جست‌وجوی استاد و پژوهشگر

Kari Tammiمشاهده پروفایل

استاد

• Mechatronics
• Autonomous Vehicles
• Electric Vehicles
• +۷ مورد دیگر

Kari Tammi serves as Professor and Dean of Aalto University's School of Engineering since 2015, concurrently holding the position of Chief Engineer Counselor at Finland's Administrative Supreme Court. His career spans industrial research leadership at VTT Technical Research Centre (2000-2015), postdoctoral work at North Carolina State University (2007-2008), and foundational research at CERN (1997-2000). His academic credentials include: MSc, Helsinki University of Technology, 1999 LicSc, Helsinki University of Technology, 2003 DSc, Helsinki University of Technology, 2007 Teacher’s Pedagogical Qualification, Häme University of Applied Sciences, 2017 Research Focus: Tammi pioneers in Mechatronics , Autonomous/Electric Vehicle Systems , and Energy Efficiency Optimization , with specialized expertise in Dynamics , Control Systems , and Digital Twin Applications . His work bridges theoretical innovation with industrial deployment across maritime, automotive, and manufacturing sectors. Publication Trends: Recent output (2024-2025) demonstrates concentrated advancement in industrial digital twins for crane operations, winter-condition autonomous perception, and marine energy systems. Key patterns include GPU-free real-time processing, semantic-enhanced metaverse architectures, and snow-robust sensor fusion techniques. Professional Leadership: As former VTT Team Leader and current Engineering Dean, Tammi directs cross-disciplinary research initiatives connecting academic theory with industrial practice, particularly in sustainable transportation and smart manufacturing ecosystems.

Jean-Claude Moissinacمشاهده پروفایل

مدرس

• Semantic Web Technologies
• Knowledge Graphs
• Machine Learning
• +۳ مورد دیگر

Jean-Claude Moissinac is a Lecturer at Télécom Paris, part of the Institut Mines-Télécom, affiliated with the Image, Data, Signal department and Multimedia research group. He serves as the educational manager for the CPD master's degree program (Digital Project Designer). His research focuses on semantic technologies applied to multimedia systems and knowledge representation. His primary research areas include: Semantic representations of knowledge and knowledge graphs Machine learning techniques for graph-based data analysis Advanced multimedia document processing (production, transport, representation) Semantic web technologies across diverse application contexts His publications demonstrate consistent focus on semantic technologies, knowledge graphs, and multimedia systems, with recent work emphasizing cultural heritage applications and graph-based machine learning techniques. Earlier publications focused on multimedia systems optimization and SVG standards development. He has led multiple research projects including: ILOT Project : Intelligent Learning Object for Teaching SOA2M : Multimodal interface research Data&Musée : Semantic applications for cultural heritage data SemBib : Semantic representation of bibliographic data GeoRacing : Interactive mobile TV services for sporting events He contributes to standardization efforts as co-author of the SVG web standard and participates in academic committees including SVG Open, IEEE International Symposium on Multimedia, and project evaluations for the French National Research Agency.

• Semantic Web
• Knowledge Representation and Reasoning
• Artificial Intelligence
• +۵ مورد دیگر

Floriano Scioscia is an Associate Professor at the Polytechnic University of Bari, Italy, specializing in Information Processing Systems (SSD ING-INF/05). His research bridges Semantic Web technologies, Knowledge Representation, and Artificial Intelligence to develop innovative solutions for Internet of Things, Blockchain, and smart city infrastructures. His primary research domains include Semantic Web, Knowledge Representation and Reasoning, Internet of Things, Blockchain, Edge Computing, and Artificial Intelligence. He applies these to solve critical problems in smart mobility (e.g., blockchain-enhanced ridesharing), last-mile logistics optimization, energy infrastructure management, and healthcare decision support. His work consistently focuses on creating lightweight, scalable frameworks like Tiny-ME for resource-constrained environments and semantic-enhanced platforms for real-world system integration. Analysis of his 2023-2025 publications reveals a dominant trend toward edge-centric semantic technologies. Key themes include OWL reasoning on constrained devices (Tiny-ME Wasm), blockchain integration for transparent service platforms (RideMATCHain), and AI-driven optimization for logistics/smart cities. His work demonstrates exceptional cross-domain applicability, with consistent emphasis on explainability, scalability, and practical implementation in transportation, energy, and healthcare infrastructure. The publications show strong synergy between theoretical knowledge representation advances and deployable system architectures.

