جست‌وجوی استاد و پژوهشگر

• Urban Planning
• Transportation Engineering
• Environmental Monitoring
• +۳ مورد دیگر

Alonso Espinosa Mireles de Villafranca is a Postdoctoral Research Fellow at the Department of Civil Engineering within the Faculty of Built Environment at Tampere University. His work focuses on urban sustainability, transportation systems, environmental monitoring, and network science. He holds a strong background in analyzing public transport systems, crowd-sourced data analysis, and the impact of climate factors on urban environments. His research interests include optimizing multi-modal transport networks, improving urban environmental quality through citizen science, and addressing summertime heat exposure through multidisciplinary approaches. He has conducted studies on traffic management for altruistic vehicles, indoor environmental monitoring, and mobility patterns during special events like Helsinki's Vappu celebrations. Key contributions include developing evaluation frameworks for public transport connectivity and exploring the role of network morphology in traffic dynamics. His work often integrates data science techniques with urban planning challenges, emphasizing real-world applications in Finnish urban contexts.

Patrick Holthausمشاهده پروفایل

پژوهشگر ارشد

• Social Robotics
• Human-Robot Interaction
• Assistive Robotics
• +۴ مورد دیگر

Dr. Patrick Holthaus is a Senior Research Fellow at the University of Hertfordshire's Robotics Research Group , affiliated with the School of Physics, Engineering and Computer Science . He specializes in assistive and social robotics, focusing on nonverbal communication, trust dynamics, and systems integration. His work integrates fundamental research with real-world applications like the Robot House and Kaspar robot for children with autism. Teaching: Module leader for Data Structures and Algorithms and co-leader of Advanced Research Topics in Computer Science . Supervises multiple PhD students and serves as external examiner for postgraduate programs. Research Leadership: Principal Investigator (PI) for projects such as the Robot House Immersive Showcase and Enhancing Research Cultures grants. Collaborates internationally on initiatives like the SWAG wearable assistive garments and Hospital@Home virtual care system. Key Projects: Co-Investigator (CoI) on Horizon Europe’s SWAG project and the Dinwoodie-funded Hospital@Home initiative. Previously led EPSRC’s EMERGENCE network and the Kaspar Explains study. Labs & Teams: Manages the Robot House facility, a unique environment for HRI studies. Active in the Assuring Autonomy International Programme (AAIP).

Marvin Chancánمشاهده پروفایل

پژوهشگر

• Robotics
• Artificial Intelligence
• Computer Vision
• +۶ مورد دیگر

Marvin Chancán is a Postdoctoral Associate at Yale University's Intelligent Autonomy Lab, led by Ian Abraham. His research focuses on bio-inspired sensing and control for robotic systems, combining deep learning, computer vision, and neuroscience to develop high-performance neural architectures for autonomous navigation. He holds a Ph.D. in Electrical Engineering and Robotics from Queensland University of Technology (QUT, 2022), an M.S. in Applied Control from PUC-Rio (2012), and a B.S. in Mechatronics Engineering from Universidad Nacional de Ingeniería (UNI, 2009). His work bridges biological neural circuits (e.g., insect and mammalian brains) with robotic systems, achieving state-of-the-art results in motion-and-vision-based localization and navigation. His publications appear in top venues like NeurIPS, ICRA, and RSS. Notable contributions include DeepSeqSLAM, CityLearn, and the MVP framework, emphasizing self-supervised and sample-efficient learning. Chancán has received prestigious awards, including the 2020 QUT HDR High Achiever Award and several scholarships. He has served as a reviewer for journals like Science Robotics and conferences such as IROS. His industry experience spans automation and IT roles across The Americas, Europe, and Oceania. His research aims to advance autonomous systems through biologically inspired algorithms, with a focus on real-world applications in robotics and autonomous vehicles.

