Jose Maria Armingol Moreno is a Full Professor at the University Carlos III of Madrid, affiliated with the Department of Systems Engineering and Automation. He serves as Director of the Master's Degree in Internet of Things: Applied Technologies and leads the Intelligent Systems Laboratory research group within the Duque de Santomauro Institute of Motor Vehicle Safety. His expertise spans autonomous systems, computer vision, robotics, and intelligent transportation infrastructure. Key research interests include vehicle safety, sensor fusion, and deep learning applications in traffic monitoring and autonomous navigation. He has authored over 100 peer-reviewed articles, with recent work focusing on 3D vehicle detection, UAV battery systems, and intelligent infrastructure for smart cities. Dr. Armingol has also led numerous projects on autonomous vehicle development, cooperative driving systems, and traffic safety technologies. His contributions include patented innovations in vehicle inspection and collision avoidance systems.
Dr. Jong Kwan "Jake" Lee is an Associate Professor and Department Chair of the Computer Science Department at Bowling Green State University in Ohio, with expertise in visualization, computer vision, and machine learning. He holds a Ph.D. and M.S. in Computer Science from the University of Alabama in Huntsville and a B.Eng. from Kyungpook National University, Korea. Research Interests : Deep learning-based solar physics visualization Computer vision and pattern recognition High-performance computing applications Machine learning for business and engineering problems Computer graphics and multimedia Teaching : He teaches courses ranging from operating systems to advanced computer graphics and artificial intelligence, with a focus on both undergraduate and graduate levels. His research publications reflect trends in solar feature analysis, GPU acceleration, and encrypted data management. Service : Dr. Lee has held leadership roles including department chair and served on university-wide committees for curriculum development and diversity. He has advised over 30 graduate and undergraduate students in computer science and data science.
Dr. Hao Liu is a Researcher in the Department of Tissue Technology and Biofabrication at ETH Zurich's Institute for Biomechanics. His work intersects tissue engineering, biofabrication, and advanced computational techniques such as computer vision and 3D reconstruction. He focuses on interdisciplinary applications, including robotics and neural radiance fields (NeRF) for biomedical and robotic systems. Institution: ETH Zurich Role: Researcher (Staff of Professorship for Tissue Engineering and Biofabrication) Research interests span tissue engineering, biofabrication, and computational methods like 3D reconstruction, visual localization, and neural networks. His recent work emphasizes robust 3D scene understanding, camera pose estimation, and deep learning for multi-view stereo. Publications highlight advancements in relative pose estimation, neural radiance fields, and line-based geometric analysis, contributing to both robotics and biomedical engineering fields. He has no listed scientific awards, but his research demonstrates significant innovation in computational imaging and biofabrication. No advising or grant details are provided in the available texts. His affiliation with the Institute for Biomechanics suggests involvement in labs focused on biomechanical systems and biofabrication technologies.
José Miguel Salles Dias is a researcher at the University of Lisbon, affiliated with the Instituto Superior Técnico and INESC-ID. His work spans speech recognition, human-computer interaction, virtual reality, and machine learning, with a focus on assistive technologies for the elderly, urban mobility analysis, and privacy-preserving systems. Key Research Areas: Speech recognition for elderly users, multimodal interfaces, virtual reality environments, and urban transport analytics. Notable Contributions: Development of gesture-based interaction systems, energy consumption prediction models, and speech interface tools for minority languages. His publications reflect trends in integrating machine learning with real-world applications, including health data systems, bike-sharing optimization, and immersive VR environments. While no explicit awards are listed, his collaborations with institutions like INESC-ID and participation in conferences like INTERSPEECH and Eurographics highlight his academic engagement.
