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Etienne VougaView profile

Associate Professor

• Physical simulation
• Geometry processing
• Discrete differential geometry
• +4 more

Etienne Vouga is an Associate Professor in the Department of Computer Science and the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin. His interdisciplinary research spans physical simulation, geometry processing, and computer graphics, with applications ranging from special effects in films like The Hobbit and Tangled to Artec's Shapify 3D portrait service. Education: PhD from Columbia Computer Graphics Group Postdoctoral research at Harvard SEAS Professor Vouga's research focuses on physical simulation of thin, elastic materials like cloth, hair, and paper, as they deform and collide. He has made significant contributions to discrete differential geometry, developing algorithms that better understand how geometric properties affect physical behavior of deformable objects. His work bridges computer graphics, computational mechanics, and scientific computing, with applications in special effects, 3D printing, architectural design, and biological modeling. He has pioneered approaches in inverse problems where desired outcomes drive the design process rather than simply simulating physical phenomena. Award Highlights: Best Paper Award at GECCO (2017) for Evolutionary Decomposition for 3D Printing Professor Vouga teaches Computer Graphics: Honors (CS 378H/CS 384G) and Physical Simulation (CS 395T) graduate courses every spring semester. He also serves as head coach of UT's competitive programming teams. His research group has developed several important software tools including Discrete Shell Code for shell simulation and Asynchronous Contact Mechanics Code for robust collision handling. While he is not actively expanding his research group significantly, he remains open to working with exceptional graduate students whose research interests align closely with his work in physical simulation and geometry processing.

Florian WittkämperView profile

Researcher

• Quantum Magnetometry
• Optically Pumped Magnetometers
• Alkali Vapor Cells
• +3 more

Dr. Florian Wittkämper is a Researcher at the Leibniz Institute of Photonic Technology , specializing in quantum magnetometry and sensor development. His work focuses on advancing optically pumped magnetometers (OPMs), microfabricated alkali vapor cells, and plasmonic coatings for space applications. Current research explores vector magnetometry with strong bias fields Innovations in omnidirectional magnetic field sensitivity Passivated gold mirror integration for improved OPM performance Plasmonic coatings for stray-light reduction in space environments His recent publications demonstrate expertise in addressing heading errors through nonlinear Zeeman effects and light shift compensation, while developing robust materials for quantum sensing systems. Scientific awards and educational background information were not found in the provided texts.

• Machine Translation
• Machine Learning
• Data Fusion
• +3 more

Yusuf Can Semerci is an Assistant Professor at the Faculty of Science and Engineering, Maastricht University, specializing in the Department of Advanced Computing Sciences. His research bridges machine translation, artificial intelligence, and healthcare applications, with a focus on context-aware systems and data fusion. Key research areas: Machine Translation, AI for Neurodegenerative Diseases, Cognitive Games Current projects: JOINclusion (social inclusion games), PROCare4Life (integrated care systems) Methodologies: Context-aware neural networks, adapter-based translation strategies, physiological signal analysis His recent work explores attention mechanisms in translation models, cognitive game design for Parkinson’s and Alzheimer’s patients, and sensor-to-interaction AI frameworks. Publications span top conferences like COLING and journals in digital health. Collaborations include interdisciplinary studies with medical and educational teams.

Ulrich HusemannView profile

Professor

• Experimental Particle Physics
• CMS Experiment
• Top Quark Physics
• +6 more

Prof. Dr. Ulrich Husemann is a Professor of Experimental Particle Physics at the Institute of Experimental Particle Physics (ETP) of the Karlsruhe Institute of Technology (KIT) . He leads research in CERN's CMS experiment , focusing on top quark physics , Higgs boson coupling measurements , and dark matter detection using machine learning techniques. His work also explores medical applications of semiconductor detectors for radiotherapy. 2024: Co-convener of the Physics Preparatory Group for the European Strategy for Particle Physics 2024: Organizer of the German Physical Society Spring Meeting on Particle Physics 2023: KIT Senate member 2021–2023: Vice Dean at KIT Department of Physics His research spans detector system development and data analysis for hadron colliders, including contributions to the CMS Phase-1 Pixel Detector Upgrade (2017) and Phase-2 Silicon Strip Tracker for the High-Luminosity LHC. Medical applications include evaluating HV-CMOS technology for beam monitoring in ion therapy with Heidelberg Ion Beam Therapy Center. Recent publications focus on ttH/tH production rates (2025), ttbb cross-section measurements (2024), and machine learning integration in particle physics (2024). Earlier works (2005–2018) address rare top quark decays, silicon detector radiation effects, and LHC/HL-LHC sensitivity projections. He teaches courses on Computer-Guided Data Analysis , Accelerators and Detectors , and Modern Experimental Physics . As of spring 2025, he has supervised 20 doctoral theses, 50 Master's theses, and over 90 Bachelor's theses. Contact: ulrich.husemann@kit.edu | Office: Building 401, Room 407 (Campus North) and Building 30.23, Room 8-19 (Campus South).

