جستجوی استاد راهنمای دانشگاهی

• Neural Engineering
• Biomedical Engineering
• Human-Computer Interaction
• +۳ مورد دیگر

Alexandre Armengol Urpi is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Mechanical Engineering within the School of Engineering. His research focuses on neural engineering, biomedical engineering, human-computer interaction, IoT systems, and signal processing. He explores applications such as brain-computer interfaces (BCI), EEG-based diagnostics, and augmented reality (AR) integration with IoT devices. His work emphasizes translating tacit expert knowledge into actionable data, improving medical diagnostics through machine learning, and enhancing cybersecurity in BCI systems. Projects include developing wireless health-monitoring systems and hands-free AR navigation tools using SSVEP signals. Recent studies address covert spatial attention decoding, industrial IoT-AR systems, and RFID innovations beyond traditional identification. While no formal student advisees are listed, his research collaborations involve interdisciplinary teams across MIT. His contributions span theoretical frameworks and practical implementations, with a focus on closing the gap between human expertise and technological systems. Key research trends include integrating biosignals with smart devices, enhancing neural interface accuracy, and applying deep learning to object pose estimation in industrial contexts. His publications highlight a balance between foundational neuroscience and applied engineering solutions. No scientific awards or grants are explicitly mentioned in the provided texts.

Alejandro Sztrajmanمشاهده پروفایل

پژوهشگر

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

Alejandro Sztrajman is a Researcher and Research Associate at the University of Cambridge's Department of Computer Science and Technology, affiliated with the Rainbow Group. He holds a PhD in Computer Science from University College London (UCL), supported by a Marie Curie Fellowship, and has conducted research internships at Microsoft and Adobe. His work bridges machine learning and visual computing, focusing on neural fields, generative AI, and physics-based rendering, with applications in material appearance modeling, scene illumination, and computational photography. His research spans topics like neural BRDF representations, point cloud generation via diffusion models, and interpretable time series analysis. Key contributions include LSCD (irregular time series imputation), NeuMaDiff (material synthesis via hyperdiffusion), and FrePolad (point cloud generation). He has also developed methods for HDR image deglaring and color calibration for OLED displays. Awards include the Wiley Top Cited Paper and Marie Curie Fellowship funding. His work emphasizes practical applications, with publications in top-tier venues like ECCV, CVPR, and ICML. Collaborations involve Professors Cengiz Öztireli and Rafał Mantiuk at Cambridge, and Tobias Ritschel at UCL. His research aims to advance generative AI, neural rendering, and physically grounded machine learning techniques.

• Computer Vision
• Robotics
• 3D Vision
• +۴ مورد دیگر

Sangyun Shin is a Researcher in the Department of Computer Science at the University of Oxford, supervised by Professors Niki Trigoni and Andrew Markham. His work focuses on advancing object localization systems for robotics through 3D vision technologies. He specializes in integrating multimodal sensing (e.g., RGB-D cameras, acoustic arrays) to enhance robotic perception in challenging environments. His research interests include 3D motion capture for wildlife, domain-adaptive 3D detection, and self-supervised learning for nighttime vision. He has explored applications ranging from long-range wildlife tracking to autonomous drone navigation using reinforcement learning. Recent efforts emphasize sensor fusion and neural network architectures tailored for dynamic environments. Key technical contributions include the SoundLoc3D system for 3D sound localization and the WildPose framework for wildlife motion capture. His publications span topics like acoustic neural warping fields (SPEAR), spherical point cloud segmentation, and LiDAR-based object detection for urban driving. Shin's work bridges theoretical advancements in machine learning with practical robotics applications, addressing challenges such as cross-domain adaptation and low-resource sensor setups. His research has implications for autonomous systems, environmental monitoring, and human-drone interaction interfaces.