Dr. Eric Brachmann is a Researcher at Heidelberg University 's Visual Learning Lab (since 2017) and a Guest at Leibniz University Hannover (since 2019). He earned his Dr. rer. nat. in 2018 from TU Dresden (summa cum laude), preceded by a Diplom in media computer science (2012) and studies (2006–2012) at TU Dresden. Doctorate: TU Dresden (2018, summa cum laude) Diplom: TU Dresden (2012, passed with distinction) Education: Media and computer science (2006–2012) His research focuses on Computer Vision and Machine Learning , particularly 6D object pose estimation , camera localization , and neural-guided optimization . His work bridges classical geometric methods (e.g., RANSAC) with modern deep learning techniques, including differentiable optimization and reinforcement learning for pose estimation. His publications emphasize end-to-end learning , robust model fitting , and RGB-D image analysis , with applications in robotics and 3D scene understanding. Key contributions include DSAC, CONSAC, and neural extensions of RANSAC for efficient hypothesis sampling. 2018 : GI Dissertation Award nomination 2014 : ACCV Honorable Mention Demo Award 2012 : Enno Heidebroek Award for top graduate 2008–2012 : German National Academic Foundation scholarship 2008 : IBM Award for intermediate diploma As a co-organizer of ICCV and ECCV workshops, Eric drives collaboration in visual localization and 6D pose estimation . He has reviewed for CVPR, ICCV, NeurIPS, and TPAMI, earning recognition as an Outstanding Reviewer (CVPR 19, NeurIPS 19). He has held industry roles at IBM (2010–2011) and T-Systems (2008–2009). At TU Dresden and Heidelberg, he taught courses on computer vision and 3D world reconstruction , supervised theses, and developed practical seminars.
Romain Vuillemot is an Assistant Professor in Computer Science at École Centrale de Lyon, affiliated with the LIRIS Laboratory (CNRS UMR 5205). He holds an Habilitation (HDR) in Interactive Data Visualization from École Centrale de Lyon (2024) and specializes in data visualization, sports analytics, and video tracking systems. His research develops novel visualization techniques for sports analytics, particularly in table tennis and swimming. His current projects focus on real-time embedded visualization in sports videos and developing advanced tracking systems for athletic performance analysis. Vuillemot created benchmark datasets for swimmers detection and tracking, and developed visualization tools for robotics and deep learning applications. His publications demonstrate increasing focus on sports video analytics and multimedia datasets for computer vision applications. He teaches courses in algorithms, object-oriented programming, web development, and interactive data visualization. He has organized scientific events including the Sciences 2024 Challenge at École Centrale de Lyon.
Koloud Al Khamaiseh is an Assistant Teaching Professor in the Department of Computer Science at Michigan Technological University. She holds a Ph.D. in Electrical and Computer Engineering from Western Michigan University (2023), an M.Sc. in Computer Engineering from Jordan University of Science and Technology (2010), and a B.Sc. in Computer Engineering from Mutah University (2006). She has taught extensively at both Western Michigan University and Tafila Technical University, covering courses such as Data Communications, Digital Logic Design, Computer Organization, Microprocessor Systems, and Object-Oriented Programming. Her research focuses on interdisciplinary applications of computer science, with primary interests in: Machine Learning for Medical Image Processing Cybersecurity for healthcare systems HTML Parsing and Information Retrieval techniques Pattern Recognition methodologies Her work demonstrates consistent innovation in automated surgical assessment systems and cybersecurity optimization. Publication trends highlight her specialization in computer vision applications for surgical training (2021-2024), with earlier foundational work in cybersecurity algorithms (2014-2016). Her most frequent collaborators include Janos Grantner, Ikhlas Abdel-Qader, and Saad Shebrain.
Roles and Affiliations: Sangjin Hong is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University (SUNY). He holds a Ph.D. from the University of Michigan and has prior industry experience at Ford Aerospace, Samsung, and as a research fellow at the University of Michigan. Education: B.S. and M.S. in Electrical Engineering and Computer Science, University of California, Berkeley Ph.D. in Electrical Engineering and Computer Science, University of Michigan, Ann Arbor Research Interests: Focuses on low-power VLSI design for multimedia and wireless communication systems, SOC design methodology, digital signal processing architectures, and hardware-aware system modeling. His work integrates algorithmic innovation with hardware optimization for real-world applications like surveillance, healthcare, and network systems. Teaching: Teaches core courses such as Discrete Mathematics for Engineers (ESE 122) , Digital Signal Processing Architectures (ESE 375) , and System Specification and Modeling (ESE 356/501) . Courses emphasize hands-on projects and SystemC-based system design. Awards and Affiliations: Senior Member of IEEE Member of Eta Kappa Nu and Tau Beta Pi Honor Societies Key Research Themes: Recent articles highlight work in real-time biomedical imaging, machine learning for education, and multi-sensor collaboration in surveillance systems. His publications span over two decades, reflecting expertise in both theoretical and applied aspects of signal processing and system design.