• Building automation and control systems
• Building energy performance modeling
• Data-driven energy modeling
• +۷ مورد دیگر

Marco Savino Piscitelli is a Fixed-Term Researcher at the Polytechnic University of Turin within the Department of Energy (DENERG) and member of the Interdepartmental Center SmartData@PoliTO. He actively contributes to the BAEDA Lab (Building Automation and Energy Data Analytics) since 2021. His academic activities span two schools: College of Architecture and Design (as member), and College of Electrical and Energy Engineering (as invited member). Research Interests focus on: Adaptive control strategies for smart buildings Data-driven energy management systems Knowledge graphs and LLMs for building interoperability Fault detection in HVAC and district heating systems Energy benchmarking frameworks Grid-interactive efficient buildings Commercial research projects with generative AI applications Recent Publications (2023-2025) demonstrate expertise in: AI-enhanced energy planning Brick-compliant semantic models Deep learning for power demand forecasting Multi-zone HVAC optimization Experimental building diagnostics Collective self-consumption strategies Teaching Roles include: Lecturer for Energy Management and Automation in Buildings (2023-2025) Collaborator for Energy Transition and Low-Carbon Architecture (2021-2025) Lecturer for Models and Scenarios for Energy Planning (2024) Titolare (lead instructor) for Building Physics and Air Conditioning (2024) Research Projects : 2024-2025 : Scientific Manager for "Data-Driven Performance Analysis through Generative AI" 2023-2024 : Scientific Head for "Advanced data-driven smart reporting systems" Labs & Teams : BAEDA Lab - Building Automation and Energy Data Analytics SmartData@PoliTO - Big Data and Data Science Laboratory Collaboration with CityLearn v2 development team

Yi Dingمشاهده پروفایل

استادیار

• Multimodal Machine Learning
• Human-Computer Interaction
• AI Ethics
• +۵ مورد دیگر

Yi Ding is a tenure-track Assistant Professor of Computer Science at Georgia State University. He leads a research group focusing on multimodal machine learning and human-computer interaction, particularly in understanding human behavior through technology. His work emphasizes applications in healthcare, education, and community benefits. Before academia, he had extensive industry experience as an engineer and researcher. Education B.S. in Computer Science and Mathematics, University of Massachusetts Amherst (2011) M.S. in Computer Science, University of California Santa Barbara (2021) Ph.D. in Computer Science, University of California Santa Barbara (2022) Research Interests Dr. Ding explores how technologies can interpret human behavior and social signals to improve health, education, and community systems. His work addresses challenges in multimodal data fusion, label noise robustness, and ethical AI design. Key areas include: Multimodal machine learning frameworks Human-centric AI ethics Data augmentation strategies for noisy labels Gesture-based interfaces Bio-signal authentication systems Professional Activity His research bridges academia and industry, with contributions to VR/AR systems, health monitoring technologies, and adaptive user interfaces. He collaborates with interdisciplinary teams to address real-world challenges through innovative machine learning approaches.