Liang Zhengمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Deep Learning
• Person Re-identification
• +۳ مورد دیگر

Liang Zheng is a Senior Lecturer in the School of Computing at The Australian National University (ANU). He holds a Computer Science Futures Fellowship and an ARC DECRA Fellowship. His research focuses on computer vision applications such as person re-identification and medical image understanding, alongside fundamental challenges in dataset-level learning and synthetic data analysis. Zheng has contributed to influential datasets like Market-1501, PRW, and MARS, advancing person re-identification and multi-camera tracking. He has served as Area Chair for CVPR, ECCV, and ACM Multimedia, and is an Associate Editor for IEEE T-CSVT. Education: PhD (Electrical Engineering) from Tsinghua University (2015), Bachelor of Science (Life Science) from Tsinghua University (2010). His research integrates deep learning and generative models, addressing challenges in image generation, adversarial robustness, and multi-agent systems. Notable awards include the Computer Science Futures Fellowship (ANU) and ARC DECRA Fellowship (2020). Key contributions include pioneering work on re-identification datasets and methods, as well as advancements in cross-modal retrieval and domain adaptation. Ongoing research emphasizes scalable models, synthetic data generation, and ethical AI practices.

Benjamin Brackeمشاهده پروفایل

پژوهشگر

• Machine Learning
• Medical Imaging
• Computer Vision
• +۳ مورد دیگر

Benjamin Bracke, M.Sc., is a researcher with a strong focus on interdisciplinary applications of machine learning and computer vision. His work spans medical imaging, biodiversity informatics, and educational technology. He has contributed to projects such as ImageCLEF challenges, advancing multimedia retrieval systems in healthcare and ecological contexts. Recent research includes diabetic foot ulcer diagnosis, snake species classification using multimodal data, and kidney tumor segmentation via U-Net architectures. Key research interests include machine learning pedagogy, integrating industrial projects into Master’s curricula, and developing automated solutions for medical diagnostics. His technical expertise lies in object detection, semantic segmentation, geospatial data fusion, and metadata-driven classification systems. Bracke’s publications demonstrate a decade-long commitment to medical engineering (e.g., femoral neck axis detection for hip surgeries) alongside modern AI applications. Despite no listed awards or grants, his work bridges academic research with practical clinical and ecological challenges. No lab affiliations or student advisements are explicitly mentioned in the provided texts.

Stephan Pareigisمشاهده پروفایل

استاد

• Autonomous Systems
• Robotics
• Reinforcement Learning
• +۳ مورد دیگر

Prof. Dr. Stephan Pareigis is the Head of the Department of Computer Science at Hamburg University of Applied Sciences. He holds a professorship in Applied Mathematics and Technical Informatics. His career includes leadership roles such as Chair of the Examination Board for Computer Science (2005–2018) and founding director of the autosys research lab . His expertise spans autonomous systems, reinforcement learning, robotics, and embedded machine learning. He has extensive industry experience, including roles at Heidelberger Druckmaschinen AG and Siemens AG before joining academia in 2004. His research focuses on bridging simulation and real-world applications in autonomous systems, particularly in miniature platforms for reliable AI development. Education and Academic Journey: 1992: Diplom Mathematiker (LMU Munich, minor in Computer Science) 1998: Dr.rer.nat in Numerical Methods for Reinforcement Learning (CAU Kiel) Research Interests: Autonomous and Intelligent Systems Reinforcement Learning for robotics Embedded Machine Learning Miniature autonomy testing frameworks His work emphasizes real-world applicability through projects like Miniature Autonomy for scalable AI validation. Labs and Teams: Leads the autosys research lab , focusing on autonomous systems research and industry collaboration through the Forschungs- und Transferzentrum Smart Systems .

DANIEL CORES COSTAمشاهده پروفایل

استادیار

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

Daniel Cores Costa is an Assistant Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing under the Higher Technical School of Engineering. His research focuses on advanced computer vision techniques, including spatio-temporal convolutional neural networks, few-shot learning, and generative adversarial networks (GANs) for small object detection. He earned his Doctorate from the same university in 2022 with a thesis titled Spatio-temporal convolutional neural networks for video object detection , advised by Dr. Victor Manuel Brea Sánchez and Dr. Manuel Mucientes Molina. His work spans video object detection, medical imaging applications (e.g., COVID-19 diagnosis), and autonomous systems like UAV navigation. Key areas include improving data efficiency via pseudo-label mining, enhancing GAN-based data augmentation pipelines, and developing spatio-temporal feature aggregation techniques for dynamic scenes. He collaborates with the GSI Group (Intelligent Systems Group) , contributing to interdisciplinary projects at the intersection of AI, robotics, and healthcare. Publications highlight trends in few-shot learning for medical imaging, video-language evaluation benchmarks (TVBench), and real-time UAV-based object detection. Notable contributions address challenges in small object detection in videos and X-ray vision systems. His research emphasizes practical applications in healthcare diagnostics and autonomous systems. Despite no explicit mention of awards or grants, his prolific publication record (2015–2025) underscores sustained academic engagement. Advising details are unavailable, but his involvement in thesis supervision and collaborative groups like GSI indicates active mentorship.

Henny Admoniمشاهده پروفایل

دانشیار

• Human-Robot Interaction
• Assistive Robotics
• Nonverbal Communication
• +۵ مورد دیگر

Henny Admoni is an Associate Professor in the Robotics Institute at Carnegie Mellon University (CMU), with a courtesy appointment in the Human-Computer Interaction Institute. She leads the Human And Robot Partners (HARP) Lab, focusing on assistive and collaborative robotics, particularly how robots can interpret human nonverbal cues like eye gaze to improve interactions. Her research spans healthcare, human-robot teamwork, and socially assistive robotics, supported by NSF, ONR, and industry partners. Admoni holds a PhD in Computer Science from Yale University and a BA/MA from Wesleyan University. Education PhD in Computer Science, Yale University BA/MA in Computer Science, Wesleyan University Research Interests Admoni’s work emphasizes human-centered robotics, including assistive systems for mobility-impaired users, driver situational awareness modeling, and fostering social support networks through AI. She investigates how robots can proactively learn from humans, adapt to team dynamics, and communicate transparently to build trust. Key themes include nonverbal communication, shared autonomy, and ethical design. Grants & Awards NSF CAREER Grant (2020) Okawa Research Grant (2021) A. Nico Habermann Career Development Professorship (CMU) Advising & Teaching Admoni advises graduate students on topics like robot learning, assistive systems, and human-robot teaming. She teaches courses on Human-Robot Interaction at both undergraduate and graduate levels, emphasizing interdisciplinary approaches that blend robotics, AI, and cognitive science. Labs & Projects The HARP Lab develops robots for meal preparation assistance, driving support, and socially assistive interventions. Current projects include the HARMONIC dataset for collaborative tasks and COHUMAIN for socio-cognitive architectures in human-machine teams. Admoni is on sabbatical at KTH University through 2025.

• Agro Food Robotics
• Precision Farming
• Optimal Control
• +۳ مورد دیگر

Eldert van Henten is a full professor of Biosystems Engineering at Wageningen University & Research. His research focuses on agro food robotics, precision farming, and optimal control systems in agriculture. He earned his MSc (1987) and PhD (1994) in Agricultural and Environmental Sciences from Wageningen University. He previously led the Farm Technology Group until 2021 and currently leads projects like FlexCRAFT (Cognitive Robots for Flexible Agro Food Technology) and Trimbot2020, developing robotics and automation for agricultural applications. His work emphasizes translating technology to practical environments (TRL 5-6), with contributions to robotic harvesting, precision weed control, and greenhouse climate modeling. Education: MSc (cum laude, 1987), PhD (1994) in Agricultural Sciences at Wageningen University. Key Projects: FlexCRAFT: Generic robotics principles for food processing, packing, and greenhouse operations. Trimbot2020: Outdoor garden trimming robot with 3D path planning. PoultryBot: Autonomous egg collection in poultry farming. Editorial Roles: Member of editorial boards for Biosystems Engineering, Computers and Electronics in Agriculture, and others. Research Themes: Agro food robotics (harvesting, vision systems, machine learning). Optimal control of agricultural processes (greenhouse climate, irrigation). Generic robotics frameworks for agri-food applications. Awards: Not explicitly listed, but recognized via research impact (h-index 31–47 across platforms). His teaching includes courses on greenhouse technology, field robotics, and thesis supervision. He collaborates with industry through user groups and consortia to ensure practical technology transfer.