Bodo RosenhahnView profile

Professor

• Computer Vision
• Machine Learning
• Big Data Analysis
• +7 more

Bodo Rosenhahn is a Full Professor at Leibniz University Hannover, heading the Institute for Information Processing since September 2008. His research focuses on automated image interpretation with profound expertise in Computer Vision, Machine Learning, and Big Data Analysis. He has established himself as a leading researcher through extensive contributions to the field and successful industry transfer of his work. Rosenhahn received his Computer Science education at the University of Kiel, earning his Dipl.-Inf. in 1999 and Dr.-Ing. in 2003. His academic journey included a postdoctoral position at the University of Auckland (2003-2005), funded by the German Research Foundation, followed by senior researcher work at the Max-Planck Institute for Informatics in Saarbruecken (2005-2008). His research interests span multiple cutting-edge areas including Computer Vision, Machine Learning, 3D Human Pose Estimation, Motion Capture, Object Tracking, Anomaly Detection, and Reinforcement Learning. His work bridges theoretical foundations with practical applications, particularly in medical imaging, autonomous systems, and industrial quality control. The group he leads has developed innovative approaches for video-based motion capture, semantic scene analysis, and multi-object tracking that have achieved state-of-the-art results in numerous challenges. His most recent publications demonstrate strong trends toward explainable AI systems, uncertainty quantification in vision models, robust multi-model fitting techniques, and the integration of quantum principles with machine learning. These works reflect his commitment to developing both theoretically sound and practically applicable computer vision solutions that address real-world challenges in industry and medicine. DAGM-Prize 2002 Dr.-Ing. Siegfried Werth Prize 2003 DAGM-Main Prize 2005 ERC-Starting Grant 2011 (EUR 1.43 million) CVPR 2017 Multi-Object Tracking Challenge PhysRev-A Editors Suggestion 2023 TÜV-Süd Innovation award 2018 As head coach of the LUH AI competition team, Rosenhahn has mentored numerous students who have achieved success in international competitions. His research has been supported by prestigious grants including the ERC Starting Grant and POC Grant. He has also received the Erskine Fellowship for research at the University of Canterbury. Since 2023, he serves as associate editor for IEEE TPAMI, the highest-ranked journal in computer science. Rosenhahn leads a vibrant research group focused on automated image interpretation with multiple ongoing projects including Multiple People Tracking, Relational Object Tracking, Physics-based modeling, Video-based Motion Capture, and Quantum Learning. His group has developed significant datasets such as the Multimodal Motion Capture Indoor Dataset (MPI08) and Multimodal Motion Capture Dataset (TNT15) that have become valuable resources for the computer vision community. The group maintains strong industry connections, successfully transferring research into practical applications while continuing to push the boundaries of fundamental research in computer vision and machine learning.

Javier Civera SanchoView profile

Associate Professor

• Computer Vision
• Robotics
• Artificial Intelligence
• +5 more

Javier Civera Sancho is an Associate Professor at the University of Zaragoza, where he serves as Deputy Director of the Institute of Research in Engineering of Aragon (I3A). He is affiliated with the School of Engineering and Architecture in the Department of Computer Science and Systems Engineering, where he leads research in the RoPeRT group (Robotics, Computer Vision and Artificial Intelligence). With a background in Industrial Engineering and a PhD in Systems and Computer Engineering, both from the University of Zaragoza, Civera has established himself as a prominent researcher in computer vision and robotics. Industrial Engineering degree (2004, University of Zaragoza) PhD in Systems and Computer Engineering (2009, University of Zaragoza) Civera's research primarily focuses on computer vision, with special emphasis on SLAM (Simultaneous Localization and Mapping), 3D reconstruction, and spatial artificial intelligence. His work aims to provide computers with human-like visual capabilities, including object recognition, 3D structure estimation, spatial context understanding, and tracking of moving objects. He has maintained a long-standing research line in scene localization and mapping, making significant contributions to visual SLAM methodologies and applications. His publication trends reveal a strong focus on advancing visual SLAM techniques, with recent work exploring neural rendering integration, semantic mapping, robust camera calibration, and applications in challenging environments like agriculture. His research bridges theoretical computer vision with practical robotics applications, showing increasing integration with deep learning approaches while maintaining strong geometric foundations. 2 research sexenios (with last granted for 2012-2017) 2 teaching quinquenios h-index: 21 (Google Scholar) 397 citations in last 5 years (Google Scholar) Civera has directed 2 doctoral theses and is currently supervising 4 PhD students. His research has been supported through various institutional frameworks at the University of Zaragoza, including his role in the I3A which provides critical research infrastructure. He is actively involved in guiding the next generation of researchers in computer vision and robotics, emphasizing both theoretical understanding and practical implementation. As a member of the RoPeRT research group, Civera collaborates with a multidisciplinary team working at the intersection of robotics, computer vision, and artificial intelligence. His work often involves developing systems that integrate multiple sensor modalities and create robust spatial understanding for autonomous agents.

Bing YuView profile

Associate Professor

• Biophotonics
• Optical Imaging
• