle-to-everything (V2X) communications Wireless sensor networks for industrial applications Digital twin modeling for UAV communications Earthquake disaster management systems
Dr. Emmanuel Prempain is an Associate Professor at the School of Engineering, University of Leicester. His research focuses on control systems, convex optimization, and their applications in aerospace systems such as helicopters, re-entry vehicles, and UAVs. He specializes in robust control methodologies like gain scheduling, fixed-order synthesis, and Linear Matrix Inequality (LMI) frameworks. Recent work emphasizes energy-efficient control strategies for robotic arms and quadrotor UAVs, leveraging iterative learning control (ILC) and hybrid optimization algorithms. His contributions include model predictive control (MPC) designs for nonlinear systems and fault-tolerant switched control for multivariable systems. Key Technologies: Robust control, MPC, ILC, UAV control, aerospace systems Applications: Autopilot design, robotic trajectory tracking, energy optimization Dr. Prempain has developed a 2DoF Twin Rotor MIMO system for educational and research purposes, demonstrating practical applications of advanced control theories. His publications span over two decades, reflecting a sustained commitment to advancing control system methodologies.
Dr. Sudharman K. Jayaweera is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico, Albuquerque, NM. He holds a PhD in Electrical Engineering from Princeton University (2003), an MS in Electrical Engineering from Princeton University (2001), and a BE in Electrical and Electronic Engineering with First Class Honors from the University of Melbourne, Australia (1997). He is a Senior Member of IEEE and serves as an editor for IEEE Transactions in Vehicular Technology. Dr. Jayaweera's research spans several key areas in modern communications and signal processing: Cognitive radios and autonomous learning systems Wireless communications and statistical signal processing Machine learning applications in communications Smart-grid technologies and cyber-physical systems Satellite communications and space networks Vehicular networks and distributed systems His recent work shows a clear trend toward integrating artificial intelligence with traditional communications systems, particularly focusing on UAV networks, spectrum management, and security aspects of cognitive radio systems. The publications reflect a strong emphasis on practical implementations of theoretical concepts, with applications ranging from smart grids to space communications. Dr. Jayaweera has received numerous scientific awards including the IEEE PACRIM 2011 Gold Award for Best Communications Paper, the IEEE AVSS '06 Best Paper Award, and the WPMC '03 Excellent Paper Award. He has also been recognized with fellowships including the National Research Council (NRC) Senior Fellow at the Naval Postgraduate School and ASEE Air Force Summer Faculty Fellow. As an advisor, Dr. Jayaweera has mentored numerous graduate students to completion of their PhD and MS degrees. His former students have gone on to successful careers at institutions including Syracuse University, SUNY Oswego, Qualcomm, Sandia National Labs, and other leading technology companies. He also directs the EYES Summer Internship program for international students at UNM. Dr. Jayaweera leads the Communications and Information Sciences Lab (CISL) and the Cognitive Radio Lab (CRL) at UNM, where his teams work on cutting-edge research in autonomous cognitive radios (which he terms "Radiobots"), machine learning for communications, and next-generation wireless systems.
Roman Fedorovych Sukhonos is a Senior Lecturer at the Department of Automobiles, Heat Engines and Hybrid Power Plants within the Faculty of Transport at National University "Zaporizhzhia Polytechnic". He has been affiliated with the university since 2011, contributing to both teaching and research activities in the field of internal combustion engines and automotive systems. His educational background includes: Bachelor's degree in Engineering Mechanics from Zaporizhzhia National Technical University (2010) Master's degree in Internal Combustion Engines from Zaporizhzhia National Technical University (2011), with qualifications as "Master in Internal Combustion Engines, Researcher (Engineering Mechanics), Lecturer at Universities and Higher Educational Institutions" Roman Sukhonos specializes in thermal and gas-dynamic processes in internal combustion engines. His research focuses on improving engine performance, efficiency, and emissions characteristics through innovative design approaches and diagnostic methods. He has particular expertise in two-stroke engine technology, resonant supercharging systems, and the analysis of engine misfires and their effects on performance. His recent publications demonstrate a strong focus on practical applications of engine technology, ranging from chainsaw engines to high-performance automotive systems like Ferrari V12 engines. There's a clear trend toward integrating modern diagnostic techniques, including acoustic monitoring and neural networks, with traditional engine design principles. His work spans both theoretical analysis and experimental validation of engine performance characteristics. Dr. Sukhonos has received numerous awards and recognitions for his contributions: Letter of appreciation from Zaporizhzhia Regional State Administration (June 2023) Honorary certificate from Zaporizhzhia Regional Council (June 2022) Letter of appreciation from Rector of National University "Zaporizhzhia Polytechnic" (May 2022) Honorary certificate from Executive Committee of Zaporizhzhia City Council (November 2021) Certificate from Rector of National University "Zaporizhzhia Polytechnic" (December 2019) Certificate from Oleksandrivka District Administration (December 2018) Third degree diploma from Ministry of Education and Science, Youth and Sports of Ukraine (July 2012) Certificate from Executive Committee of Zaporizhzhia City Council (December 2005) While specific details about his advising activities are not explicitly mentioned, his extensive publication record with multiple co-authors suggests active involvement in research mentoring. His participation in numerous conferences and publication of textbooks indicates significant contributions to curriculum development and student education. His research appears to be supported through university affiliations and collaborations with colleagues. Dr. Sukhonos is part of a research team at the Department of Automobiles, Heat Engines and Hybrid Power Plants that focuses on engine diagnostics, performance optimization, and alternative engine designs. His collaborations with colleagues like Georgy Slynko indicate a strong research group working on advancing internal combustion engine technology despite the industry's shift toward electric propulsion.
Lusophone University of Humanities and TechnologiesPortugal
João Pedro Matos-Carvalho is an Assistant Professor at Lusófona University in Lisbon, affiliated with the School of Engineering and the Department of Electrical and Computer Engineering. He is also an Integrated Member of the Center of Technology and Systems (CTS) at UNINOVA and COPELABS, Lusófona University, contributing to interdisciplinary research in robotics and intelligent systems. Ph.D. in Electrical and Computer Engineering, FCT NOVA (2021) M.Sc. (Hons.) in Electrical and Computer Engineering, FCT NOVA (2017) His research focuses on aerial robotics, machine learning, remote sensing, and sensor networks, with applications in UAV navigation, precision agriculture, environmental monitoring, and embedded AI. He has made significant contributions to GPS-denied navigation, multispectral imaging, and AI-driven signal processing. The recent publications reflect a strong trend in integrating deep learning with real-world engineering systems, particularly in UAV autonomy, IoT, and human-centric applications like fall detection and online learning analysis. His work spans algorithm design, software development, and practical deployment in complex environments. Best Paper Award at IEEE Conference (2018) Distinguished Paper Award by LASIGE at FCUL (2021) Best Poster Presentation Award at International Complex Systems and Their Applications Conference (2023) He has secured the competitive Scientific Employment Stimulus (CEEC) grant from FCT and has advised or collaborated on multiple research projects. He has guest-edited special issues in journals such as Drones and Frotiers in Computer Science , and serves as a reviewer for leading scientific journals. He leads the development of open-source tools like AutoNAV and Raster Forge, supporting simulation and geospatial analysis. He is actively involved in research teams at CTS-UNINOVA and COPELABS, focusing on intelligent systems, aerial robotics, and data fusion. His lab work emphasizes practical UAV platforms, sensor integration, and AI deployment in real-time systems.
Scot T. Martin is the Gordon McKay Professor of Environmental Science and Engineering and Professor of Earth and Planetary Sciences at Harvard University, with affiliations in the School of Engineering and Applied Sciences and the Department of Earth and Planetary Sciences. His research focuses on engineering solutions to global environmental challenges, particularly air/water pollution and their connections to regional/global change, with significant work in the Amazon basin. Key research interests include aerosol dynamics, climate impacts of pollution, and the interplay between human activities and natural ecosystems. His work integrates field observations, laboratory experiments, and computational modeling to address complex environmental systems. Recent studies emphasize Amazonian deforestation patterns, aerosol-cloud interactions in urban and pristine environments, and the development of novel sensing technologies for atmospheric chemistry. Notable methodologies include unmanned aerial vehicle (UAV) measurements, advanced spectroscopic techniques, and machine learning-driven models. Scientific contributions span over 15 years, with a focus on air quality, climate feedback mechanisms, and environmental policy implications. His interdisciplinary approach bridges engineering, earth sciences, and public health.
Joaquín Pérez Soler is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. His academic work integrates advanced telecommunications research with innovative engineering education practices. He is actively involved in research groups focused on Communications and Digital Systems Design (DSDC) and Human-Robot Interaction (HRI). His research spans optical wireless communication, visible light communication (VLC), free-space optics (FSO), radio-over-fiber (RoF), V2X communications, and electromagnetic interference. Additionally, he has a strong interest in educational technologies, particularly the use of robotics and software-defined radio in teaching. His work also extends to societal challenges, including the gender digital divide and STEAM education initiatives. His recent publications reflect a dual focus: advancing hybrid optical-wireless communication systems and innovating in engineering pedagogy. Themes include VLC positioning for industrial V2V, turbulence effects in FSO, and hands-on learning via robotic platforms. His work increasingly bridges technical innovation with social impact, particularly in digital inclusion and gender equity in technology education. Deployment of Visible Light Positioning techniques at low data rate for V2V industrial communications (2024) Evolution of OWC: A Collaborative Contour Across Various Sectors (2024) VLC positioning in low data rate for V2V communication. (2024) Análisis del estado de la digitalización y la brecha digital de género del sector empresarial de la Comunidad Valenciana (2024) Transformación Digital y Equidad de Género en la Formación del Profesorado de Magisterio (2023) Dr. Pérez Soler has contributed to educational innovation through projects like HOP LEARNING and Girls4STEM, promoting inclusive and hands-on learning in engineering. He has been involved in multiple teaching initiatives using software-defined radio and mobile robotics, assessing their impact on student engagement and learning outcomes. While no specific grants are mentioned, his sustained publication output and leadership in educational projects suggest active participation in funded research and teaching development programs. He leads and contributes to interdisciplinary research teams, particularly within the DSDC and HRI groups, focusing on both technical and educational aspects of engineering. His work fosters collaboration between telecommunications engineering and pedagogical innovation, aiming to prepare students for modern technological challenges while promoting equity and accessibility in STEM fields.
Xiaojun Xu is a Professor at the School of Computer Science, Beijing Institute of Technology, with a prolific research career spanning machine learning security, medical AI, and robotics. Their work demonstrates strong interdisciplinary collaboration across computer science, healthcare, and engineering domains. Institution: Beijing Institute of Technology, School of Computer Science Research Focus: AI security, medical imaging, robotics, and remote sensing applications Collaborations: Extensive work with Bo Li (29 papers), Dawn Song (11 papers), and medical researchers Xu's research interests center on adversarial machine learning, with significant contributions to model security, backdoor detection, and LLM unlearning. They've pioneered techniques like Meta Neural Analysis for Trojan detection and developed frameworks for certified robustness. Their medical imaging work focuses on quantitative susceptibility mapping for neurodegenerative diseases, particularly Parkinson's and Alzheimer's. In robotics, they've advanced control systems for quadruped and amphibious vehicles. Recent publications reveal a strong trend toward large language model security, with multiple 2024-2025 papers on machine unlearning and watermarking techniques. Their work bridges theoretical security with practical healthcare applications, particularly in medical image analysis where they've developed tools for subcortical nucleus segmentation and brain age prediction. Key venues: NeurIPS, CCS, IEEE S&P, NeuroImage, IEEE Transactions Research impact: High citation count with consistent top-tier publication record Xu has secured significant research funding, evidenced by the volume and diversity of publications across multiple domains. Their work on blockchain-enabled IoT systems and RAFT-based private blockchain demonstrates expertise in distributed systems. The medical imaging research shows strong hospital collaborations, particularly in developing tools for Parkinson's diagnosis. Current projects appear focused on LLM security challenges and multimodal medical AI systems with potential clinical applications.
Süleyman Mete is an Associate Professor in the Industrial Engineering Department at Gaziantep University's Faculty of Engineering. He has been serving in this position since 2021, following previous roles as a Doctor Lecturer at Gaziantep University (2019-2021) and Munzur University (2017-2019). His academic career began as a Research Assistant at Gaziantep University (2011-2016) and Tunceli University (2010-2011). Dr. Mete earned his Doctorate in Industrial Engineering from Gaziantep University (2013-2017), followed by a Master's degree in Industrial Engineering from the same institution (2011-2013). He completed his undergraduate studies in Industrial Engineering at Selcuk University (2005-2010). Dr. Mete's research focuses on Operations Research, Modeling and Optimization, and Multi-criteria Decision Making. His work spans several specialized areas including disassembly line balancing, humanitarian logistics, risk assessment, and fuzzy logic applications. His research integrates mathematical programming with practical industrial applications, particularly in disaster response, sustainable manufacturing, and supply chain optimization. He has developed innovative approaches combining various multi-criteria decision-making methods with fuzzy logic to address complex real-world problems in occupational safety, transportation, and humanitarian aid distribution. His extensive publication record demonstrates a clear trajectory toward increasingly complex applications of optimization techniques, particularly in humanitarian contexts. Recent work shows a strong focus on drone-assisted logistics, urban mobility sustainability, and advanced risk assessment methodologies using various fuzzy set approaches. His research consistently bridges theoretical methodological development with practical applications in industrial engineering problems. University 2% Most Influential Scientist (2023) University 2% Most Influential Scientist (2022) Dr. Mete has successfully supervised numerous graduate students through their thesis work, with a particular focus on humanitarian logistics, disassembly line balancing, and risk assessment applications. His research projects include "Sustainable, Digital, and Smart Humanitarian Logistics in Disaster Relief Operations" (EU-funded) and "Data-Driven and Web-Based Decision Support System Development for Integrated Optimization of Vector Control Process" (Tübitak 1505). He has also served as a visiting researcher at Karlsruhe Institute of Technology (KIT) in Germany under the TÜBİTAK 2219 Postdoctoral Research Program. Dr. Mete's work demonstrates strong integration between theoretical methodological development and practical applications, particularly in humanitarian contexts and industrial engineering problems. His research group appears to focus on optimization techniques applied to real-world challenges in disaster response, sustainable manufacturing, and supply chain management.
