Panos Trahanias is Professor and Vice Chair in the Department of Computer Science at the University of Crete, and heads the Computational Vision and Robotics Laboratory (CVRL) at Foundation for Research and Technology - Hellas (FORTH). His research bridges computational vision, robotics, and embedded systems, with specific focus on humanoid robot navigation, real-time SLAM implementations, and adaptive control systems. Recent investigations include developing climbing quadruped robots, robust grip-lifting mechanisms, and probabilistic contact estimation methods for dynamically challenging terrains. Medical applications feature prominently through neural network approaches for ventilator waveform analysis in critical care settings. Hardware innovation is demonstrated through FPGA-accelerated visual SLAM architectures and reconfigurable embedded systems designed for resource-constrained robotic platforms. His work consistently advances the integration of probabilistic methods, deep learning, and adaptive control in autonomous systems.
Milton Halem is a Research Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), affiliated with the College of Engineering and Information Technology. He also holds an Emeritus position as Chief Information Research Scientist at NASA Goddard Space Flight Center's Earth Sciences Directorate. His expertise spans machine learning, quantum computing, atmospheric science, and climate modeling. Halem's research focuses on integrating AI with environmental systems, including wildfire digital twins, planetary boundary layer estimation, and climate forecasting. Key research interests include applying deep learning to wildfire prediction, developing quantum algorithms for optimization, and advancing climate observation systems. His work bridges disciplines like remote sensing, cybersecurity, and geophysical data analytics. Awards include NASA's Exceptional Scientific Achievement Medal (2022), Outstanding Leadership Medal (2018), and the Distinguished Service Medal (1996). Affiliations: UMBC CSEE, NASA Goddard (Emeritus) Notable Projects: Wildfire Digital Twin Initiative, SOAR atmospheric radiances system, AI-enhanced air quality forecasting Publications emphasize machine learning applications in climate science, quantum computing, and environmental monitoring. Collaborations include NASA, NOAA, and academic partners worldwide. Current projects explore AI-driven climate models and real-time wildfire impact assessment systems.
Heidar A. Malki is a Professor of Engineering Technology and Senior Associate Dean of the Technology Division at the Cullen College of Engineering, University of Houston (UH). He holds a joint appointment in the Electrical and Computer Engineering Department and has over three decades of academic and research experience. He earned his Ph.D. in Electrical Engineering from the University of Wisconsin-Milwaukee (1990). His roles include Department Chair (2009–present) and Associate Dean for Research (2004–2009). He is a Senior Member of IEEE and serves as an Associate Editor for the IEEE Transactions on Fuzzy Systems. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Milwaukee (1990) M.S. in Electrical Engineering, University of Wisconsin-Milwaukee (1985) B.S. in Electrical Engineering, University of Wisconsin-Milwaukee (1983) Research Interests: Dr. Malki specializes in control systems, neural networks, fuzzy logic, and smart grid optimization. His work bridges academic research with industrial applications, particularly in the energy and telecommunications sectors. Notable areas include neuro-fuzzy controllers, power system dynamics, and cyber-security for critical infrastructure. He has collaborated with organizations like Southwestern Bell and the oil/gas industry on neural network applications. Publications & Awards: With over 100 publications, Dr. Malki’s work spans journals like IEEE Transactions on Fuzzy Systems and International Journal of Bifurcation and Chaos . His awards include the Fluor Daniel Outstanding Faculty Award (2001, 2003) and recognition in Who's Who in America . He has authored textbooks on control systems and contributed to academic volumes on fuzzy logic applications. Grants & Leadership: He secured funding for initiatives like the Houston Information Technology Workforce Certification Center and led conferences such as the 1997 IEEE International Conference on Neural Networks. His educational contributions include pioneering web-based control systems laboratories and interdisciplinary graduate programs in technology. Labs & Teams: His research teams focus on advanced wireless sensor networks, mechatronics, and energy system optimization. Collaborations extend to NASA and the U.S. Department of Energy, emphasizing applied engineering solutions for real-world challenges.
