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Osman HasanView profile

Professor

• Formal Methods
• Theorem Proving
• Hardware Verification
• +7 more

Professor Dr. Osman Hasan is a faculty member at the School of Electrical Engineering & Computer Science (SEECS) , National University of Sciences & Technology (NUST) , Islamabad, where he currently serves as Pro-Rector (Academics). His research primarily lies in formal methods, hardware verification, reliability analysis, and embedded systems, with applications in smart grids, robotics, and cybersecurity. His research interests include: Formal Methods and Theorem Proving Hardware and Software Verification Reliability and Safety Analysis of Critical Systems Smart Grids and Power Systems Approximate Computing and Energy-Efficient Design Robotics and Biomedical Systems His recent publications show a strong trend toward formal verification of hardware and cyber-physical systems, integration of machine learning with formal methods, and applications in power systems and robotics. He frequently employs higher-order logic theorem proving (e.g., HOL, ACL2) and model checking to ensure correctness and reliability. He has been actively involved in numerous international conferences such as FMCAD, FSEN, CICM, and ICTAC, serving on program committees and organizing workshops. His leadership as Pro-Rector highlights his commitment to enhancing academic quality and research excellence at NUST. He has supervised numerous students who have co-authored papers with him, indicating an active research group. His work bridges theoretical formal methods with practical engineering applications, particularly in safety-critical domains.

Xavier PuigView profile

Researcher

• Embodied AI
• Artificial Intelligence
• Computer Science
• +4 more

Xavier Puig is a Research Scientist at FAIR, focusing on Embodied AI and human-centered intelligent systems. Previously, he earned his Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), advised by Professor Antonio Torralba. He also holds a double degree in Computer Science and Telecommunications from UPC's CFIS program. Education Ph.D., MIT (CSAIL), 2022 B.S./B.S., UPC (CFIS program), 2016 Research Interests span building agents that collaborate with humans through goal anticipation, task coordination, and realistic behavior modeling in simulation environments. His work integrates Embodied AI , Multi-agent Systems , and 3D Scene Understanding with applications in Human-Robot Collaboration and Interactive Decision-Making . Recent Trends in his publications emphasize Planning in Multi-agent Environments , Language-guided Robotics , Procedural Scene Generation , and Human Foundation Models for embodied tasks. Scientific Awards Best Paper Award, NeurIPS Cooperative AI Workshop (2020) Spotlight Presentation, ICLR (2021)

• Computer Vision
• Machine Learning
• Image Processing
• +5 more

Dr. Xin Lin is a Professor at the School of Computer Science and Technology, University of Science and Technology of China in Hefei. With an extensive publication record spanning computer vision, machine learning, and artificial intelligence, Dr. Lin leads a research group focused on solving challenging problems in image processing, robotics, and wireless communications. His work bridges theoretical advancements with practical applications across healthcare, autonomous systems, and industrial manufacturing. Dr. Lin's research interests encompass computer vision, machine learning, image processing, and artificial intelligence, with particular expertise in image restoration, 3D object detection, and human pose estimation. His laboratory develops innovative approaches to handle multiple image degradations simultaneously and create lightweight, efficient vision systems suitable for real-world deployment. The research demonstrates strong interdisciplinary connections, applying computer vision techniques to medical imaging, satellite communications, and industrial IoT applications. Analysis of Dr. Lin's recent publications reveals a strong focus on multi-task learning approaches that address multiple image degradation problems simultaneously. His work shows increasing sophistication in handling complex real-world scenarios, from low-light conditions to rain interference, while maintaining computational efficiency. The research trajectory demonstrates a clear path from fundamental image processing techniques to practical applications in autonomous driving, healthcare, and industrial systems. Dr. Lin has received recognition for his contributions to the field through numerous publications in top-tier venues including CVPR, IEEE Transactions, and ACL. His work on image restoration, particularly the Dual Degradation Representation framework, has gained significant attention in the computer vision community. Dr. Lin actively supervises graduate students and collaborates with researchers worldwide. His laboratory works on cutting-edge projects involving digital twins for manufacturing, satellite communications, and medical imaging applications. Current research directions include developing more robust and efficient models for real-world deployment scenarios, with particular attention to resource-constrained environments.

Lin Geng FooView profile

Researcher

• Video Analysis
• Activity Understanding
• Diffusion Models
• +2 more

Lin Geng Foo is a postdoctoral researcher at the Max-Planck Institute for Informatics under the Visual Computing and Artificial Intelligence (VCAI) Department, supervised by Prof. Christian Theobalt. He earned his PhD and Bachelor's from the Singapore University of Technology and Design (SUTD) with the President's Graduate Fellowship and top honors. His research focuses on video analysis , activity understanding , diffusion models , and dynamic neural networks , particularly for 3D human generation/editing . His work includes groundbreaking papers on adversarial attacks, human mesh recovery, and pose estimation. Key trends in his recent publications include applications of diffusion models dynamic networks 3D human modeling video analysis action recognition neural rendering for tasks like mesh recovery, controllable video editing, and sign language translation. Scientific awards include PREMIA Best Student Paper 2023 Commendation Prize Keppel Awards of Excellence (2018, 2019) EY Business Analytics Award He serves as a reviewer for major conferences (CVPR, ICCV, NeurIPS, ICML) and has presented talks at ICME 2024 Metaverse Workshop and SDSC Industry & Demo Day 2024.

