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Eunhwa Yang is an Associate Professor and Curriculum Committee Chair at the School of Building Construction, Georgia Institute of Technology. She holds a Ph.D. in Human Behavior and Design from Cornell University and an MS in Building Construction from Georgia Tech. Her research focuses on the interplay between built environments and human outcomes, employing Bronfenbrenner’s ecological systems theory to study workplaces, campuses, and homes. Key areas include sustainable building practices, facility management optimization, and aging-in-place strategies for individuals with mild cognitive impairment. As an advocate for innovative teaching methods, Dr. Yang has developed facility management courses using active learning and service learning approaches. She has led workshops on pedagogical strategies at GT’s Center for Teaching and Learning and Cornell’s Center for Teaching Excellence. Professional affiliations include the International Facility Management Association (IFMA) and CoreNet Global, where she has received young researcher awards and fellowships. Her recent work emphasizes data-driven solutions for energy performance contracting, hybrid workspaces, and inclusive housing designs. The Workplace Ecology Lab under her direction explores environmental factors influencing sleep health, workplace safety, and aging-in-place technologies. Over 15 years of grant-funded research has produced impactful studies on green leasing, building lifecycle analysis, and multi-stakeholder decision-making in urban transformation projects. Awards: IFMA Fellowships, IAPS Workshop Grant Grants: CoreNet Global Academic Challenges, IAPS sponsored projects Labs: Workplace Ecology Lab at Georgia Tech School of Building Construction Dr. Yang’s advisory work supports facility managers, corporate real estate directors, and designers in optimizing space utilization through evidence-based strategies. Her research bridges environmental science, architectural design, and human behavior studies to create healthier, more equitable built environments.

• Sustainable Building Practices
• Facility Management Strategy
• Workplace Design and Ergonomics
• +3 more

Melike Erol-Kantarci is an Associate Professor and Tier 2 Canada Research Chair in AI-enabled Next-Generation Wireless Networks at the School of Electrical Engineering and Computer Science, University of Ottawa. She is the founding director of the Networked Systems and Communications Research (NETCORE) laboratory. She holds a PhD and has over 140 peer-reviewed publications, with an h-index of 37. Her research focuses on AI-driven wireless networks, 5G/6G communication systems, smart grids, and cybersecurity. Education: PhD in Electrical Engineering (not explicitly stated, inferred from profile). Research interests include AI-enabled wireless networks, smart grids, IoT, and underwater sensor networks. She has received awards such as the 2019 N2Women recognition and IEEE Best Tutorial Paper Award (2017). She serves on editorial boards of IEEE journals and has organized international conferences. Her recent articles emphasize AI applications in 6G networks, secure communications, federated learning, and resource allocation. Key trends include leveraging LLMs for network orchestration, combating adversarial attacks, and optimizing next-gen wireless architectures. Her work on beam selection, ISAC, and RIS-assisted systems addresses critical challenges in 5G/6G. Awards include the Canada Research Chair and best paper recognitions. She actively contributes to open RAN standards and has co-edited books on smart grids and intelligent transportation. Her lab, NETCORE, drives innovation in networked systems.

• AI-enabled wireless networks
• 5G and 6G communications
• Smart grids
• +7 more

Dr. Miles Hansard is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on 3D scene reconstruction, multi-view imaging, depth cameras, radar localization, and human vision. He specializes in geometric aspects of vision, image processing, and robotics applications. His teaching includes modules on Computer Graphics (for both postgraduate and undergraduate students) and Introduction to Multimedia Systems. These courses cover topics like 3D graphics algorithms, human perception of color, and multimedia system fundamentals. Dr. Hansard's work integrates sensor technologies such as time-of-flight cameras and millimeter-wave radar to improve 3D reconstruction and robotic systems. He contributes to projects like the GRIP software framework for robotic grasping and has published extensively in journals like IEEE Transactions on Image Processing and the International Journal of Neural Systems. He is affiliated with the Centre for Multimodal AI, where he collaborates on research involving sensor fusion, autonomous systems, and visual perception. His expertise bridges computer vision, robotics, and multimedia engineering.