Visham Ramsurrun is a Senior Lecturer and Programme Coordinator of the MSc Cyber Security programme at Middlesex University Mauritius. He holds a Ph.D. and B.Sc. in Computer Science & Engineering from the University of Mauritius, and a Postgraduate Certificate in Higher Education from Middlesex University (UK). His research focuses on cybersecurity, IoT, network design, machine learning, and smart agriculture. Education: Ph.D. in Computer Science & Engineering (2007-2010), University of Mauritius Postgraduate Certificate in Higher Education (2017-2018), Middlesex University (UK) BSc (Hons) Computer Science & Engineering (2000-2003), University of Mauritius Research interests include: Cybersecurity frameworks for IoT and autonomous systems Blockchain applications in healthcare and data management Network defense mechanisms (e.g., SDN-based moving target defense) Energy-efficient sensor networks and transmission optimization Smart agriculture systems and environmental monitoring Recent publications emphasize cybersecurity in autonomous vehicles, IoT-enabled healthcare systems, and sensor-based home security solutions. He has secured grants from the Mauritius Research and Innovation Council (MRIC) and collaborates with industry partners on innovation projects. His work includes developing augmented reality control platforms and low-cost air quality monitoring systems. As a member of the IPv6 Forum Mauritius, he advocates for network architecture advancements. His research spans academic collaborations across disciplines, with a focus on underserved regions' technological challenges.
Ioannis Kotidis is an Associate Professor in the Department of Informatics at the Athens University of Economics and Business (AUEB), School of Information Sciences and Technology. He holds a Diploma in Electrical and Mechanical Engineering from the National Technical University of Athens (1995), and Master's and Ph.D. degrees from the University of Maryland (1997, 2000). Prior to joining AUEB, he worked as a Senior Technical Specialist at AT&T Labs-Research in Florham Park, New Jersey until January 2006. His research spans multiple areas of database systems with particular focus on On-Line Analytical Processing (OLAP) and data warehousing, data mining, sensor/P2P networks, mobile data management, data fusion & dissemination, data streams, RFID data management, approximate query answering, and database preservation . His work bridges theoretical foundations with practical implementations, as evidenced by numerous publications in top-tier database conferences and journals. Professor Kotidis leads several significant research projects including RECOST (REal time management of COmplex STreams), DBSENSE (Information Management in Wireless Sensor Networks), INFORE (Interactive Extreme-Scale Analytics and Forecasting), and DeLorean (Storage, Indexing and Analysis Techniques for Time-Series Management). His recent work focuses on blockchain applications for decentralized OLAP processing, complex event processing frameworks, and advanced techniques for managing complex data streams. Among his notable achievements is the best paper award at the ACM SIGMOD International Conference on Management of Data for his work on DynaMat: A Dynamic View Management System for Data Warehouses. His publications consistently address challenging problems in database systems with innovative approaches that have influenced both academic research and practical implementations. He has supervised numerous undergraduate theses on topics including blockchain-based OLAP view management, complex event processing using FlinkCEP, and graph similarity learning. His research has attracted significant funding through various research programs at AUEB, including Basic Research Funding Programs 1 & 2.
Dr. Peiyuan Pan is a Senior Lecturer in Computer Science at London Metropolitan University, affiliated with the School of Computing and Digital Media. He holds a PhD in Computer-aided Manufacturing Engineering, a postgraduate certificate in Teaching and Learning in Higher Education, and a BSc (Hons) in Computer Science. He specializes in teaching OO programming, web systems development, and e-commerce applications. His research focuses on software system development, AI technologies, embedded systems, e-manufacturing, and supply chain management. Dr. Pan has led several research projects, including a Virtual Surgery system for Java programming education (2010–2011) and an Internet-based supply chain improvement system (1999–2002). He received the Vice Chancellor's Teaching Fellowship Award in 2010–2011. His publications span e-learning methodologies, robotics, and manufacturing automation. He contributes to interdisciplinary work in AI-driven design systems, fuzzy logic protocols, and web-based expert systems. His teaching responsibilities include leading the Computing and Business Information Technology FdSc program. Professional affiliations include the ACM and active participation in international conferences on computing and manufacturing systems.