• Face Recognition
• Deep Learning
• Computer Vision
• +5 more

Xiaoyu Zhang is a Professor at the Center for Intelligent Computing Systems within the Institute of Information Engineering at the Chinese Academy of Sciences in Beijing, China. Having earned a PhD from the Institute of Automation, Chinese Academy of Sciences in 2010, Dr. Zhang has established a distinguished research career spanning over 15 years with over 130 publications. Dr. Zhang's research focuses on cutting-edge areas of computer vision and artificial intelligence, with particular expertise in face recognition systems, deepfake detection, knowledge graph reasoning, and network security. Their work bridges theoretical innovation with practical applications, addressing critical challenges in biometric security, multimedia analysis, and privacy protection. Recent research demonstrates pioneering work in masked face recognition, temporal action localization, and graph-based representation learning. Analysis of Dr. Zhang's publication trends reveals a strong emphasis on face recognition technology (appearing in approximately 40% of recent publications), with significant contributions to deepfake detection and knowledge graph reasoning. The research consistently combines deep learning methodologies with domain-specific innovations, showing evolution from traditional computer vision approaches to sophisticated transformer-based architectures and graph neural networks. Key thematic areas include robustness against adversarial conditions, handling of incomplete or masked data, and cross-domain knowledge transfer. Dr. Zhang has been instrumental in major collaborative projects including the FRCSyn Challenge series, which has become a benchmark for evaluating face recognition systems with synthetic data. Their work has been published in top-tier venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and the International Journal of Computer Vision. Within the Center for Intelligent Computing Systems, Dr. Zhang leads research initiatives focused on developing next-generation biometric security systems and intelligent multimedia analysis frameworks. The research group maintains strong collaborations with international institutions including ETH Zurich, Imperial College London, and various Chinese universities, fostering a dynamic environment for interdisciplinary research at the intersection of computer vision, machine learning, and security.

• Development and Learning
• Language Acquisition
• Perception and Action
• +4 more

Chen Yu is a Professor of Psychology at the University of Texas at Austin, affiliated with the College of Liberal Arts. He holds a Ph.D. from the University of Rochester. His research focuses on Development and Learning , Language Acquisition , Perception and Action , Visual Attention , Social Interaction , Computer Vision , and Machine Learning . He leads the Developmental Intelligence Lab , exploring interdisciplinary connections between cognitive science and computational methods. Recent teaching includes courses like Introduction to Machine Learning and Computational Behavioral Science . His work intersects with cutting-edge gravitational wave astronomy, contributing to cosmological studies using standard siren measurements and multimessenger observations. Key areas include improving Hubble constant estimation, analyzing binary neutron star mergers, and mitigating biases in cosmological parameter inference. Cosmological research highlights include optimizing gravitational wave detector networks (e.g., Cosmic Explorer), studying lensing effects, and advancing techniques to identify merger host galaxies. His publications span high-impact journals, reflecting expertise in both theoretical and observational astrophysics.

• Machine Learning
• Robotics
• Human-Robot Interaction
• +4 more

Joseph Camp is a Professor of Comparative Pathobiology and Secretary of Faculties at Purdue University. His work spans interdisciplinary areas including machine learning, robotics, and human-robot interaction. He leads the Global Engineering Program and holds affiliations across technical and administrative roles. His research focuses on reinforcement learning, multi-agent systems, and applying large language models to enhance decision-making and action anticipation in robotics and healthcare contexts. Key research interests include neuro-symbolic systems for short-context action prediction, robust reinforcement learning under noisy feedback, and geometric reasoning for zero-shot robotic grasping. His work also explores disentanglement in concept-residual models and transfer learning in multi-agent environments. He has published extensively on topics ranging from probabilistic imitation learning to bio-inspired robot locomotion design. While no specific awards or grants are listed, his contributions to human-robot interaction (HRI) and AI-driven medical devices reflect a strong focus on practical applications. His lab’s recent work integrates large language models with traditional robotics algorithms to improve adaptability in dynamic environments. No advising relationships or student names are explicitly mentioned in the provided texts.

