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Federico ThomasView profile

Research Professor

• Robotics
• Kinematics
• Distance Geometry
• +6 more

Federico Thomas is a Professor of Research at the Spanish National Research Council (CSIC), based at the Institut de Robótica i Informàtica Industrial (IRI), affiliated with the Technical University of Catalonia (UPC), Barcelona, Spain. He has held visiting and invited professorships at the University of Massachusetts, Oxford University, École Centrale de Nantes, and the University of Ferrara. Telecommunications Engineering Degree, 1984, UPC Ph.D. in Computer Science, 1988, UPC NATO Postdoctoral Fellow, University of Massachusetts, 1991 His research lies at the intersection of Geometry and Kinematics , with strong applications in Robotics , Computer Vision , and Computer Graphics . He specializes in Distance Geometry and Clifford Algebras , developing mathematical frameworks for solving complex robotic motion and configuration problems. His work enables precise modeling of mechanisms such as lobster-inspired arms and parallel robots. His recent publications (2022–2025) reveal a deep engagement with geometric computation, focusing on rotation matrices, sphere tangents, ellipse intersections, and singularity analysis in parallel robots. These works consistently apply advanced algebraic and geometric methods to practical robotic challenges, demonstrating a trajectory of theoretical rigor with engineering relevance. His scientific honors include the NATO Postdoctoral Fellowship. He has served as an Associate Editor for IEEE Transactions on Robotics (2005–2009, and currently), ASME Journal of Mechanisms and Robotics (2009–2012, and currently), and The International Journal of Mechanics and Control . Federico Thomas was the former director of IRI. He advises students in robotics and geometry, as indicated by the 'Students' section on his homepage. He has not received major grants explicitly listed, but his sustained publication record and editorial roles suggest continuous research support. His 'Art and Science Tapas' initiative reflects an interdisciplinary outreach effort, blending history, art, and science. He leads research activities within the IRI, particularly in computational robotics and geometric methods. His team likely includes collaborators such as J.M. Porta, S. Sarabandi, and B. Bongardt, with whom he co-authors frequently. The 'Kinematics Virtual Library' and 'Tapas' projects suggest an educational and cultural component to his lab’s mission.

Benoit GuillardView profile

Researcher

• 3D Surface Reconstruction
• Neural Networks
• Computer Vision
• +4 more

Benoit Guillard is a researcher specializing in 3D surface reconstruction and neural network applications. He earned his PhD in 2023 from EPFL’s Computer Vision Lab (CVLAB), focusing on representation learning for 3D geometry. His work bridges computer vision, machine learning, and computer graphics through innovative solutions for garment modeling, LiDAR simulation, and implicit surface processing. Notable contributions include: MeshUDF (ECCV 2022): Differentiable meshing for unsigned distance fields DrapeNet (CVPR 2023): Self-supervised garment draping technique UCLID-Net (NeurIPS 2020): Single-view 3D reconstruction with hybrid architectures His research has garnered recognition through CVPR 2022 Best Reviewer and NeurIPS 2022 Top Reviewer awards. He has served as a reviewer for top-tier conferences including CVPR, ICCV, and SIGGRAPH.

• Computer Vision
• Biometrics
• Face Recognition
• +3 more

Xiaoming Liu is a Professor at Michigan State University with extensive contributions to computer vision and biometrics. His research spans face recognition, 3D reconstruction, image forgery detection, and adversarial machine learning, with over 300 publications from 1999 to 2025. His primary research interests include Computer Vision , Biometrics , and Adversarial Machine Learning . Liu's work focuses on developing robust systems for face recognition at scale, detecting image manipulations, and creating 3D reconstruction techniques. Recent projects include SapiensID for human recognition, FRCSyn for synthetic face recognition, and proactive watermarking schemes. Liu's publication trends show increasing focus on multi-modal biometrics (combining face, body, and gait), image forgery detection using hierarchical approaches, and adversarial defense mechanisms . His 2023-2025 work emphasizes synthetic data applications and physics-driven recognition systems. Liu has mentored numerous students including Feng Liu, Minchul Kim, and Xiao Guo, who frequently co-author his papers. His research has been supported by grants enabling projects like FarSight (long-range biometrics) and ProMark (proactive watermarking). He leads research in biometrics security and computer vision, with recent work focusing on ethical AI applications and robust recognition systems. His team develops tools for detecting deepfakes and improving recognition in challenging conditions.