• Computer Vision
• 3D Scene Reconstruction
• Robotics
• +6 more

Owen Embury is a researcher in the Department of Meteorology at the University of Reading, specializing in Earth surface remote sensing and climate science. His work focuses on developing satellite-based datasets for climate applications, particularly sea surface temperature (SST) analysis. He contributes to the European Space Agency’s Climate Change Initiative (SST CCI) and collaborates on projects like the OSTIA system for reprocessing SST data. His research spans over two decades, addressing challenges in satellite data harmonization, cloud detection, and dust-related biases. Key contributions include long-term SST datasets since the 1980s and advancements in thermal infrared radiometry. Embury’s work intersects with global climate monitoring, supporting initiatives like the Copernicus Marine Environment Monitoring Service. Publications emphasize SST validation, Bayesian cloud detection, and multivariate lake monitoring. Collaborations with international teams on projects such as Himawari-8 SST products and GOES-12 sensors highlight his global impact. His research underpins climate models and policy frameworks, reflecting a commitment to advancing Earth observation for scientific and societal benefit.

• Earth Surface Remote Sensing
• Climate Science
• Oceanography
• +3 more

Professor Kerstin Bach is affiliated with the Department of Computer Technology and Informatics at NTNU's Faculty of Information Technology and Electrical Engineering. Her research focuses on artificial intelligence, machine learning, and their applications in health informatics and robotics. Notable projects include the selfBACK app for musculoskeletal pain management and reinforcement learning for robotic systems. She has supervised numerous doctoral students in areas like activity recognition and explainable AI. Education: Formal academic degrees not explicitly listed in text. Research interests span: - Case-Based Reasoning for clinical decision support - EHealth/mHealth applications (e.g., wearable sensor analytics) - Explainable AI methodologies - Human activity recognition using accelerometer data Recent work emphasizes: - Machine learning models for health monitoring (sleep/wake detection, gait analysis) - Reinforcement learning for robotics and autonomous systems - AI-driven clinical tools for pain management Key Projects: selfBACK app: Digital self-management tool for musculoskeletal pain (RCT validated) UtiliGEM: Energy management framework for IoT devices Labs/Teams: Active in NTNU's AI research groups focusing on healthcare informatics and robotics. Collaborates with medical institutions on clinical AI applications.

• Artificial Intelligence
• Machine Learning
• Case-Based Reasoning
• +7 more

Mohammad Naser Sabet Jahromi is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University, Denmark. He is affiliated with the Visual Analysis and Perception Centre for AI Ethics, Law and Policy. His research focuses on explainable AI (XAI), biometrics, machine learning, and ethical AI applications in legal and medical domains. He actively participates in interdisciplinary projects like REPAI: Responsible AI for Value Creation (2023-2027), which explores AI ethics, computational discourse analysis, and value-driven AI systems. His educational background is not explicitly detailed in the provided text, but his research trajectory indicates strong expertise in computer science and AI systems. Key research interests include interpretable machine learning models, privacy-preserving biometric systems, and AI applications in asylum adjudication and educational assessment. Recent work emphasizes developing XAI frameworks like SIDU-TXT for NLP, verifying machine unlearning mechanisms, and automating large-classroom assessments. His projects bridge technical AI advancements with societal implications through collaborations with legal and ethical scholars. Notable contributions include datasets evaluating XAI methods in medicine and methodologies for transparent AI decision-making. He has participated in conferences such as ICPR 2024 and JURISIN 2023, showcasing interdisciplinary research impact.

• Explainable Artificial Intelligence
• Biometrics
• Machine Learning
• +4 more

Dr. Sivakumar Rathinam is a Professor in the Department of Mechanical Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Science & Engineering department. He holds certifications as a Fellow of ASME (2021) and Senior Member of IEEE (2019). His research focuses on motion planning for autonomous vehicles, collaborative decision-making, combinatorial optimization, and vision-based control systems. He leads the Autonomy Lab, addressing challenges in multi-agent systems, path planning, and rural autonomous vehicle accessibility. Education: Ph.D., Civil Systems Engineering, University of California, Berkeley (2007) M.S., Electrical Engineering & Computer Science, UC Berkeley (2006) M.S., Mechanical Engineering, Texas A&M University (2001) B.Tech., Mechanical Engineering, Indian Institute of Technology Madras (1999) Research Highlights: Dr. Rathinam's work spans autonomous vehicle navigation, UAV coordination, and sensor fusion for adverse conditions. His lab develops algorithms for multi-agent pathfinding and persistent monitoring missions. Recent efforts include rural road detection datasets (R2D2) and thermal/LIDAR sensor fusion for safety in challenging environments. Awards: Outstanding Faculty Contribution Award (2021) Best Paper Runner-Up, ICAPS (2021) Teaching Excellence Award (2012) Labs & Teams: Directs the Autonomy Lab, collaborating with industry partners through the Mechanical Engineering Industry Advisory Council. Active in NSF-funded projects on equitable rural autonomy and multi-UAV recharging frameworks.

• Motion Planning
• Autonomous Vehicles
• Collaborative Decision Making
• +3 more

Yehuda Bock is a Senior Lecturer and Research Geodesist at the University of California San Diego (UCSD), affiliated with the Scripps Institution of Oceanography and its Institute of Geophysics and Planetary Physics (IGPP). He directs the Scripps Orbit and Permanent Array Center (SOPAC), a key facility for geodetic data management and analysis. His roles include advancing seismogeodetic techniques for natural hazard mitigation and improving observational networks. Education: B.A. from New York University, B.S. from Technion-Israel Institute of Technology, M.S. and PhD from Ohio State University. Research focuses on crustal deformation, geodetic instrumentation, and integrating GNSS with seismic data for earthquake and tsunami warning systems. He explores applications of GNSS in meteorology, structural health monitoring, and climate studies. Recent work emphasizes real-time data fusion, machine learning for geophysical analysis, and understanding tectonic processes in regions like the San Andreas Fault and Cascadia Subduction Zone. His publications highlight advancements in deformation modeling, early warning systems, and interdisciplinary approaches to geoscience data.

• Geodesy
• Lithospheric Deformation
• Seismology
• +4 more

Dr. Annette Stahl is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). She is an Onsager Fellow and leads the Robot Vision Group, overseeing the AILARON project funded by the Research Council of Norway. Her roles include affiliation with NTNU AMOS and the SFI AutoShip Centre for Research-based Innovation. Stahl holds a PhD in Applied Mathematics (Computer Vision) from Heidelberg University. Affiliations: NTNU AMOS, SFI AutoShip, and AILARON project leadership Research Focus: Robotic vision, autonomous systems, underwater exploration, and aquaculture monitoring Education: PhD in Applied Mathematics (Computer Vision) – Heidelberg University Research Interests: Her work spans robotic and computer vision, control theory, autonomous vehicles, mathematical image analysis, and machine learning applications in aquaculture and maritime systems. Key focus areas include underwater SLAM, sensor fusion for autonomous ships, and plankton detection using AI. Articles: Recent publications emphasize underwater robotic systems, sensor fusion for autonomous navigation, and AI-driven aquaculture monitoring. Key themes include 3D reconstruction, visual SLAM, and maritime tracking algorithms. Grants & Projects: AILARON (FRINATEK/IKTPLUSS) AUTOSIGHT (IKTPLUSS) AROS (IKTPLUSS) SFI AutoShip (SFI) Advising: Main supervisor for 10+ PhD candidates (e.g., Trym Nygård, Mauhing Yip) Co-advisor for projects like INDISAL (fish identification) Labs/Teams: Robot Vision Group at NTNU Collaborations with SINTEF Ocean and Zebop AS

• Robotic Vision
• Autonomous Vehicles
• Computer Vision
• +7 more

Dr. Fernando E. Casado is a Researcher at the Personal Robotics Lab (PRL) within the Department of Electrical and Electronic Engineering at Imperial College London since March 2023. He holds a BSc in Computer Science (2017, University of Santiago de Compostela) with awards for academic excellence, an MSc in Artificial Intelligence Research (2018, Menéndez Pelayo International University), and a PhD in Computer Science (2022, USC) with Cum Laude distinction for his work on continual federated learning strategies. His research focuses on multi-robot and multi-user machine learning to enhance trustworthy human-robot interaction, addressing challenges such as concept drift, non-stationary data, and personalized robotic behavior. Key contributions include federated learning frameworks for assistive robotics, adaptive algorithms for heterogeneous data, and eye-gaze tracking for trust assessment in HRI. Publications span topics like federated learning, continual learning, and human-robot trust, with applications in assistive devices and smart environments. Awards include the Best Academic Record and Thesis Award (BSc) and the highest honors in his PhD. Casado’s work bridges theoretical machine learning with practical robotics, emphasizing privacy-aware systems and user-centered design. He collaborates internationally, including visits to PRL in 2021, and contributes to advancing adaptive algorithms for real-world robotic applications.

• Machine Learning
• Robotics
• Human-Robot Interaction
• +5 more

Alberto Vale is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Lisbon's Instituto Superior Técnico. His research focuses on robotics, control systems, nuclear fusion engineering, and autonomous systems. He is affiliated with the Institute of Plasmas and Nuclear Fusion and teaches courses such as Control of Cyber-Physical Systems and Autonomous Systems . His work emphasizes remote maintenance systems for nuclear facilities, radiation detection technologies, and mobile robotics applications. Research interests include: Advanced control strategies for cyber-physical systems Development of autonomous systems for nuclear fusion facilities Radiation detection and localization using drones and mobile robots Path planning and navigation in constrained environments Recent publications highlight innovations in fusion diagnostics (e.g., reflectometry systems for DEMO), LIDAR-based SLAM techniques, and optimization of autonomous fleets for solar farm inspection. He has received the EDA Research, Technology, and Innovation Papers Award 2023 and the Best Presentation Award .

• Robotics
• Control Systems
• Nuclear Fusion Engineering
• +5 more

K.C. Chang is a Professor in the Department of Systems Engineering and Operations Research at George Mason University's Volgenau School of Engineering. He is the Director of the Sensor Fusion Lab and the coordinator of the Financial Engineering Program at GMU, reflecting his interdisciplinary research focus on data fusion and probabilistic reasoning. Education: PhD, Electrical Engineering, University of Connecticut MS, Electrical Engineering, University of Connecticut BS, Communication Engineering, National Chiao-Tung University Dr. Chang's research centers on distributed data fusion, Bayesian networks, and decision-making under uncertainty, with applications in sensor networks, wireless communications, satellite systems, and financial engineering. His work integrates statistical modeling, machine learning, and systems engineering principles to solve complex, real-world problems involving uncertainty and large-scale information processing. The recent publications reflect a strong trend in applying probabilistic reasoning to dynamic spectrum access, satellite communication, and electronic warfare. His research leverages copula functions and information genealogy to enhance fusion accuracy in non-Gaussian and dependent environments. Applications span defense, aerospace, and finance, demonstrating the versatility of his methodological contributions. Scientific Awards: IEEE Fellow (2010) for contributions to sensor data fusion and Bayesian probabilistic inference Dr. Chang has advised numerous graduate students and led major research initiatives funded by the Office of Naval Research (ONR), National Science Foundation (NSF), Army Research Office (ARO), Air Force Office of Scientific Research (AFOSR), and industry partners like Intelligent Fusion Technology Inc. and Impact Technologies, LLC. His editorial roles include serving as Editor for Tracking/Navigation Systems and Large Scale Systems in IEEE Transactions on Aerospace and Electronic Systems, and as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics. He was also Technical Program Co-Chair of the 2009 International Conference on Information Fusion. He leads the Sensor Fusion Lab at George Mason University, a research group focused on advancing the theory and application of multi-sensor, multi-source information fusion. The lab works on foundational algorithms as well as real-world implementations in defense, communication, and financial systems. The lab collaborates with government agencies and private sector organizations to translate theoretical advances into operational technologies.

• Data Fusion
• Bayesian Networks
• Probabilistic Reasoning
• +9 more

Edmund Førland Brekke is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU). He leads the Autosit and Autosight projects, and serves as a work package leader in the SFI Autoship center. His research focuses on target tracking, navigation, and SLAM, with applications in collision avoidance for unmanned vessels. He co-founded the company Zeabuz for marine surface autonomy solutions. PhD in Technical Cybernetics (NTNU, 2010) MSc in Industrial Mathematics (NTNU, 2005) Research Interests: Edmund specializes in theoretical foundations of multitarget tracking, integrating target tracking with navigation/SLAM, and applying these to autonomous maritime collision avoidance. His work addresses challenges in heavy-tailed clutter, sensor fusion, and situational awareness for autonomous ferries and river barges. Publications Trends: His recent work (2018–2022) emphasizes collision avoidance algorithms, sensor fusion, and SLAM for autonomous vessels. Key areas include maritime radar tracking, trajectory prediction using AIS data, and integration of visual/Lidar sensors for navigation. Guidance: He has supervised numerous PhD candidates on topics like digital twins, multi-sensor tracking, and risk assessment for autonomous ships, including graduates from 2014–2024. Projects: Active in Autoferry (autonomous ferries), Autobarge (river barges), and the ORCAS initiative. Collaborates with the SFI Autoship center.