Luca Beber is a Lecturer at the University of Trento, affiliated with the Department of Information Engineering and Computer Science. He specializes in distributed systems, robotic perception, and automation engineering. His teaching activities include courses on distributed estimation for robots and vehicles, focusing on advanced technological solutions and theoretical aspects of distributed control systems. Research interests revolve around integrating artificial intelligence techniques into robotic systems for applications in industrial automation, medical robotics, and material characterization. His work emphasizes practical and theoretical challenges in distributed systems, including robotic perception, viscoelastic tissue analysis, and collaborative robotic arms for medical diagnostics. Notable research contributions include robotized palpation for cancer detection and anatomy-aware teleoperation systems for medical imaging. Beber holds a pivotal role in bridging theoretical robotics with real-world applications, particularly in healthcare and industrial automation.
Ozlem Kilic is the Dean of the College of Emerging and Collaborative Studies at the University of Tennessee. Previously, she served as Associate Dean for Academic and Student Affairs in the Tickle College of Engineering and held roles as Professor and Associate Dean at the Catholic University of America. Her academic background includes a DSc in Electrical Engineering from George Washington University (1996), an MS (1991), and a BS from Bogazici University (1989). Kilic has over 25 years of professional experience, including roles as an Electronics Engineer at the U.S. Army Research Laboratory and Senior Engineer/Program Manager at COMSAT Laboratories specializing in satellite communications and antenna systems. Her research focuses on antennas, wave propagation, satellite communications, microwave remote sensing, computational electromagnetics, and radar-based vital sign detection. Notable contributions include developing hybrid numerical electromagnetic tools for defense applications and pioneering non-contact vital sign monitoring systems using UWB radar. She has authored over 150 publications in these areas. Education: DSc in Electrical Engineering, George Washington University (1996) MS in Electrical Engineering, George Washington University (1991) BS in Electrical Engineering, Bogazici University (1989) Kilic's work bridges theory and application, with emphasis on high-performance computing for large-scale electromagnetic problems and bio-inspired optimization techniques. She received the ACES Outstanding Service Award (2017) and became an ACES Fellow (2016). Her lab develops low-cost anechoic chambers and innovative radar systems for healthcare and infrastructure inspection applications. Current research includes: 5G antenna concepts and millimeter-wave systems Non-invasive health monitoring via Doppler radar Compressive sensing for through-wall detection GPU-accelerated electromagnetic simulations Her work has been applied to military antenna design, disaster response sensing, and clinical patient monitoring systems.
Prof. Kui Wu is a Professor in the Department of Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. His research focuses on computer networks, wireless and mobile networking, mobile computing, and network security. He is part of the Parallel, Networking and Distributed Computing (PANDA) research group. Key areas of expertise include distributed learning frameworks, autonomous systems, IoT anomaly detection, and edge computing architectures. His work integrates machine learning techniques with network optimization, addressing challenges in real-time systems, security, and resource allocation. Notable contributions include advancements in federated learning, privacy-preserving distributed systems, and UAV-based monitoring solutions. Prof. Wu's research also explores edge computing innovations, such as smart contract-aided IoT resource sharing and energy-efficient edge data centers. He has contributed to over 50 peer-reviewed publications, with recent work emphasizing AI-driven network design, anomaly detection in IoT, and reinforcement learning applications in autonomous driving safety. His research has practical implications for improving the reliability and efficiency of next-generation communication and computing infrastructures.
