جست‌وجوی استاد و پژوهشگر

• Distributed Machine Intelligence
• Low-Power AI for IoT
• Indoor Localization
• +۳ مورد دیگر

Anuradha Ravi is a Research Assistant Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). Previously, she served as a Research Scientist at the Living Analytics Research Center, Singapore Management University (2018-2023), and as an Assistant Professor at Shiv Nadar University (2016-2018). She holds a Ph.D. in Computer Science from the Indian Institute of Technology Roorkee, where her doctoral research focused on optimizing energy efficiency and latency reduction for mobile devices through intelligent offloading strategies in heterogeneous networks. Her research interests span multiple cutting-edge domains in computing and networking: Distributed machine intelligence at the edge Low-power AI systems for IoT devices Indoor localization and occupancy sensing Wireless networking protocols Mobile and ubiquitous computing Smart building management systems Analysis of her recent publications (2015-2025) reveals a consistent focus on efficiency optimization in constrained environments, with key themes including autonomous robotics, AI-driven compression techniques, multi-sensor localization, and energy-aware mobile systems. Her work increasingly emphasizes real-world applications in contested or resource-limited settings, leveraging machine learning for adaptive solutions. She actively mentors graduate students at UMBC in projects related to perception systems for unmanned vehicles, compression-aware federated learning, and network middleware development. During her tenure at Shiv Nadar University, she supervised undergraduate research on cooperative IoT strategies and 5G edge computing. She previously contributed to the Living Analytics Research Center's initiatives in smart building occupancy detection and collaborative machine intelligence frameworks for IoT devices.

Evangelos Boukasمشاهده پروفایل

دانشیار

• Deep Learning
• Unmanned Aerial Vehicles
• Computer Vision
• +۵ مورد دیگر

Evangelos Boukas is an Associate Professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), Faculty of Engineering. His research focuses on autonomous robotic systems for marine vessel inspection, confined space navigation, and man overboard detection using UAVs and deep learning techniques. His core research interests include Deep Learning , Unmanned Aerial Vehicles , Computer Vision , and Robotics , applied to solve challenges in marine robotics and autonomous inspection. He develops uncertainty-aware navigation systems, probabilistic segmentation methods, and reinforcement learning frameworks for real-world deployment in complex environments. Recent publications demonstrate a strong trend toward GPU-accelerated deep learning for confined space inspection, with emphasis on uncertainty estimation, domain adaptation, and real-time processing. Key application areas include marine vessel classification, ballast tank inspection, and search-and-rescue operations, often leveraging multimodal sensor fusion. Dr. Boukas actively supervises five PhD students across major research projects: Probabilistic Deep Learning for Autonomous Aerial Inspection (2025-2028) Aerial Robots Design for Confined Spaces Inspection (2024-2027) Active Semantic Segmentation for Confined Spaces using Aerial Robots (2024-2027) Autonomous Aerial Robotics for Man Overboard Incidents (2023-2026) Visual and Multimodal Perception for Construction Robotics (2023-2026) He leads the "Perception and Cognition for Autonomous Systems" research group at DTU, which develops advanced perception algorithms for robots operating in challenging environments like ship ballast tanks and marine vessels, contributing to UN Sustainable Development Goals for industry innovation and marine conservation.

Ning Wangمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Human-Machine Interaction
• +۵ مورد دیگر

Dr. Ning Wang is an Associate Professor in Robotics and AI at Sheffield Hallam University's Department of Computing. She holds a PhD in Electronics Engineering from The Chinese University of Hong Kong (2011). Her research focuses on human-machine interaction (HMI), embodied AI, and healthcare robotics applications. She has led projects funded by EU FP7, ASTUTE 2020, and the Royal Society. Key achievements include advancements in robotic grasping, teleoperation systems, and autonomous driving algorithms. Awards include Best Paper at ICAC’24 and DISA’23. Education: PhD in Electronics Engineering (2011, The Chinese University of Hong Kong) Affiliations: Communication and Computing Research Centre; Industry and Innovation Research Institute Professional Roles: Associate Editor for International Journal of Advanced Robotic Systems and Frontiers in Computer Science Research interests span robotics, AI, and healthcare integration. Notable contributions include multimodal human-robot interaction frameworks and hybrid force/motion learning systems. Recent work on vision-guided grasping (2025) and impedance control (2024) highlights her impact in dexterous robotics. Awards include 7 major recognitions across conferences like ICAC, DISA, and IEEE forums. She actively participates in organizing international conferences (e.g., ICAC’22, ICIT’24) and serves as a grant reviewer for UKRI and NWO. Labs/Teams: Leads a robotics team focusing on embodied AI, teleoperation, and medical robotics applications. Collaborates with industry partners on autonomous systems and smart manufacturing solutions.