• Machine Learning
• Maritime Engineering
• Robotics
• +4 more

Shengkai Zhang is an active researcher with 26 publications and 444 citations spanning engineering, computer science, and environmental disciplines. His work demonstrates strong interdisciplinary collaboration through co-authorship with researchers like Kezhong Liu and Mozi Chen across multiple high-impact venues including IEEE conferences, arXiv, and specialized journals. His research interests center on Machine Learning applications in maritime systems , with significant contributions to Large Language Model integration for ship navigation, wireless sensing for bridge officer monitoring, and sensor fusion techniques. Additional expertise spans robotics perception (visual-inertial systems, mmWave radar enhancement), environmental modeling (urban energy systems, climate studies), and biomedical applications of traditional medicine. Recent work shows increasing focus on AI foundation models and their security implications. Zhang's publication trajectory reveals consistent output with accelerating impact since 2023, featuring 15+ papers in 2024 alone. His research clusters around three core themes: Maritime AI Systems (LLM navigation, track association, watchkeeping monitoring) Advanced Sensing Technologies (mmWave radar, Wi-Fi sensing, GNSS fusion) Environmental & Biomedical Applications (urban energy modeling, gut microbiome studies) These areas demonstrate both technical depth in signal processing/computer vision and practical focus on real-world engineering challenges.

Renjie LiaoView profile

Assistant Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

Xuesong LiView profile

Research Fellow

• Machine Learning
• 3D Reconstruction
• Object Detection
• +5 more

Xuesong Li is a Research Fellow at Australian National University (ANU) and CSIRO, focusing on machine learning and 3D scene reconstruction. His research integrates autonomous systems, dynamic/static scene modeling, and object detection through innovative approaches like Neural Radiance Fields (NeRF). Previously, he worked at Huawei (Shanghai) on autonomous driving technologies involving LiDAR point cloud analysis. He holds a Ph.D. in Robotics from UNSW Sydney (2020), with a thesis centered on object detection for intelligent robots. Education: Ph.D. in Robotics, UNSW Sydney (2020) Research Interests: Machine learning applications in 3D reconstruction (e.g., dynamic/static scenes, reflective objects), spatial transcriptomics analysis, crop biomass estimation via multi-modal datasets, and autonomous system development. His work bridges computer vision with real-world challenges in agriculture, robotics, and medical imaging. Publications Trends: Recent work emphasizes cross-domain knowledge transfer, long-tailed recognition, and real-world 3D reconstruction challenges. Notable contributions include the MMCBE dataset for crop biomass prediction and AGP-Net for spatial transcriptomics analysis. Awards: None explicitly mentioned. Advising & Collaborations: Actively recruiting visiting PhD and master’s students in machine learning and 3D reconstruction. Collaborates with institutions like CSIRO and Huawei, focusing on applications in autonomous systems and precision agriculture. Labs/Teams: Involved with ANU’s Division of Ecology and Evolution and CSIRO’s research groups, leveraging interdisciplinary approaches to tackle complex problems in robotics and environmental science.

Ubbo VisserView profile

Associate Professor

• Artificial Intelligence
• Robotics
• Semantic Web
• +5 more

Ubbo Visser is an Associate Professor and Director of Graduate Studies in the Department of Computer Science at the University of Miami's College of Arts and Sciences. He is also a Fellow of the CNSA. His research focuses on Artificial Intelligence, Robotics, and Semantic Web technologies, with applications in healthcare, criminal justice, and competitive gaming. He leads projects in RoboCup robotics competitions and has developed novel AI tools for cognitive testing (e.g., LASSI-BC) and digital health agents for behavioral interventions. Key research areas include ontology engineering (e.g., Drug Target Ontology, BioAssay Ontology), human-robot interaction, and multi-agent systems. His work integrates symbolic and sub-symbolic AI methods to address real-world challenges such as algorithmic fairness in facial recognition systems and dynamic motion control in humanoid robots. He has secured funding from NIH, NSF, and industry partners, and serves on editorial boards for advanced robotics and AI journals.

• Computer Vision
• Machine Learning
• Robotics
• +3 more

James Hays is affiliated with Brown University . His research spans Computer Vision , Robotics , 3D Reconstruction , and Image Synthesis . His recent work focuses on 3D Object Detection (e.g., CoRL 2024 ), Semantic Scene Flow (e.g., ICLR 2024 ), and Multimodal Image Generation (e.g., CVPR 2024 ). He has contributed to datasets like ContactDB and ContactPose for robotic grasp analysis. Key trends in his publications include 3D Perception , Efficient Neural Architectures , and Cross-modal Learning for autonomous systems. While no explicit scientific awards are listed, his work is frequently published in top-tier venues like CVPR , ECCV , and CoRR .