Pietro PeronaView profile

Professor

• Machine Learning
• Computer Vision
• Signal Processing
• +5 more

Pietro Perona is the Allen E. Puckett Professor of Electrical Engineering at the California Institute of Technology (Caltech). He holds a D.Eng. from the University of Padua (1985) and a Ph.D. from UC Berkeley (1990). His academic roles include serving as Director of the Center for Neuromorphic Systems Engineering (1999–2004) and Executive Officer for Electrical Engineering (2006–2010). His research focuses on computational vision, visual recognition, and machine learning. Key projects include the Visipedia initiative (smart apps for species identification like iNaturalist and Merlin Bird ID) and collaborations on neuroethology with Professors Anderson and Dickinson, studying fruit fly and mouse behavior. He also explores human visual perception and crowd-sourced learning. Perona has been recognized as an IEEE Fellow and leads the Caltech Vision Lab, which develops machine vision systems for applications in conservation, robotics, and neuroscience. His lab’s work spans foundational research to real-world tools, including datasets like the Caltech Fish Counting Dataset and MABe22 benchmarks. He teaches courses on computational vision and has advised numerous projects in AI and robotics. Current collaborations include partnerships with Amazon Web Services (AWS) and Disney on AI-driven solutions.

Ken GoldbergView profile

Professor

• Artificial Intelligence
• Robotics
• Deep Reinforcement Learning
• +5 more

Ken Goldberg is a Professor of Industrial Engineering and Operations Research at UC Berkeley, with secondary appointments in EECS, Art Practice, and UCSF's Radiation Oncology. He joined UC Berkeley in 1995 and holds a PhD from Carnegie Mellon University (1990) and dual degrees from the University of Pennsylvania (1984). His research spans robotics, automation, and art, focusing on artificial intelligence, medical robotics, and human-robot interaction. He leads the AUTOLab and is a co-founder of the Berkeley Artificial Intelligence Research (BAIR) Lab. Goldberg's art installations, including collaborations with Sundance films, explore the intersection of technology and humanity. He has pioneered cloud robotics, surgical robotics, and collaborative filtering systems. Notable awards include the IEEE Fellow (2005), Joseph Engelberger Robotics Award (2000), and NSF Presidential Faculty Fellow (1995). His work integrates technical innovation with societal impact, emphasizing ethical AI and automation. Educations: PhD in Computer Science, Carnegie Mellon University (1990) BSEE in Electrical Engineering, University of Pennsylvania (1984) BSE in Economics, UPenn Wharton (1984) Research Interests: Goldberg's work bridges robotics, AI, and art. Key areas include cloud robotics (FogROS2), surgical robotics (Cal-MR), AI ethics, and interactive systems. His research in dexterous manipulation and grasp planning (Dex-Net) has advanced robotic capabilities in manufacturing and healthcare. Publications: Over 300 papers, including influential works on grasp planning, robotic learning, and collaborative filtering. Recent focus on AI in robotics, surgical assistance, and ethical automation. Awards: IEEE Fellow (2005) Joseph Engelberger Robotics Award (2000) NSF Presidential Faculty Fellow (1995) Advising and Grants: Supervised numerous students in robotics, automation, and AI. Active in funding initiatives like the National Robotics Initiative and NSF grants. Labs/Teams: Leads AUTOLab and co-founded the Berkeley Center for New Media (BCNM). Collaborates with industry partners like Ambi Robotics and Jacobi Robotics.

Hyung Jin ChangView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +2 more

Dr Hyung Jin Chang is an Associate Professor in the School of Computer Science at the University of Birmingham . He earned a PhD in machine learning and computer vision from Seoul National University in 2013, following a BSc in Electrical and Computer Engineering from the same institution in 2006. Research focuses on artificial intelligence , computer vision , robotics , and human-robot interaction , particularly human-centred visual learning for hand/body pose estimation, gaze tracking, and action/internal state understanding. Key projects include EU FP7 GRANT 612139 , EU H2020 GRANT 643783 , and industry collaborations with Samsung Electronics and Samsung GRO Grant . Recent publications address advancements in unsupervised learning , domain adaptation , 3D hand-object interaction modeling , and multimodal gaze following in conversational scenarios. His work bridges theoretical AI research with practical applications in vision-based robotics and medical image analysis, supported by both academic and industrial grants.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +6 more

Juan Carlos Gonzalez Victores is an Associate Professor at the Department of Systems Engineering and Automation, Universidad Carlos III de Madrid (UC3M), actively leading research in the Robotics Lab. His work spans multiple disciplines including Computer Science, Medicine, and Robotics and Industrial Informatics. Research interests focus on: Human-Robot Interaction with applications in neurorehabilitation and accessibility Autonomous Systems for smart cities and underground infrastructure inspection Deep Learning and Reinforcement Learning for robotic manipulation Medical Robotics with emphasis on surgical and diagnostic applications Recent publications highlight trends in: Garment-agnostic manipulation techniques Neurorehabilitation systems using synthetic data Human-like sketching via Deep Q-Learning Safety analysis of AI-human organizational interfaces Contact: juancarlos.gonzalez@uc3m.es | jcgvicto@ing.uc3m.es