Eleni Boumpaمشاهده پروفایل

پژوهشگر

• Smart Environments
• Healthcare Services
• Cyber-Physical Systems
• +۳ مورد دیگر

Eleni Boumpa is a Researcher and Ph.D. candidate at the Department of Computer Science and Biomedical Informatics, University of Thessaly. She leads the AuDi-o-Mentia research team and is affiliated with the Intelligent Systems Laboratory. Her work focuses on assistive technologies for people with neurodegenerative diseases, smart environments, cyber-physical systems, and healthcare in consumer electronics. Education: BSc in Computer Science & Biomedical Informatics (2018), University of Thessaly Current Role: Ph.D. candidate since 2018, researching 'Ambient Assistive Systems in Smart Environments' Her research interests include developing smart systems for elderly care, gas leakage detection via TinyML, logistics optimization, and security in healthcare wearables. Her work is funded by the EPICS in IEEE program and has led to innovations like home assistive systems for dementia patients. Awards: 1st place in IEEE Mind the Gap 2017 (dementia assistive system) Finalist in Microsoft Imagine Cup 2017 3rd place at ECESCON 2017 (best business idea) Advising & Grants: As team leader of AuDi-o-Mentia, she coordinates interdisciplinary research on memory aids for neurodegenerative diseases. Her projects integrate acoustic-based systems, tinyML, and sensor networks into assistive technologies. Labs & Teams: Member of the Intelligent Systems Laboratory and founder of the AuDi-o-Mentia team, focusing on dementia care technologies.

• Robotics
• Multi-robot Systems
• Control Systems
• +۴ مورد دیگر

Lorenzo Sabattini is an Associate Professor at the Department of Engineering Sciences and Methods (DISMI) at the University of Modena and Reggio Emilia, Italy. His research focuses on multi-robot systems, decentralized estimation/control, and human-robot interaction. He holds a Ph.D. in Control Systems and Operational Research from the University of Bologna (2012), and has been a Visiting Researcher at the University of Maryland (2010). He served as a founding co-chair of the IEEE RAS Technical Committee on Multi-Robot Systems from 2014–2021. His work has been recognized with awards such as the Fabrizio Flacco Young Author Best Paper Award (2018) and the IEEE RAS Most Active Technical Committee Award (2018). Research interests include multi-robot coordination, coverage control, human-robot collaboration, and Industry 5.0 applications. Notable projects involve digital twins for operator well-being, AGV fleet management, and socially acceptable robotics. He has organized workshops at IEEE ICRA, RSS, and ERF, and serves as an Associate Editor for International Journal of Robotics Research (2023–2024) and IEEE Robotics and Automation Letters (2015–2018). Publications span topics like connectivity maintenance in robot networks, multimodal stress assessment in HRC, and resilient multi-robot systems. He actively collaborates on industrial automation projects, such as the INCLUSIVE system for adaptive HMIs and autonomous depalletizing systems. His research bridges theory and practice, addressing challenges in logistics, manufacturing, and human-centric robotics.