Dylan Loseyمشاهده پروفایل

دانشیار

• Human-Robot Interaction
• Machine Learning
• Control Theory
• +۳ مورد دیگر

Dylan Losey is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech , part of the College of Engineering . He leads the Collaborative Robotics Lab (Collab) , focusing on developing learning and control algorithms for robots collaborating with humans. Education : Ph.D. & M.S. in Mechanical Engineering from Rice University (2018 & 2016), B.E. in Mechanical Engineering from Vanderbilt University (2014). Formerly a Postdoctoral Scholar at Stanford University (2019–2020) and Visiting Scholar at UC Berkeley (2017). Research Interests include Human-Robot Interaction , Machine Learning , and Control Theory , with applications in assistive robotics, healthcare, and user-centered design. His lab creates robots that personalize behavior to adapt to human needs, such as the Kiri-Spoon for robot-assisted feeding. Awards include the NSF CAREER Award (2024), Best Application Paper (IEEE Transactions on Haptics, 2024), and Outstanding New Assistant Professor (Virginia Tech, 2023). Advising & Grants : Advises students like Maya Keely and Heramb Nemlekar. His NSF CAREER grant supports research on bidirectional human-robot communication. Collaborates with institutions like The Virginia Home (Richmond) for user-centric design. Labs & Teams : Directs the Collaborative Robotics Lab , emphasizing interdisciplinary work with partners like Cornell University. Recent projects include assistive robotics for mobility-impaired users and haptic display systems.

• Computer Vision
• Generative Modeling
• Multimodal Foundation Models
• +۴ مورد دیگر

Jiankang Deng is a Lecturer in the Department of Computing at Imperial College London, within the Faculty of Engineering. His research focuses on multi-modality foundation models, generative modeling of physical entities, and computer vision applications. He leads projects such as InsightFace.ai for facial analysis and synthesis. Previously, he earned his PhD (2020) from Imperial College under Prof. Stefanos Zafeiriou, focusing on deep face analysis and modeling. Education: PhD in Computing (2016-2020) from Imperial College London. Research interests include photorealistic facial reconstruction, generative adversarial networks, and multimodal learning. His work bridges computer vision with practical technologies for societal impact. Publications reflect expertise in 3D reconstruction, diffusion models, and cross-modal systems. Notable areas include hand motion analysis, face reflectance estimation, and tactile robotics. His work emphasizes scalability and real-world applicability. No awards listed explicitly, but his research contributions are evident through active publications and industry collaborations. Advising and lab work involve the IBUG research group, focusing on facial recognition and generative AI systems. Labs/Teams: Part of the Imaging and Biomedical Engineering Group (IBUG), collaborating on projects like FaceMAE for privacy-preserving recognition and MogFace for advanced face detection systems.

Vladimir Kulyukinمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Vision
• Robotics
• +۳ مورد دیگر

Vladimir Kulyukin is an Associate Professor in the Department of Computer Science at Utah State University. His research spans theoretical computer science, artificial intelligence, and robotics, with notable contributions to computability theory, bio-inspired computing, and assistive technologies for visually impaired individuals. He has pioneered work in non-invasive electronic beehive monitoring using machine learning, computer vision, and sensor networks, addressing challenges in precision agriculture and environmental science. Key research interests include formal methods in computability, neural network capabilities, and the intersection of robotics with human-centric applications. His work in assistive technologies includes innovations in blind navigation systems, mobile barcode scanning, and verbal route guidance for independent shopping. Recent articles highlight advancements in computability theory (e.g., universality theorems, primitive recursive functions), applications of YOLO networks for bee traffic analysis, and fusion of multimodal datasets for hive health monitoring. His interdisciplinary approach bridges theoretical foundations with practical implementations in agriculture, education, and accessibility. Notable projects include the development of BeePIV for bee traffic measurement, mobile PIV tools for fluid mechanics education, and the Shoptalk system for blind grocery shopping. These contributions underscore his commitment to both foundational research and real-world impact in STEM fields.

• Computer Vision
• Image and Video Processing
• Machine Learning
• +۵ مورد دیگر

Alexander C. Loui is a Professor in the Department of Computer Engineering at Rochester Institute of Technology (RIT), leading the Multimodal Analysis and Perception (MAP) Lab. He holds a Ph.D., M.A.Sc., and B.A.Sc. in Electrical Engineering from the University of Toronto, Canada. His academic roles include Senior Area Editor of IEEE Transactions on Image Processing and Senior Editor of SPIE/IS&T Journal of Electronic Imaging. Loui specializes in computer vision, image/video processing, machine learning, and multimedia systems, with over 100 peer-reviewed publications and 95+ patents. Research interests span video summarization, medical image segmentation, and AI-driven multimedia applications. He has led technical teams at Kodak Alaris, Kodak Research Labs, and Bell Communications Research. Awards include IEEE Region 1 Technological Innovation Award and Fellowships from IEEE and SPIE. His work bridges academia and industry, emphasizing practical algorithm development and patent innovations. Teaching responsibilities include Digital Signal Processing and Multidisciplinary Senior Design courses. Key contributions: MAP Lab leadership, over 95 patents (e.g., video object segmentation, salient foreground detection), and editorial roles in top journals. His research trends focus on label-efficient learning, trustworthy AI, and predictive content curation systems.