Kadir Durak serves as Assistant Professor in Electrical and Electronics Engineering at Özyeğin University's Faculty of Engineering, specializing in quantum technologies. His research group operates within the Quantum Optics Laboratory focusing on cutting-edge quantum communication and sensing systems. Dr. Durak earned his B.Sc. in Physics from Middle East Technical University (2009) and completed his Ph.D. at National University of Singapore (2015). Following his doctorate, he led a research team at Singapore's Centre for Quantum Technologies developing space-ground quantum key distribution via CubeSat platforms. His research spans quantum cryptography, photonics, and quantum information with emphasis on: Optimization of entangled photon sources for secure communication Quantum key distribution networks (including satellite-based systems) Quantum radar and imaging technologies Single atom-photon interactions in cavity QED systems Quantum random number generation His recent publications demonstrate significant contributions to quantum security mechanisms, noise-tolerant quantum sensing, and practical quantum communication implementations. Notable scientific recognition includes: Bronze Medal for Quantum Cryptography Network invention at Istanbul International Inventions Fair (2019) Accepted publication in IOP Journal of Optics (2021) on vacuum fluctuation-based QRNG Dr. Durak actively recruits graduate students for quantum technology research through fully-funded positions offering tuition waivers, monthly stipends (5000-6000 TL), and research resources. His laboratory collaborates with defense institutions including TÜBİTAK and ASELSAN, with recent demonstrations of quantum radar and entangled photon imaging systems. Current projects focus on quantum communication networks, ultra-cold atom physics, and sub-diffraction limit imaging.
Xosé Manuel Pardo López is an Associate Professor of Software and Computer Systems at the University of Santiago de Compostela (Spain). His research focuses on computer vision, robotics, and machine learning, with significant contributions to visual saliency, object and scene recognition, and robot vision systems. He collaborates extensively with research centers across Spain and internationally, particularly in the fields of computer vision and robotics applications. Dr. Pardo received his PhD in Physics from the University of Santiago de Compostela in 1998, with research focused on 3D medical image analysis. Following his doctoral studies, he completed postdoctoral research at the Computer Vision Center of Barcelona (Spain) and INRIA Sophia Antipolis (France) between 1998 and 2000. His primary research interests span biologically inspired computer vision , visual saliency modeling , object and scene recognition , human activity recognition , and machine learning applications in robotics. Dr. Pardo's work bridges theoretical computer vision with practical applications, particularly in robot vision systems, photogrammetry, and visual inspection technologies. His research has evolved from early work in medical image analysis to current projects focusing on advanced dimensional control systems and damage inspection methodologies for high-impact industrial sectors. Dr. Pardo's recent publications demonstrate a strong trend toward open-world recognition systems , incremental learning approaches , and practical applications of computer vision in robotics . His work increasingly addresses challenges in face verification systems , scene understanding for mobile robots , and 3D scene reconstruction using wireframe models. The research shows a clear trajectory from fundamental visual attention modeling toward applied solutions for industrial and robotic applications. Dr. Pardo has been actively involved in numerous research projects spanning over a decade, including: "Federated and continuous learning from heterogenous data in devices and robots" (2021-2024) "Glocal" and continuous Machine Learning for a society of intelligent devices (2018-2020) Development of new advanced dimensional control systems in manufacturing processes (2011-2014) Development of new generation techniques for damage inspection in aeronautics, railway, naval and wind power sectors (2009-2010) His collaborative research approach is evident in his extensive publication record across leading computer vision and robotics venues. Dr. Pardo has supervised numerous research projects and has been instrumental in developing practical computer vision solutions for industrial applications, particularly in the areas of dimensional metrology and visual inspection systems.