Yanyu Wangمشاهده پروفایل

استادیار

• Artificial Intelligence in Construction
• Infrastructure Management
• Sustainable Communities
• +۳ مورد دیگر

Yanyu Wang, Ph.D. , is an Assistant Professor at the Bert S. Turner Department of Construction Management within Louisiana State University's College of Engineering . Specializing in artificial intelligence and infrastructure management, Dr. Wang's research addresses data sharing practices, sustainable construction, and vision-based safety systems. Contact: yanyuwang@lsu.edu Ph.D., Civil and Environmental Engineering (Infrastructure Management), Carnegie Mellon University, 2023 M.S. & B.S., Construction Management, Huazhong University of Science and Technology, 2018 & 2016 B.S., Law (Minor), Wuhan University, 2016 Dr. Wang's research spans advanced AI applications in construction, including data sharing frameworks , reinforcement learning for construction automation , and vision-based safety systems that leverage crowdsourced labeling. Current work explores sustainable infrastructure operation and modular construction optimization. Publications reveal expertise in data governance for civil infrastructure, predictive modeling of human errors in air traffic control systems, and AI-driven construction safety . Key themes include digital transformation, interdisciplinary collaboration, and sustainable urban development.

Khaled Salehمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Autonomous Systems
• +۴ مورد دیگر

Dr. Khaled Saleh is a Lecturer in Computing and Information Technology at the University of Newcastle's School of Information and Physical Sciences. He holds a PhD in Computer Science from Deakin University (2019). Prior to his current role, he served as a Postdoc Research Fellow at the University of Technology Sydney (2020–2022) and a Research Fellow at Deakin University (2018–2019). Research Focus: AI, Machine Learning, Autonomous Systems, Computer Vision, and Intelligent Transportation Systems Grants: Secured over $683K in funding, including projects on traffic safety and AI in education Editorial Roles: Topic Editor at MDPI Sensors and reviewer for top journals/conferences (e.g., IEEE T-ITS, ICRA) Research interests span autonomous systems, traffic safety, and AI-driven solutions for transportation and education. He has published over 30 papers in top venues and actively contributes to grants and industry collaborations. Teaching: Coordinates courses like Programming Languages and Business Intelligence.

Suchendra M. Bhandarkarمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۴ مورد دیگر

Suchendra M. Bhandarkar is a Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. He holds courtesy faculty positions in the College of Engineering and the School of Environmental, Civil, Agricultural, and Mechanical Engineering. His research focuses on Artificial Intelligence, Computer Vision, Pattern Recognition, and Computational Biology. He has advised PhD students such as Anirban Mukhopadhyay and has been actively involved in interdisciplinary projects spanning robotics, coral reef mapping, and food safety automation. Education: Ph.D. and M.S. in Computer Engineering from Syracuse University (1985-1989), and B.S. in Electrical and Electronics Engineering from Indian Institute of Technology Bombay (1983). His funded projects include advanced GeoAI terrain analytics, underwater robotics, and AI-driven food safety systems. Notable grants include support from USDA, NIH, and the US Army. Research highlights include 3D coral reef mapping via SLAM, deep learning for food safety, and zero-shot learning algorithms. Key collaborations involve the USDA Agricultural Research Service, Sony Corporation, and international networks like CapsNet for medical imaging. His labs and teams focus on applying AI to environmental, biomedical, and agricultural challenges.

Dietrich Paulusمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Medical Imaging
• +۲ مورد دیگر

Prof. Dr.-Ing. Dietrich Paulus heads the Active Vision Group at the University of Koblenz's Institute of Computational Visualistics. His research spans computer vision, robotic perception, medical image analysis, and 3D reconstruction techniques. Current projects include automated spine modeling for medical applications, hyperspectral imaging for autonomous driving, agricultural robotics, and sensor calibration methods. Recent work demonstrates strong applications in medical imaging with projects on 3D vertebrae measurement, spinal ligament detection, and cochlear image segmentation. The group also develops educational robotics frameworks and participates in robotic challenges as pedagogical tools. Methodological advances include generative adversarial networks for synthetic data generation, point-cloud change detection, and online calibration of LIDAR systems.