Sanja Fidlerمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۵ مورد دیگر

Sanja Fidler is an Associate Professor at the University of Toronto (Department of Computer Science) within the Mathematical and Computational Sciences School. She is also a co-founder and affiliated faculty at the Vector Institute and serves as VP of AI Research at NVIDIA. Her research focuses on the intersection of Computer Vision, Machine Learning, and Graphics, with emphasis on 3D reconstruction, generative models, and interactive methods. She previously held a Research Assistant Professor position at the Toyota Technological Institute at Chicago. Research Interests: Dr. Fidler specializes in Computer Vision and Machine Learning, particularly in areas such as 3D vision, 3D reconstruction/synthesis, object detection, and interactive scene understanding. Her work bridges graphics and vision, addressing challenges in geometric representations, generative models, and real-world applications like medical imaging and autonomous systems. Awards: She has received notable awards including the Canada CIFAR AI Chair (2018), Connaught Innovation Award (2020), NVIDIA Pioneer of AI Award (2016), and multiple outstanding reviewer distinctions at top conferences. Her teaching excellence was recognized with the Professor of the Year award by UofT's CS Student Union in 2014/2015. Collaborations & Labs: Leading the NVIDIA Toronto AI Research Lab and contributing to initiatives at the Vector Institute, her work integrates academic and industrial research. She co-organized workshops on topics like 'Geometry Meets Deep Learning' and '3D Indoor Scene Understanding' at major conferences.

• Smart Grid and Smart Energy
• Demand Side Management
• Virtual Power Plants
• +۳ مورد دیگر

Hongjian Sun is Professor and Director of Research in Smart Grid at Durham University's Department of Engineering, where he leads the Durham Smart Grid Laboratory. He earned his Ph.D. from the University of Edinburgh and held postdoctoral positions at King's College London and Princeton University. Sun's research encompasses: Demand response and virtual power plant optimization Electric vehicle charging infrastructure management Smart grid communications and IoT integration Renewable energy-grid interface solutions His publications demonstrate consistent focus on energy system digitalization, with recent advancements in digital twins for virtual power plants, reinforcement learning for grid management, and integrated sensing-communication systems. Research integrates theoretical frameworks with practical implementations in microgrid optimization and EV-grid integration. Sun has secured significant research funding from EU H2020, EPSRC, and UK government programs for projects including TESTBED2 (€1.4M), CHEDDAR (£3M), and VPP-WARD (£1.84M). These initiatives develop solutions for grid decarbonization and renewable integration. Honors include Fellowship of Durham Energy Institute and Higher Education Academy. Sun serves on editorial boards for IET Smart Grid and IEEE Transactions on Industrial Informatics, and organizes leading conferences in smart grid communications.

Bart Dhoedtمشاهده پروفایل

استاد

• Distributed Machine Learning
• Edge Computing
• Neuromorphic Computing
• +۲ مورد دیگر

Bart Dhoedt is a Professor at Ghent University's Department of Information Technology, affiliated with the Internet Technology and Data Science Lab (IDLab). He teaches courses on algorithms, advanced programming, software development, and distributed systems. His research bridges distributed machine learning, edge computing, and hardware-efficient AI. Research interests focus on Distributed Machine Learning (parallel processing across systems), Edge Computing (decentralized data processing), Neuromorphic Computing (brain-inspired hardware), Sensor Fusion (multimodal data integration), and Representation Learning (efficient feature extraction). Recent publications emphasize active inference, robotic navigation, and object-centric AI. Publications (2017-present) show strong trends in robotic autonomy (navigation, manipulation), active inference (Bayesian modeling), and multimodal world models , with applications in industrial automation and cognitive systems. No awards or grants are documented. He leads research at IDLab, collaborating on embedded AI and distributed systems. No advised students or external affiliations are mentioned.