• Computer Science
• Artificial Intelligence
• Machine Learning
• +7 more

Professor Adam Prugel-Bennett is a faculty member at the School of Electronics and Computer Science , University of Southampton. His research spans artificial intelligence, machine learning, and robotics, with a focus on 3D perception, explainable AI, and marine data analysis. He contributes to interdisciplinary projects including coastal morphological analysis and underwater robotics. Key research areas include: LiDAR-based 3D object detection Multilevel explainable artificial intelligence Georeferenced seafloor imaging Variational autoencoder applications Bathymetric mapping His recent work explores cross-domain adaptation, sensor fusion, and algorithmic stability. He supervises PhD students in computer science and engineering while collaborating with research groups like Vision, Learning and Control. Current projects involve EPSRC-funded initiatives on complex computational systems and AI-based knowledge exchange for coastal analysis.

• Computer Vision
• Deep Learning
• 3D Point Cloud Processing
• +7 more

Yusuf Hüseyin Şahin is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University, Faculty of Computer and Informatics. He earned all his academic degrees—B.Sc., M.Sc., and Ph.D.—from the same institution in Computer Engineering. His research lies at the intersection of computer vision, deep learning, and 3D data processing, with applications in medical imaging, architectural heritage, and drone-based vision systems. B.Sc., M.Sc., Ph.D. in Computer Engineering, Istanbul Technical University His primary research interests include 3D point cloud processing, deep learning, image segmentation, adversarial attacks, and medical image analysis. He has published extensively on these topics, particularly focusing on point cloud registration, segmentation, and classification using neural networks. His recent work explores uncertainty modeling, active learning, and generative models for both synthetic data creation and real-world applications. The trend in his publications from 2017 to 2024 shows a clear progression from foundational work in CNN-based 3D classification and cerebral vessel analysis to advanced topics such as dynamic graph networks, conformal prediction, and heritage digitization. His work bridges theoretical machine learning with practical applications in healthcare and cultural preservation. He is currently leading a research project titled "Konformal Tahmin ile Sıcaklık Tahmin Modellerinde Doğruluğun Arttırılması" (Improving Temperature Prediction Accuracy Using Conformal Forecasting), funded under the SRP program from 2025 to 2026. This indicates an expanding interest in predictive modeling and uncertainty quantification. While no scientific awards are listed in the provided texts, his h-index of 5 and 293 citations on Scopus reflect an active and growing research profile. Dr. Şahin teaches undergraduate courses such as Data Structures (BLG 223E) and Object-Oriented Programming (BLG 252E). He has no listed advisees or thesis supervision records. He is part of a collaborative research network involving Gozde Unal and other researchers in medical and architectural computer vision. His lab activities appear to focus on deep learning for 3D data, with emphasis on robustness, efficiency, and real-world deployment.

• Artificial Intelligence
• Medical Image Computing
• Computer Vision
• +9 more

Gözde Ünal is a Professor at Istanbul Technical University (ITU) in the Department of Artificial Intelligence and Data Engineering, under the School of Computer and Informatics. She has held leadership roles including Deputy Dean and former Director of the Application and Research Center. She previously served as Associate and Assistant Professor at Sabancı University and taught at Georgia Institute of Technology. Education: Ph.D., Electrical and Computer Engineering, North Carolina State University (1998–2002) M.Sc., Electrical and Electronics Engineering, Bilkent University (1996–1998) B.Sc., Electrical and Electronics Engineering, Middle East Technical University (1992–1996) Her research focuses on medical image computing, computer vision, deep learning, augmented and virtual reality, and AI for healthcare . She specializes in image segmentation, point cloud processing, and surgical scene analysis. Her work bridges computer science with biomedical applications, particularly in MRI, ultrasound, and dermoscopic imaging. She leads interdisciplinary research in both medical and cultural heritage domains. The recent publications (2021–2025) highlight a strong trend in deep learning for medical image segmentation , adversarial attacks on point clouds , AI in surgical skill assessment , and cultural heritage digitization using 3D point clouds . Her work increasingly employs transformers, self-supervised learning, and multimodal fusion, with a focus on robustness and real-world deployment. Scientific Awards: Fakülte Yılın Seçkin Eğitimcisi Ödülü, ITU, 2018 Marie Curie Alumni Association Career Award, 2017 GENÇ BİLİM KADINI, L’Oréal Turkey, 2010 GEBİP, Turkish Academy of Sciences, 2010 She has supervised numerous students and research projects, securing grants from EU and TTO (Technology Transfer Office). Her projects include AI-based optimization in banking, AI for assisted reproduction, deep learning for quality control, and normalization of multimodal brain networks. She leads a dynamic research group working on cutting-edge AI applications. She is actively involved in professional societies: President of the Marie Curie Alumni Association Turkish Chapter, member of MICCAI, IEEE, and TMRD. Her lab focuses on AI-driven solutions in medical imaging and heritage preservation, with strong international collaborations, particularly in Europe.