PD Dr. Slobodan Ilic is a Senior Key Expert Research Scientist at Siemens AG (since 2014) and an Adjunct Professor at the Chair of Computer Science Applications in Medicine , Technical University of Munich (TUM). His work bridges 3D computer vision and medical imaging , with a focus on real-time object detection, deformable surface modeling, and depth data analysis. Current Roles: Adjunct Professor at TUM, Senior Key Expert at Siemens Research Themes: 6D pose estimation, non-rigid 3D reconstruction, RGB-D data processing Labs: CAMP Chair (TUM), Siemens AG Research Division His recent work explores LLM-driven control systems , semantic-aware 3D generation , and cross-modal medical imaging . Articles highlight advancements in point cloud registration , rotation-invariant descriptors , and hyperspectral calibration . While no specific awards are documented in the provided text, his team at Siemens/TUM advises PhD candidates in 3D vision for robotics and medical applications .
Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania, where he has been a faculty member since 1998. A distinguished IEEE Fellow, he has held leadership roles such as Director of the GRASP Laboratory (2008–2013), Associate Dean for Graduate Education (2012–2016), and Faculty Director of Online Learning since 2016. His academic journey includes an undergraduate degree in Electrical Engineering from the National Technical University of Athens (1986) and a PhD in Computer Science from the University of Karlsruhe (1992). Education : National Technical University of Athens (BEng), University of Karlsruhe (PhD) Daniilidis is renowned for his contributions to geometric deep learning, data association, event-based cameras, and vision-based manipulation and navigation. His work spans visual odometry, omnidirectional vision, 3D pose estimation, and structure from motion, with a focus on integrating deep learning with geometric principles for real-time applications. His publications highlight advancements in event camera datasets, spherical CNNs for rotation-equivariant representations, and unsupervised motion learning. These works bridge robotics, computer vision, and neural networks, emphasizing dynamic scene analysis and robust perception systems. Scientific Awards : IEEE Fellow Best Conference Paper Award at ICRA 2017 Best Student Paper Finalist at Robotics Science and Systems 2018 Daniilidis has contributed to key academic roles, including Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (2003–2007) and co-chairing conferences like IEEE 3DPVT 2006 and ECCV 2010. He leads research initiatives in the GRASP Laboratory, focusing on cutting-edge robotics and perception technologies.
Frederic Cordier is an Associate Professor (HDR) at the University of Haute-Alsace, affiliated with the LMIA department within the Faculty of Science and Technology (FST). His research focuses on computer graphics, 3D modeling, and geometric algorithms. He holds a PhD in Computer Science from the University of Geneva (2004) and advanced degrees from the University of Lyon. His work spans sketch-based interfaces, cloth simulation, medical modeling, and texture mapping. Key projects include inferring mirror symmetry from sketches, compressing 3D mesh sequences, and reconstructing organ models from medical data. His contributions to real-time cloth simulation and dressed virtual humans have been influential in interactive systems and virtual garment design. Publications emphasize geometric algorithms for shape reconstruction, symmetry detection, and medical applications. He has held visiting roles at KAIST (South Korea) and conducted postdoctoral research in computational geometry. Teaching includes graduate-level computer science courses in Geneva and Haute-Alsace.
Horst Bischof is a Professor at the Institute for Computer Graphics and Vision at Graz University of Technology, Austria, and serves as Vice Rector for Research. He holds an M.S. and Ph.D. from Vienna University of Technology and a Habilitation (venia docendi) in applied computer science. His research focuses on computer vision, medical image processing, and robot vision, with over 750 peer-reviewed publications. He has organized major conferences like CVPR 2015 and ECCV 2018, and serves on editorial boards of prestigious journals. Key awards include the Most Influential Paper over the Decade Award (MVA 2019), Jan Konderink Award (ECCV 2018), and the 29th Pattern Recognition Award (2002). His work spans object recognition, medical computer vision, and visual learning. He leads research teams in robot vision and collaborates with industry partners like Infineon Technologies. Current projects include LiDAR-based sensing systems, autonomous vehicle technologies, and medical imaging solutions like MedEyeTrack for eye tumor treatment. His research emphasizes practical applications in robotics, automotive, and healthcare sectors.
Yuze He is a Research Fellow in the Computer Science Department at Carnegie Mellon University, specializing in infrastructure-supported autonomous driving systems. His research integrates LiDAR technology, real-time mapping, and edge computing to enhance perception and localization for autonomous vehicles. Recent work focuses on high-resolution panoramic LiDAR systems, roadside infrastructure coordination, and efficient 3D mapping solutions. He develops federated learning frameworks for distributed edge platforms and contributes benchmark datasets for traffic analysis.