Inge Hermanrudمشاهده پروفایل

دانشیار

• Innovation in Public Sector Services
• Digital Guidance Systems
• Elderly Care Technology
• +۶ مورد دیگر

Inge Hermanrud is an Associate Professor at the University of Inland Norway, affiliated with the Department of Organisation, Leadership and Management within the Inland School of Business and Social Sciences. She holds a PhD from Nord University (2013) focusing on competence networks in public agencies and ICT use in learning/knowledge sharing. Her work combines academic research with practical insights into public service innovation, digital transformation, and organizational learning. Education: PhD in Organizational Studies, Nord University (2013) Master's in Planning, University of Tromsø (1999) Research Focus: Innovation in public sector services, especially eldercare and welfare Digital guidance systems in NAV (Norwegian Labour and Welfare Administration) IoT applications in social services Lean methodologies for process improvement Knowledge dissemination in distributed organizations Key Projects: VIT - Guidance and Information Access in NAV Smart service ecosystems in elderly care Lean mind-set implementation in healthcare Her work bridges theory and practice, addressing challenges in digital transformation, organizational collaboration, and service delivery optimization. She has contributed to policy-relevant research on public-sector innovation and digital service design.

Abhinav Guptaمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Abhinav Gupta is a Professor at Carnegie Mellon University's Robotics Institute, specializing in Computer Vision, Machine Learning, and Robotics. His research focuses on scene understanding, human-centric vision, and language-vision interactions. He leads projects like NEIL and has developed tactile skins like ReSkin. His work emphasizes self-supervised learning, robot manipulation, and visual learning. Education & Positions: PhD from CMU, postdoc at CMU, former affiliations with UMD and UPenn. Current roles include teaching courses like Visual Learning and Recognition. Research Interests: 3D scene representation, affordances, visual-language links, and robotics applications. His lab explores topics like tactile sensing, robot exploration, and generalizable manipulation through projects such as Track2act and Gen2act. Awards & Recognition: Includes the JK Aggarwal Prize (2020), ONR Young Investigator Award (2018), and Okawa Grant. His work on NEIL earned CNN's Top 10 Ideas of 2013. Students & Collaborations: Advised over 20 PhD/Master's students and postdocs. Notable former advisees include David Fouhey, Xiaolong Wang, and Lerrel Pinto. Collaborations with institutions like Meta AI and UIUC. Key Projects: NEIL (Never Ending Image Learner), Curious Robot, and ReSkin tactile skins. His research bridges theory and practice, with applications in robotics, computer vision, and AI systems.

Tomas Lozano-Perezمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Tomas Lozano-Perez is the School of Engineering Professor in Teaching Excellence at MIT, affiliated with CSAIL. He holds SB, SM, and PhD degrees in Computer Science from MIT. His research focuses on robotics, computer vision, machine learning, medical imaging, and computational chemistry. He is a Fellow of AAAI, ACM, and IEEE, and recipient of major awards including the IEEE Robotics Pioneer Award (2011) and the 2021 IEEE Robotics and Automation Award. Education: PhD in Computer Science, MIT (1980) SM in Computer Science, MIT (1976) SB in Computer Science, MIT (1973) Research Interests: Robotics: motion planning, manipulation, and decision-theoretic planning Computer Vision: object recognition via interpretation trees Machine Learning: multiple-instance learning and diffusion models Medical Imaging: computer-assisted surgery Computational Chemistry: drug activity prediction and protein structure analysis Key Contributions: Developed configuration-space approach for robot motion planning Pioneered integration of task, motion, and decision-theoretic planning Advances in embodied intelligence and human-robot interaction Projects & Labs: Lead of the Learning and Intelligent Systems research group at CSAIL Co-lead of the Task and Motion Planning for Autonomous Robots project Member of the Embodied Intelligence Community of Research