Mingi Jeongمشاهده پروفایل

استادیار

• Computer Science
• Robotics
• Artificial Intelligence
• +۷ مورد دیگر

Mingi Jeong is a Ph.D. candidate in Computer Science at Dartmouth College and a member of the Reality and Robotics Lab under Prof. Alberto Quattrini Li. Starting December 2025, he will join Virginia Tech as a tenure-track Assistant Professor in the Aerospace and Ocean Engineering Department. His research focuses on autonomous systems for maritime environments, specifically robust decision-making in unstructured aquatic scenarios. Education: B.S. in Nautical Science (Korea Maritime and Ocean University), M.E. in Maritime Safety Engineering (Korea Maritime and Ocean University), and Ph.D. candidate at Dartmouth College. Key research interests include autonomous navigation for ASVs, obstacle detection/avoidance, multi-robot systems, and environmental monitoring. He has developed the SeePerSea dataset—the first multi-modal in-water object perception dataset—and pioneered intention-aware collision avoidance algorithms. His work combines robotics, AI, and ocean engineering for societal impact in search-and-rescue, cargo transport, and environmental surveillance. Notable awards include the 2025 Neukom Prize and MIT Postdoctoral Fellowship (declined). He has presented at top venues like IROS, RSS, and IEEE conferences globally. His lab emphasizes hands-on fieldwork and interdisciplinary collaboration.

• Computer Vision
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Sridharan Sridha is a Professor at the University of Queensland, specializing in advanced AI and computer vision research. His work spans neural networks, robotics, medical informatics, and surveillance systems. He collaborates extensively with institutions like the University of Queensland’s School of Information Technology and Electrical Engineering. Key research focuses include adversarial machine learning, multimodal fusion, and domain adaptation. His contributions to aerial-ground person re-identification (AG-ReID), LiDAR-based place recognition, and medical signal analysis have been widely recognized. Recent projects emphasize self-supervised learning, transformer-based architectures for hyperspectral imaging, and autism severity detection using physics-augmented models. His work bridges theory and practical applications in autonomous systems, healthcare, and robotics.

• Computer Graphics
• 3D Computer Vision
• User Interaction
• +۵ مورد دیگر

Chi-Wing FU, Philip is a Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK). He holds dual roles in research and education, including Associate Editor-in-Chief of IEEE Computer Graphics and Applications. His research focuses on computer graphics, 3D vision, and human-computer interaction, with over 100 publications in top venues like SIGGRAPH, CVPR, and IEEE Visualization. Education: B.Sc. (1st Hons), Computer Science & Engineering, CUHK M.Phil., Computer Science & Engineering, CUHK PhD, Indiana University, Bloomington Research Interests: Dr. Fu's work spans 3D shape generation, computational LEGO design, AR visualization, and robotic interaction. He has pioneered projects like Make-A-Shape (large-scale 3D modeling) and DreamStone (text-driven 3D creation). His team also develops tools for medical data visualization and hand-object pose estimation. Recent Trends in Articles: Recent work emphasizes AI-driven creativity (e.g., LEGO art, text-to-3D systems) and real-time AR applications. His publications often bridge theory (e.g., generative models) with practical systems (e.g., user interfaces for design). Awards: Postgraduate Research Output Award (2023) MSRA Fellowship Nomination (2022) Best Associate Editor (IEEE CG&A) Outstanding Reviewer (ICCV 2021, CCF CAD/CG 2023) Advising & Grants: Supervised over 40 PhD/Master students and postdocs. Active in securing grants for projects like computational LEGO design (with Autodesk), medical AR visualization, and 3D generative AI. Collaborates with industry partners like Adobe and Huawei. Labs & Teams: Leads the Computational Design and Visualization Lab, focusing on 3D systems, robotics, and creative AI. Key projects include the LEGO Sketch Art toolchain and the HandShadowPoser AR system.