Ganesh Sankaranarayananمشاهده پروفایل

دانشیار بالینی

• Surgical Simulation
• Medical Robotics
• Artificial Intelligence in Surgery
• +۳ مورد دیگر

Dr. Ganesh Sankaranarayanan is a Clinical Associate Professor and co-director of the Center for Assessment of Surgical Proficiency at the University of Texas Southwestern Medical Center. He holds adjunct roles at the University of Texas at Arlington and Texas A&M College of Medicine. His expertise spans surgical simulation, medical robotics, and AI-driven surgical guidance. He earned a Ph.D. in Electrical Engineering from the University of Washington and completed postdoctoral research at Rensselaer Polytechnic Institute. Education: Ph.D. in Electrical Engineering, University of Washington Postdoctoral Research, Rensselaer Polytechnic Institute Key Roles: Co-director, Center for Assessment of Surgical Proficiency Adjunct Professor (UT Arlington) Clinical Associate Professor (Texas A&M College of Medicine) Research Interests: Dr. Sankaranarayanan focuses on advancing surgical education through virtual reality (VR) simulators, haptic technology, and AI applications in surgery. His work includes developing simulators for NOTES, laparoscopic procedures, and emergency scenarios like OR fires. He emphasizes objective performance metrics and cognitive load management in training. Publications: Over 60 peer-reviewed articles in journals like Annals of Surgery and Surgical Endoscopy , with recent work on AI-guided anatomy identification and VR-based safety training. Awards: Immy Award finalist (Lap Band Simulator) Grants & Leadership: NIH-funded research, leadership in SAGES task forces, and development of collaborative surgical simulation frameworks. His work bridges engineering and medicine to enhance surgical training and patient safety.

Ya Huangمشاهده پروفایل

دانشیار

• Biomechanics
• Human Motion Dynamics
• Vibration Analysis
• +۶ مورد دیگر

Ya Huang is an Associate Professor in Human Motion Dynamics at the University of Portsmouth, affiliated with the School of Electrical and Mechanical Engineering within the Faculty of Technology. He specializes in human responses to motion, signal processing, and vibration analysis. Huang joined Portsmouth in 2009 after postdoctoral research at the University of Sheffield on structural dynamics and earned his PhD in human responses to whole-body vibration from the Institute of Sound and Vibration Research (ISVR), University of Southampton (2008). Education: PhD in Human Responses to Whole-Body Vibration, University of Southampton (2008) Postdoctoral Researcher, Blast and Impact Dynamics Group, University of Sheffield Research Interests: Dr. Huang focuses on applying analytical and computational methods to understand human biomechanical responses to whole-body vibration and mechanical shocks across land, air, and sea transport. Key areas include: - Development of signal reconstruction methodologies for vibration analysis - Maritime safety through real-time wave imaging and vessel seakeeping optimization - Musculoskeletal modelling for crew bracing strategies on fast lifeboats Grants & Projects: EPSRC-funded project (EP/X035778/1) for real-time semantic wave imaging in maritime safety Royal National Lifeboat Institution (RNLI)-funded studies on crew dynamics on lifeboats Development of stereo vision systems for ocean wave analysis Awards: Best Conference Paper Award at IEEE Conference on Intelligent Systems (2020) Labs & Teams: Part of the Centre of Excellence in Defence, Risk & Resilience. Collaborates on projects involving hydrodynamics, autonomous vessels, and human-centred marine transport design.