• Artificial Intelligence
• Biometrics
• Security Systems
• +5 more

Kingsley Nwosu is an Associate Professor in the Department of Computer Science at Norfolk State University's College of Science, Engineering and Technology. His research spans artificial intelligence, biometric systems, and security frameworks, with a focus on healthcare, campus safety, and low-resource environments. Email: kcnwosu@nsu.edu Research Themes: AI-driven emotion recognition and facial recognition systems Biometric authentication for security and privacy Blockchain applications in e-voting and data integrity Urban mobility optimization via embedded systems Public policy evaluation for social enterprises Data partitioning strategies for machine learning Publication Trends: His work since 2012 highlights interdisciplinary applications of AI and biometrics in healthcare, security, and economic development, with recent emphasis on scalable solutions for developing economies and proactive cybersecurity measures.

• Robotic Perception
• Mobile Robotics
• Machine Learning
• +3 more

Cristiano Premebida is an Assistant Professor in the Department of Electrical and Computer Engineering (DEEC) at the University of Coimbra, Portugal. He is also a senior researcher at the Institute of Systems and Robotics (ISR-UC) and holds a PhD in Electrical and Computer Engineering from the University of Coimbra (2012), a Master's degree from the same institution (2007), and a B.Sc. in Electrical Engineering from the State University of Santa Catarina, Brazil (2001). Current Academic Rank: Assistant Professor Affiliation: University of Coimbra His research focuses on robotic perception , mobile robotics , applied machine learning , and autonomous vehicle systems . Key methodologies include sensor fusion , multimodal perception , and 3D LiDAR-based navigation , with applications in precision agriculture, autonomous driving, and industrial automation. Recent publications highlight advancements in deep learning calibration , fuzzy system interpretability , and multimodal human detection . Projects funded by the Fundação para a Ciência e a Tecnologia include the GreenBotics initiative for intelligent agricultural systems and AI+Green for automation in precision agriculture. Collaborations span institutions like MIT Portugal, ISR-UC, and international robotics conferences.

Zhigang DengView profile

Professor

• Human-Centered Computing
• Computer Graphics
• Computer Animation
• +4 more

Zhigang Deng is the Moores Professor of Computer Science and Director of Graduate Studies at the University of Houston's Department of Computer Science. He earned his Ph.D. in Computer Science from the University of Southern California (2006), with earlier degrees from Peking University (M.S. 2004) and Xiamen University (B.S. 1997). His research focuses on Human-Centered Computing, Computer Graphics, and Computational Surgery, with notable contributions to insect flight simulation, surgical telementoring, and AI-human collaboration in medical diagnosis. He has been funded by NSF, NIH, NASA, and industry partners like Google and EA. His work is featured in ACM TOG, IEEE TVCG, and SIGGRAPH. Awards include the ACM Distinguished Member (2021), Moores Professorship (2021), and multiple best paper awards. He directs the University of Houston's Computer Graphics and Interactive Media Lab, and serves on editorial boards for IEEE TVCG and Computer Graphics Forum. Teaching includes courses on 3D Graphics and Game Development. Current research projects include surgical simulation frameworks (HySim), farm scene modeling from satellite data, and multimodal conversation analysis in multi-party interactions.

• Computer Vision
• Computer Graphics
• Computational Photography
• +6 more

Alexei Efros is a Professor of Electrical Engineering and Computer Science at UC Berkeley , affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) . Previously, he was a faculty member at the Robotics Institute, Carnegie Mellon University , and a postdoc at Oxford University with Andrew Zisserman. His work spans data-driven computer vision , self-supervised learning , and applications to computer graphics , computational photography , and human-AI interaction . Research Themes : Self-supervised visual learning 3D scene understanding Vision-language multimodal systems Teaching : CS 180/280A: Intro to Computer Vision CS 280: Graduate Computer Vision CS 294-192: Visual Scene Understanding Recent Publication Trends : Focus on diffusion models and self-guidance 3D perception and rendering Interpretability of vision-language models Temporal and sequential learning Scientific Collaborations : Extensive partnerships with institutions like MIT, CMU, Stanford, and NVIDIA Mentorship of PhD students now at TTIC, OpenAI, Anthropic, and academia Labs & Teams : BAIR Lab (UC Berkeley) Collaborations with Adobe Research, Google, and NVIDIA