Qi Han is a Professor in the Department of Computer Science at Colorado School of Mines since 2005. She leads the Pervasive Computing Systems (PeCS) research group, focusing on algorithms and systems for pervasive/mobile computing applications. Her work bridges networking, distributed systems, and robotics with applications in cyber-physical systems (CPS), IoT, environmental monitoring, energy-efficient infrastructure, and mine safety. She holds a Ph.D. in Computer Science from the University of California-Irvine (2005). Education : - Ph.D. Computer Science, University of California-Irvine (2005) Research Interests : Dr. Han's research spans robotics, mobile sensing, and CPS, emphasizing interdisciplinary projects like underground mine safety, smart buildings, and oil refinery inspection. Recent efforts include drone-UGV collaboration, AR systems, and edge computing optimizations. She collaborates globally with institutions like CSIRO (Australia) and Chinese Academy of Sciences. Grants & Collaborations : - Multiple NSF grants secured - Active international partnerships in CPS/IoT research Labs & Projects : - PeCS Group: Develops systems for mobile/robotic sensing applications - Current projects include energy-aware path planning, AR pose estimation, and multi-drone coordination
Fei Miao is a Pratt & Whitney Associate Professor at the School of Computing, University of Connecticut, and a courtesy faculty member of the Department of Electrical & Computer Engineering. She serves as Director of the Miao Embodied AI Lab and is affiliated with the Institute for Advanced Systems Engineering. Previously, she was a postdoc researcher at the GRASP Lab and PRECISE Lab with Professors George J. Pappas and Daniel D. Lee at the University of Pennsylvania. Dr. Miao received her PhD in Electrical and Systems Engineering from the University of Pennsylvania in 2016, where she also earned a dual Master's degree in Statistics from the Wharton School. She completed her undergraduate studies at Shanghai Jiao Tong University, earning a Bachelor's degree in Automation with a minor in Finance in 2010. Her research focuses on developing the foundations for the science of Embodied AI, with emphasis on assuring safety, efficiency, robustness, and security of cyber-physical systems through the integration of learning, optimization, and control. Her technical expertise spans multi-agent reinforcement learning, robust optimization, uncertainty quantification, control theory, and game theory. These methods are applied to connected and autonomous vehicles, intelligent transportation systems, transportation decarbonization, smart cities, and power networks. Her work involves both theoretical development and practical implementation, including system modeling, theoretical analysis, algorithmic design, and experimental validation using real urban transportation data, simulators, and small-scale autonomous vehicles. Dr. Miao's publication record reveals a strong focus on robustness in AI systems for transportation applications, with recent work emphasizing uncertainty quantification, safety guarantees, and multi-agent coordination. Her research demonstrates a clear trajectory from foundational theoretical work to practical implementations in real-world transportation systems. Her notable awards include the prestigious NSF CAREER Award (2021) for "Distributionally Robust Learning, Control, and Benefits Analysis of Information Sharing for Connected and Autonomous Vehicles," a Best Paper Award at ICCPS'21 for "DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services," and the "Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation" during her PhD studies. Dr. Miao has secured significant research funding, including a $509,573 NSF CAREER Award (2021-2026) and a $2.3 million NSF collaborative grant as PI of UConn (2020-2023). She has also received multiple NSF grants for projects related to electric vehicle fleets, vehicular sensing, and control for smart city systems. She actively collaborates with researchers across institutions and has given talks at leading universities and industry research labs including CMU, Microsoft Research, Northeastern, Caltech, UCLA, USC, UCSD, Facebook FAIR, Lawrence Berkeley National Lab, UC Berkeley, Nvidia, Stanford, Princeton University, Columbia University, Waymo, and New York University.