• Target Tracking
• Navigation
• SLAM
• +4 more

Evangelos E. Milios is a Professor in the Faculty of Computer Science at Dalhousie University , Halifax, Nova Scotia. He has been a faculty member since 1998 and leads the MALNIS (Machine Learning and Networked Information Spaces) research group. He is affiliated with the Institute of Big Data Analytics and served as Scientific Director of DeepSense , an innovation hub for ocean data analytics. Education: PhD in Electrical Engineering and Computer Science, MIT (1986) SM & EE, MIT (1983) Dipl. Eng. in Electrical Engineering, NTUA, Greece (1980) His research focuses on visual text analytics, text mining, graph mining, social network analysis, and machine learning . He has made significant contributions to modeling and mining of networked information spaces, with applications in data science and AI. The recent publications reflect a strong trend in data mining, robotics, pattern recognition, and semantic analysis , particularly in log analysis, pose estimation, and information retrieval. His work bridges theoretical algorithms with practical applications in robotics and web technologies. Scientific Awards and Honors: Distinguished Research Professor (2017–2022) Killam Chair in Computer Science (2006–2011) Senior Member, IEEE Professional Engineer, Ontario (1998–2024) He has served in key administrative roles including Associate Dean, Research (2008–2017) and Director of the Graduate Program (1999–2002) . He has supervised numerous graduate students and taught a wide range of courses in AI, machine learning, data science, and networking. His research is supported by major grants and collaborations, including NSERC and industry partnerships. Research Labs and Teams: MALNIS – Focuses on machine learning and networked information spaces. DeepSense – Ocean data analytics and AI innovation. Institute of Big Data Analytics – Cross-disciplinary big data research.

• Visual Text Analytics
• Modelling and Mining of Networked Information Spaces
• Text Mining
• +8 more

Professor Richard Lucas holds a Sêr Cymru Research Chair in the Department of Geography and Earth Sciences at Aberystwyth University. He has previously held academic and research positions at the University of New South Wales, the Australian Federal Government, and Swansea University. His research focuses on Earth observation and ecosystem dynamics, particularly in terrestrial and coastal environments. His expertise lies in utilizing satellite remote sensing to quantify ecosystem responses to climatic and anthropogenic changes. Key research areas include forest biomass estimation, mangrove monitoring, land cover change detection, and environmental sustainability. He has developed innovative frameworks such as the Earth Observation Data for Ecosystem Monitoring (EODESM) and leads the national Living Wales project for land cover monitoring. His recent publications highlight a strong trend in using multi-source Earth observation data—especially Landsat, Sentinel, and GEDI lidar—for global and regional environmental assessments. These works span topics such as mangrove resilience under tropical cyclones, habitat mapping, soil moisture analysis, and biomass modeling, reflecting a cohesive focus on sustainable ecosystem management through advanced remote sensing. Professor Lucas leads several externally funded research projects with agencies including the European Space Agency, the Welsh Government, and the Australian Research Council. Notable initiatives include CCI-BIOMASS, EO4AgroClimate, and the Global Mangrove Watch. His datasets, such as the Australia's Mangrove Portal and Plant Biomass Library, are publicly available and widely used in the scientific community. He has contributed significantly to policy-relevant science, with research impacting coastal land use policy and supporting UN Sustainable Development Goals, particularly those related to climate action and life on land. His work has been featured in media outlets and policy documents, demonstrating broad societal impact. Professor Lucas is actively involved in international collaborations and scientific outreach, regularly presenting at workshops and conferences such as Living Coasts and Dynamics Coasts. He mentors researchers and contributes to editorial and peer-review activities, further strengthening the scientific community in Earth observation and environmental informatics.

• Remote Sensing
• Earth Observation
• Ecosystem Dynamics
• +10 more