Amit K. Roy-Chowdhuryمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robot Autonomy
• +۳ مورد دیگر

Amit K. Roy-Chowdhury is a Professor and UC Presidential Chair at the University of California, Riverside (UCR), where he chairs the Robotics Program and co-directs the UC Riverside AI Research (RAISE) Institute and the Center for Networked Configurable Command, Control, and Communications (NC4). He previously chaired the Department of Electrical and Computer Engineering at UCR. His research focuses on computer vision, machine learning, and robotics, with applications in autonomous systems and AI for science. Education: He holds a PhD in Electrical and Computer Engineering from the University of Maryland, College Park (2000), an MS in Systems Science and Automation from the Indian Institute of Science, Bangalore (1995), and a BEng in Electrical Engineering from Jadavpur University, Kolkata (1993). Research Interests: His work spans foundational areas of computer vision, image processing, and machine learning, with a focus on robot autonomy, statistical signal processing, and AI applications in science. His Vision and Learning Group has published over 250 peer-reviewed papers and leads DoD-funded research in AI and robotics. Recent Research Trends : His articles emphasize AI safety, domain adaptation, 3D scene understanding, and embodied agents. Themes include robustness to occlusion, ethical AI, and cross-modal learning. Awards : IEEE Fellow (2020), NAAI Fellow (2022), IAPR Fellow (2023), UCR Doctoral Mentoring Award (2019), UMD Distinguished Alumni (2020). Grants & Leadership : Leads a DoD Center of Excellence, oversees AI initiatives at RAISE, and collaborates with industry/government on AI, robotics, and medical imaging projects. His work on plant cell tracking and face recognition has gained media attention, including a PBS/National Geographic documentary. Labs & Teams : Directs the Vision and Learning Group (formerly Video Computing Group) and co-leads RAISE and NC4, fostering interdisciplinary research in AI and robotics.

• Turbulence
• Geophysical Magnetic Fields
• Astrophysical Magnetic Fields
• +۳ مورد دیگر

Prof. Daniel P. Lathrop is a Professor of Geology at the University of Maryland, College Park. His research focuses on turbulence, geophysical/astrophysical magnetic fields, and nonlinear dynamics. He leads the Nonlinear Dynamics Laboratory, conducting experiments in liquid sodium models of planetary cores and magnetic dynamo action. Key projects include the Three-Meter Liquid Sodium Spherical Couette Experiment to simulate Earth's outer core dynamics. His work integrates geophysics, fluid dynamics, and advanced instrumentation with applications in environmental monitoring (e.g., methane flux measurement) and UXO detection via UAV-based geophysical systems. Recent collaborations explore quantum computing hardware, stochastic systems, and machine learning for predictive modeling of complex phenomena. Education: Ph.D. in Physics from University of Texas at Austin (1991) Research interests span experimental fluid dynamics, magnetic field amplification, granular material behavior under magnetic fields, and interdisciplinary applications of nonlinear physics. His lab innovates in geophysical sensing technologies, including magnetic gradiometry and multi-modal UAV platforms. Publications highlight advancements in dynamo experiments, magnetic anomaly detection, and novel approaches to computational hardware leveraging stochastic magnetic systems. Awards and recognitions are not explicitly listed in provided materials.

Olov Anderssonمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Olov Andersson is an Assistant Professor and WASP Fellow in AI for Autonomous Systems at KTH Royal Institute of Technology, leading the Division of Robotics, Perception and Learning. His research focuses on Embodied AI for autonomous robots and vehicles, combining advancements in Vision-Language Models (VLM), Large Language Models (LLM), and real-world navigation challenges. Key projects include the DARPA SubT Challenge-winning team CERBERUS and the EU H2020 Heron project for robotic road repair. He supervises multiple PhD students and postdocs, including Timon Homberger, Finn Lukas Busch, and Jesper Eriksson. Research interests emphasize full-stack autonomy in dynamic environments, including planning, mapping, and navigation. Notable contributions include the OneMap real-time open-vocabulary mapping system and self-supervised scene flow methods like Seflow. He has been recognized for technical leadership in autonomous systems through awards like the WASP Fellowship. Professional activities include co-chairing the 2024 IROS workshop on robot perception in dynamic environments and advising the Swedish Prime Minister’s AI initiative. Teaching roles span multiple graduate courses in machine learning, robotics, and systems engineering at KTH.