• Artificial Intelligence
• Machine Learning
• Automotive Production
• +8 more

Prof. Dr. Alexander Schiendorfer is a faculty member at Technische Hochschule Ingolstadt within the Faculty of Industrial Engineering , focusing on AI-based Optimization in Automotive Production . His research bridges Artificial Intelligence with manufacturing and industrial engineering , particularly in constraint programming, self-organizing systems, and machine learning applications for composite materials. His work spans pedagogical innovation in machine learning education, real-time manufacturing analytics , and AI for energy systems . Recent publications highlight applications in gas grid management, synthetic data frameworks, and defect analysis in autonomous driving sensors. He leads teams at AImotion Bavaria , collaborating on projects like SmartManPy for synthetic manufacturing data and MORL agents for multi-objective energy optimization. His methodological contributions include certainty groups for neural network confidence estimation and hierarchical resource allocation algorithms.

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +17 more

Prof. Dr. Antonio Krüger is the CEO of the German Research Center for Artificial Intelligence (DFKI) and Director of the Ubiquitous Media Technology Lab (UMTL) at the Saarland Informatics Campus in Saarbrücken, Germany. He is a leading researcher in human-computer interaction, virtual and augmented reality, multimodal interfaces, and intelligent systems. His research interests span a broad range of topics including virtual reality (VR), augmented reality (AR), gesture interaction, haptic feedback, EEG and eye tracking, gamification, cognitive load estimation, brain-computer interfaces, and smart environments. His work emphasizes user-centered design, perceptual illusions, and real-world applications in domains such as automotive, healthcare, and education. His recent publications (2023–2024) demonstrate a strong focus on advancing VR interaction through techniques like hand redirection, synthetic gesture generation for vehicles, implicit intent recognition using multimodal sensing, and personalized object referencing. These works reflect trends toward seamless, natural, and adaptive interfaces that leverage AI, perception, and real-time feedback. Prof. Krüger supervises a large team of researchers and students, including Mansi Sharma, Amr Gomaa, André Zenner, and Martin Feick, indicating active mentorship and collaborative research leadership. His lab, UMTL, develops innovative technologies in immersive environments, wearable systems, and intelligent user assistance, contributing to both academic knowledge and practical applications.

Yen-Ling KuoView profile

Assistant Professor

• Robot Learning
• Human-AI/Robot Interaction
• Artificial Intelligence
• +1 more

Yen-Ling Kuo is an Assistant Professor in the Department of Computer Science at the University of Virginia and a member of the Link Lab. Her research focuses on robot learning, human-AI/robot interaction, and integrating artificial intelligence with cognitive science to enable robots to generalize reasoning skills for human interaction, including language understanding and common sense reasoning. She holds a PhD from MIT CSAIL, advised by Boris Katz and Andrei Barbu. She teaches courses such as Artificial Intelligence and Learning for Interactive Robots, emphasizing foundational AI concepts, robotics applications, and generative AI. Research Interests: Robot Learning Human-AI/Robot Interaction Cognitive Science Integration Machine Learning for Robotic Systems Awards: NSF CAREER Award (2024) Advising & Grants: Actively recruiting students for her UVA research group, focusing on interactive robotics and AI collaboration. Her work is supported by grants including the NSF CAREER Award. Labs: Member of the Link Lab, a leading center for robotics and autonomous systems at UVA.

Ielka van der SluisView profile

Senior Lecturer

• Human Communication
• Information Presentation
• Human-Computer Interaction
• +3 more

Dr. Ielka van der Sluis is a Senior Lecturer at the Department of Communication and Information Science, Faculty of Arts at the University of Groningen since 2011. She holds a PhD in Computational Linguistics from Tilburg University (2005) and has held research positions at Trinity College Dublin (2008–2011), the University of Aberdeen (UK), and the University of Twente. Her research focuses on human communication, multimodal interaction, natural language generation, and affective computing. She is a member of the Center for Language and Cognition Groningen and has contributed to projects like TUNA (EPSRC-funded), the OLIVE EC project, and the PAT framework for multimodal instruction analysis. Her work bridges computational linguistics and human-computer interaction, emphasizing the design and evaluation of multimodal systems. She has explored topics such as cross-linguistic referring expression generation, affective text impact, and procedural instruction design. Key contributions include developing the PAT annotation model for multimodal instructions and investigating cross-cultural assessment of automated referring expressions in virtual environments. Her research history spans over two decades, with impactful contributions to the understanding of text-picture alignment in procedural instructions, action categorization in multimedia, and user-centric evaluation frameworks. She collaborates internationally, particularly in computational linguistics and next-generation localization technologies.