Xiaoyu Chenمشاهده پروفایل

استادیار

• Machine Learning
• Advanced Manufacturing
• Cyber Manufacturing Systems
• +۵ مورد دیگر

Xiaoyu Chen serves as an Assistant Professor in the Department of Industrial and Systems Engineering within the School of Engineering and Applied Sciences at the University at Buffalo. His academic foundation includes a PhD in Industrial and Systems Engineering (2021) and an MEng in Computer Science (2021) from Virginia Tech, complemented by a BS in Optical Information Science and Technology from Beijing Institute of Technology (2015). Dr. Chen's research spans machine learning methodology for advanced manufacturing, networked data analytics in cyber manufacturing systems, statistical methods for Bayesian deep learning, smart connected health applications for organ transplantation and perioperative medicine, and human behavior/performance modeling. His work bridges industrial engineering with cutting-edge AI techniques to solve complex problems in manufacturing and healthcare systems. Analysis of recent publications reveals a strong focus on developing interpretable and context-aware AI systems for manufacturing and clinical applications. Key trends include multimodal learning frameworks, distributed data processing for fog manufacturing, human-AI collaboration paradigms, and medical diagnostics through novel data modalities like breath analysis. Dr. Chen actively teaches core engineering courses including Probability & Statistics for Engineers (IE 360/560), Simulation (IE 541), and Predictive Analytics (Deep Learning) (IE 463/663), demonstrating commitment to both foundational and emerging topics in industrial engineering education.

• 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.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۴ مورد دیگر

Michail G. Lagoudakis is a Professor at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete (TUC), Greece. He leads the Intelligent Systems Laboratory (InteLLigence), focusing on machine learning, robotics, and decision-making under uncertainty. His academic journey includes a Ph.D. from Duke University (2003) and prior roles at Georgia Tech and AT&T Labs. Education: Ph.D. in Computer Science, Duke University, 2003 M.Sc. in Computer Science, University of Louisiana, 1998 Diploma in Computer Engineering and Informatics, University of Patras, 1995 Research Interests: His work spans reinforcement learning, multi-agent systems, robotics (team coordination, probabilistic methods), and complex systems. Notable projects include the EURECA-PRO university network, RoboCup Kouretes team, and algorithmic advancements like LSPI. Teaching: Courses include Artificial Intelligence, Machine Learning, and Robotics. His teaching philosophy emphasizes hands-on learning with tools like ROS and NAO robots. Projects & Collaborations: Current projects include aerial-ground robot collaboration, dialogue management systems, and solar energy optimization. Earlier work includes auction-based coordination and DPLL solver enhancements (RLSAT). Lab & Teams: The Intelligent Systems Lab develops autonomous systems, robotic platforms, and learning algorithms. The Kouretes team competes in RoboCup leagues, showcasing adaptive multi-agent strategies.

• Medical Image Processing
• Remote Sensing
• Machine Learning
• +۴ مورد دیگر

Jianwu Dang is a faculty member at Lanzhou Jiaotong University's School of Electronic and Information Engineering , with affiliations to the Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic and Image Processing . His academic career spans multiple institutions including a PhD from Southwest Jiaotong University in 1996. Research Interests : Focused on Medical Image Processing , Remote Sensing , and Wireless Sensor Networks , his work bridges Artificial Intelligence and Transportation Engineering , particularly in railway systems. Publications : Recent articles address Edge Computing coalition structures (2023), Extended Reality in education (2023), and Deep Learning for remote sensing (2022). Collaborations : Frequently co-authors with Yangping Wang and Zhanjun Hao , contributing to journals like IEEE Internet of Things Journal and conferences such as APSIPA .