Miguel Serras Vascoمشاهده پروفایل

پژوهشگر

• Multimodal Perception
• Reinforcement Learning
• Representation Learning
• +۴ مورد دیگر

Miguel Serras Vasco is a postdoctoral researcher at KTH Royal Institute of Technology in Stockholm, Sweden, affiliated with the Robotics, Perception and Learning (RPL) division. He holds a PhD from Instituto Superior Técnico, University of Lisbon (2023), where his work earned the Best PhD Thesis in AI in Portugal award. His research focuses on multimodal perception, reinforcement learning, and aligning artificial agents with human perception. He previously worked as an RSS Pioneer and research intern at Sony AI. Education: PhD in Artificial Intelligence, Instituto Superior Técnico, University of Lisbon (2023) Research Interests: Vasco’s work bridges robotics, neuroscience, and AI, emphasizing embodied agents that co-exist with humans. He explores representation learning, human-aligned image models, and sample-efficient reinforcement learning. His recent projects include super-human autonomous racing agents and olfactory perception modeling with transformers. Key Contributions: Vasco co-developed the GT Sophy racing agent, achieved outstanding results in visual decoding from brain activity, and proposed methods like FLoRA for preference-based RL. His work on NeuralSolver advances algorithm extrapolation in reinforcement learning. Awards: Best PhD Thesis in AI in Portugal (APPIA, 2023) Outstanding Paper Award at RLC 2024 (for autonomous racing research) Grants/Advising: Vasco advises students on multimodal and reinforcement learning topics. He actively organizes conferences like the Reinforcement Learning and Video Games Workshop (RLVG) at RLC 2025 and collaborates with institutions like INESC-ID (Lisbon). Labs/Teams: Associated with the Collaborative Autonomous Systems unit at KTH and previously with the GAIPS Lab (INESC-ID, Lisbon).

• Machine Learning
• Computer Vision
• Multimodal Learning
• +۷ مورد دیگر

Yunhui Guo is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced machine learning techniques including multimodal learning, continual learning, audio-visual recognition, and domain adaptation. He explores challenges in model robustness, cross-modal interactions, and efficient training strategies for deep neural networks. Key research areas include: Developing robust multimodal models for video entailment and dynamic 3D human reconstruction Improving audio-visual segmentation and sound separation through novel adaptation frameworks Advancing continual learning methods to handle domain shifts and out-of-distribution data Creating submodular optimization strategies for active learning in 3D object detection His recent work emphasizes real-world applications like medical image analysis (skin cancer sub-typing), robotics (LiDAR segmentation), and secure AI systems (model watermarking). The research also addresses foundational AI topics such as model uncertainty quantification and adaptive predictive systems. Publications focus on cutting-edge areas like multimodal LLM adaptation, hierarchical out-of-distribution detection, and bimodal online adaptation techniques. Current projects explore the intersection of multimodal perception and lifelong learning systems.

• Machine Learning
• Federated Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Jie Ding is an Associate Professor in the Department of Statistics at the University of Minnesota, College of Science and Engineering. His research bridges statistical theory and artificial intelligence, with a focus on federated learning, continual learning, and AI applications in biomedicine and telecommunications. Research Interests: Jie Ding's work centers on developing robust and scalable machine learning systems. His interests include federated and collaborative learning frameworks, statistical modeling under data scarcity, and AI-driven solutions for biomedical engineering and 5G network optimization. He investigates foundational aspects of model selection, information fusion, and adversarial robustness. The recent publications highlight a strong trend in distributed and secure AI, particularly in federated learning with dynamic resource allocation, backdoor attack analysis, and AI applications in cardiac organoid maturation. His work integrates statistical rigor with real-world deployment challenges across heterogeneous systems. Scientific Awards: No awards explicitly listed in the provided text. Advising and Grants: Dr. Ding serves as Principal Investigator (PI) on multiple major research grants, including an NSF CAREER award on continual learning and projects funded by the U.S. Department of Defense and NIH. These projects involve collaborative research with Harvard University and focus on AI-driven flexible electronics, information fusion, and 5G measurement modeling. While specific student names are not listed, his leadership of large-scale research initiatives implies active graduate student mentoring. Labs and Teams: Dr. Ding leads a research group focused on AI and statistical learning, collaborating with interdisciplinary teams in engineering, computer science, and biomedical research. His projects involve partnerships with Harvard University and the National Institutes of Health, indicating a strong network in both academic and applied research domains.