Jianfei Caiمشاهده پروفایل

استاد

• Computer Vision
• Multimedia
• Visual Computing
• +۵ مورد دیگر

Roles and Affiliations: Jianfei Cai is a Professor at Monash University's Faculty of IT, leading the Department of Data Science & AI. Previously, he held roles including Head of the Data Science & AI Department, and was a Full Professor at NTU's School of Computer Science and Engineering. He is a co-recipient of multiple paper awards and an IEEE Fellow. Currently serves as General Chair for ACM Multimedia 2024 and on editorial boards of TPAMI and IJCV. Education: PhD in Electrical Engineering, University of Missouri Columbia (2002) MEng in Electrical Engineering, Southeast University (1997) BEng in Electrical Engineering, Nanjing University of Aeronautics and Astronautics (1994) Research Interests: Focuses on computer vision, deep learning, vision-language systems, 3D imaging, and medical imaging applications. His work spans generative AI, neural scene representation (e.g., Gaussian Splatting), and open-vocabulary semantic segmentation. He has published over 380 papers and led projects in trustworthy AI and 3D vision. Key Contributions: Developed MVSplat (3D Gaussian Splatting from sparse views) Pioneered open-vocabulary object detection via scene graph discovery Advanced diffusion models for multi-sensor fusion and panorama synthesis Awards and Recognition: Monash FIT Dean's Researcher of the Year Award IEEE Fellow ACCV/ICCM/IEEE ICIP paper awards Recipient of Google/Adobe research gifts Grants and Labs: Leading projects on trustworthy generative AI, 3D Gaussian splatting compression, and safe AI Former director of NTU's Data Science & AI Research Center

Samin Yasarمشاهده پروفایل

پژوهشگر

• Robotics
• Human-Robot Interaction
• Representation Learning
• +۳ مورد دیگر

Samin Yasar is a Postdoctoral Research Associate in Computer Science at Rice University, working under Professors Lydia Kavraki, Vaibhav Unhelkar, and Vicente Ordonez. His research focuses on enabling robots to collaborate effectively with humans, emphasizing human intent understanding, motion prediction, and closed-loop decision-making systems. He holds a PhD in Computer Engineering from the University of Virginia (2024) and a BSc in Electrical and Electronics Engineering from BRAC University (2015). Research Interests: Robotics Human-Robot Interaction Representation Learning Generative Models Machine Learning Awards: UVA Endowed Fellow (2023-2024) UVA Distinguished Mentor Award (2023, 2024) HRI Pioneer Award (2022) Double Hoo Award (University of Virginia, 2022) DSN Travel Grant (2020) ISMR Travel Grant (2019) Experience: Research Intern at Microsoft (2024) Graduate Research Assistant, Collaborative Robotics Lab (University of Virginia, 2020-2024) Graduate Research Assistant, Dependable Systems and Analytics Lab (University of Virginia, 2017-2019) Assistant Manager at MGH Group (Bangladesh, 2015-2017) Grants & Funding: Recipient of multiple travel grants for top-tier robotics and AI conferences Contributor to projects funded by NSF and industry collaborations Labs/Teams: Collaborative Robotics Lab (Rice University) Dependable Systems and Analytics Lab (University of Virginia)

• Computer Vision
• Image Analysis
• Machine Learning
• +۵ مورد دیگر

Martin Kampel is a Senior Scientist at the Computer Vision department of Technische Universität Wien (E193-01), focusing on interdisciplinary research at the intersection of Visual Computing , Human-Centered Technology , and AI Ethics . His work spans 3D Vision , Image Sequence Analysis , Computer Vision for Cultural Heritage , Ambient Assisted Living , and Visual Surveillance . Current roles: Senior Scientist, Principal Investigator in EU and national projects (e.g., Blindsight, visuAAL, KIIS) Contact: martin.kampel@tuwien.ac.at Research Interests Kampel’s research explores: 3D Acquisition & Reconstruction : Camera calibration, hand-eye tracking, and range data processing. Human Behavior Analysis : Motion tracking, multi-camera networks, and action recognition from 4D point clouds. Assistive Technologies : Depth sensor applications for dementia care, privacy-aware AAL systems, and smart toilet configurations. AI Ethics : Fairness, bias, and transparency in criminal justice systems and algorithmic governance of care. Recent Projects & Publications His work includes: Blindsight : WiFi-based person localization and multimodal data synthesis for behavioral classification. visuAAL : Developing trustworthy AI for dementia care and privacy-preserving depth sensor systems. KIIS : Domain-adaptive data synthesis for supermarket product recognition. Notable publications cover 3D human pose estimation , WiFi CSI analysis , trimodal datasets , and ethical AI frameworks .