Dr. McKenzie Skiles is an Associate Professor at the University of Utah's School of Environment, Society & Sustainability, leading the Snow Hydrology Research-to-Operations Laboratory (Snow HydRO Lab). Her work focuses on snow hydrology, dust-on-snow radiative forcing, and remote sensing applications to assess snowpack dynamics and water security in semi-arid mountain regions. She holds a PhD in Geography from UCLA (2014), alongside multiple degrees from the University of Utah, including dual BS in Geography and Environmental Studies (2008) and an MS in Geography (2010). Her academic journey includes a postdoc at Caltech/JPL (2015–2016) and prior roles as an Assistant Professor at the University of Utah (2017–2023) and Utah Valley University (2016–2017). Her research integrates field observations, numerical modeling, and remote sensing to quantify how light-absorbing particles (dust, black carbon) accelerate snowmelt, impacting water resources in the Western U.S. Key projects include the Dust^2 initiative and the Surface Atmosphere Integrated Field Laboratory (SAIL). Recent work highlights the shrinking Great Salt Lake's role in increasing dust-on-snow events in the Wasatch Mountains. Publications emphasize snow albedo modeling, dust radiative forcing, lidar applications, and machine learning for snow water equivalent estimates. Collaborations span federal agencies (NASA, NOAA) and universities, with a focus on bridging science and operational water management.
Sabur H Baidya is an Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville's J.B. Speed School of Engineering. He leads the Autonomous Intelligent Mobile Systems Lab (AIMS Lab) and is affiliated with the Louisville Automation & Robotics Research Institute (LARRI). His research focuses on autonomous systems, cyber-physical systems, IoT, edge computing, and distributed intelligence. Education: B.S. in Communication Engineering, West Bengal University, 2007 M.S. in Computer Science, University of Texas at Dallas, 2013 Ph.D. in Computer Science, University of California, Irvine, 2019 Research Interests: His work integrates sensing, communication, and computing systems to develop intelligent distributed systems. Key areas include IoT security, optimization of edge computing architectures, and autonomous drone/robotics applications. He explores machine learning techniques for resource-constrained environments and cybersecurity in containerization platforms. Awards & Recognition: No specific awards listed, though his research has been published in top-tier venues across autonomous systems, IoT, and cybersecurity domains. Advising & Grants: While no student names or grant details are provided, his lab actively engages in collaborative projects with industry partners like Nokia Bell Labs and Huawei, and academic collaborations at Rutgers WINLAB and UC San Diego. Labs & Teams: Directs the AIMS Lab, collaborating with the Louisville Automation & Robotics Research Institute (LARRI) to advance robotics and automation technologies. His work also intersects with the Jacobs School of Electrical and Computer Engineering (UCSD) and WINLAB (Rutgers).
Scott Nooner is a Professor of Geophysics at the University of North Carolina Wilmington, leading the Crustal Dynamics and Geophysics Laboratory. His research focuses on mid-ocean ridge systems, seafloor geodesy, and crustal deformation processes. He specializes in using geophysical techniques like seafloor gravity, compliance measurements, and pressure gauges to study magma dynamics at Axial Seamount and other volcanic systems. His work includes studying the interplay between tectonics and hydrothermal systems, monitoring CO₂ sequestration at the Sleipner Project in the North Sea, and analyzing deformation caused by monsoonal flooding in Bangladesh. He teaches courses such as Natural Disasters, Geological Oceanography, and Introduction to Geophysics. Key Research Areas: Axial Seamount eruption dynamics, magma chamber compartmentalization, seafloor geodetic monitoring, CO₂ storage, and crustal deformation in subduction zones. Notable Projects: Collaborative monitoring of Alaska and Cascadia subduction zones, long-term studies at Axial Seamount, and Bangladesh delta subsidence analysis. Nooner advises a diverse group of graduate and undergraduate students, including Audra Sawyer, Will Hefner, and Kevin Lally. His lab maintains active partnerships with oceanographic institutions and operates advanced seafloor instrumentation networks.
Professor George Korres specializes in Electric Power Engineering at the National Technical University of Athens. His research encompasses power system analysis, protection schemes, and industrial control systems. Teaching responsibilities include courses on: Supervision/Management of Energy Systems Energy Control Centers Power System Protection Recent research focuses on advanced state estimation techniques for modern power grids, particularly hybrid measurement systems combining SCADA and PMU data. Publications demonstrate applications in transmission networks, HVDC systems, distribution grids with renewable integration, and adaptive protection for island systems.