Robert Willeمشاهده پروفایل

استاد

• Design Automation
• Quantum Computing
• Computer Science
• +۷ مورد دیگر

Prof. Dr. Robert Wille is a Full Professor at the Technical University of Munich (TUM) in the School of Computation, Information and Technology and Chief Scientific Officer at the Software Competence Center Hagenberg GmbH . He leads the Chair for Design Automation , focusing on automatic methods for complex system design in conventional and future technologies. Studied Computer Science (Diploma) at the University of Bremen (2002-2006) Doctorate (summa cum laude) from the University of Bremen (2009) His research spans quantum computing , microfluidic biochips , field-coupled nanotechnologies , and reversible circuits , with applications in machine learning , artificial intelligence , and cyber-physical systems . Recent work includes quantum circuit verification, radar-camera fusion, and silicon dangling bond logic optimization. Robert Wille has received prestigious awards such as the ERC Consolidator Grant , Google Research Award , and Distinguished Professor appointment . He serves as Associate Editor for journals like IEEE TCAD and Springer LNCS, and has chaired conferences including DATE and ICCAD.

Boyu Kuangمشاهده پروفایل

پژوهشگر ارشد

• Semantic Segmentation
• Object Detection
• Weakly Supervised Learning
• +۹ مورد دیگر

Dr. Boyu Kuang is a Research Fellow in Computer Vision and Artificial Intelligence at Cranfield University, affiliated with the Centre for Computational Engineering Sciences. His work bridges academic research with industrial applications in aviation, robotics, and energy systems. Current role: Research Fellow in Computer Vision and AI Key affiliations: Centre for Computational Engineering Sciences Research interests include semantic segmentation , object detection , weakly and self-supervised learning , multi-modal perception , and vision-language foundation models . His methodology emphasizes robust AI systems for low-resource industrial environments , with applications in aviation maintenance , robotics , and energy infrastructure monitoring . He leads the Artificial Intelligence and Machine Learning module at Cranfield. Key activities include the UKRI, ATI, and Airbus-funded ONEHeart project on autonomous systems and intelligent inspection. Collaborations span Stanford University , King’s College London , Civil Aviation University of China , and industry partners like Airbus and Leidos . Publications focus on vision-language models , multi-modal perception , and industrial AI . As an Editorial Board Member for Discover Artificial Intelligence (Springer Nature), he contributes to academic governance. Peer review experience includes 60+ journal manuscripts for venues like IEEE Transactions on Image Processing and Elsevier Neural Networks . Facilities utilized include DARTeC and AIRC .

José Antonio Avela Mataمشاهده پروفایل

استادیار

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +۷ مورد دیگر

José Antonio Avela Mata is an Assistant Professor at the University of León, affiliated with the College of Industrial, Information and Aerospace Engineering in the Department of Electrical and Systems Engineering and Automation. His research focuses on cybersecurity in IoT environments, machine learning applications for intrusion detection systems, and protocol analysis (CoAP, MQTT) within the SECOMUCI research group. He also explores neural network architectures for attack classification and battery state prediction models. His recent work highlights trends in IoT security, particularly leveraging autoencoder latent spaces for attack categorization, clustering techniques for battery charge prediction, and neural networks for cross-modal tasks like roadway detection using LiDAR and camera data. He has contributed to intrusion detection frameworks for MQTT protocols and multiclass attack classification. Research interests include: Cybersecurity for IoT and 5G networks Machine learning-driven anomaly detection Hebbian learning and encoder-decoder architectures Automated classification of network attacks