Jorge Diasمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Artificial Perception
• +۴ مورد دیگر

Jorge Dias is a Full Professor at Khalifa University (Abu Dhabi, UAE) specializing in Robotics and Artificial Intelligence, with a focus on Artificial Perception. He coordinates the Khalifa University Center of Autonomous Robotic Systems (KUCARS) and has contributed to robotics advancements since 1984. Habilitation/Doctor of Science (2011) & PhD (1994) in Electrical Engineering from University of Coimbra, Portugal His research spans Robotics for Sustainability , Computer Vision , and Social Robots , with over 300 publications. Recent work emphasizes autonomous navigation, underwater robotics, and neuromorphic computing. He has led international projects like the ECOBOTICS.sea RISE Marie Curie initiative and coordinated European consortia for elderly-inclusive robotics ("Social Robot", "GrowMeUP"). Current teaching includes courses in Algorithmic Robotics , Autonomous Systems , and Robotic Perception .

Estrid Heمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Data Mining
• Deep Learning
• +۳ مورد دیگر

Dr. Estrid He is a Senior Lecturer at RMIT University's School of Computing Technologies. She obtained her PhD from the University of Melbourne in 2020, where she subsequently served as a postdoctoral research fellow. Her research bridges natural language processing, data mining, and deep learning optimization, with applications spanning healthcare, communications, and algorithmic fairness. Research Focus: Core NLP techniques for knowledge extraction from complex texts (patents, medical records) Enhancing security/efficiency of deep learning models in resource-constrained environments Multimodal learning integrating text, sensor data, and biomedical signals Fairness-aware AI systems for computer vision and graph neural networks Her publication portfolio demonstrates strong cross-disciplinary collaboration, with recent work in: Wireless communications (terahertz signal processing, 6G hardware) Biomedical applications (brain disorder prediction, clinical NLP) Generative models for sensor data and multimodal content Algorithmic fairness in computer vision and graph networks She actively supervises graduate research, with current projects including: Graph learning for brain disorder prediction Multimodal health data mining Privacy-preserving AI for urban sensing Integration of spiking neural networks with LLMs

• Machine Learning
• Evolutionary Computation
• Multiobjective Optimization
• +۱۰ مورد دیگر

Professor Xiaodong Li is a faculty member at RMIT University's School of Computing Technologies, serving as Assistant Associate Dean for Data Science & Artificial Intelligence. He holds a Ph.D. in Artificial Intelligence from the University of Otago, New Zealand. His research focuses on machine learning, evolutionary computation, swarm intelligence, and optimization techniques with applications in blockchain security, renewable energy, and logistics. He has received prestigious awards including the 2013 ACM SIGEVO Impact Award and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and is an IEEE Fellow. His academic contributions include editorial roles at IEEE Transactions on Evolutionary Computation and leadership in IEEE Task Forces on Swarm Intelligence and Multi-modal Optimization. Current research interests span automated code generation, quantum AI-driven logistics, and anomaly detection. Supervision projects highlight interdisciplinary applications in AI ethics, solar energy monitoring, and fraud detection. Education: Ph.D. in Artificial Intelligence, University of Otago, New Zealand Key Roles: IEEE Fellow, ARC College of Experts (2023–2025) Publications: Over 280 peer-reviewed articles, including works on niching methods and evolutionary algorithms. Research trends show strong emphasis on hybrid optimization techniques, blockchain security, and AI-driven solutions for sustainability challenges. Recent articles explore dynamic environments, quantum rerouting strategies, and explainable machine learning systems. Awards: ACM SIGEVO Impact Award, IEEE Fellow, ARC College Membership Grants/Projects: Multiple industry-collaborative grants in smart logistics and energy systems. He leads the Data Science & AI team at RMIT, fostering innovation in large-scale optimization and metaheuristics. Active in international conferences like GECCO and IEEE CEC, he promotes open-source benchmark datasets for algorithm testing.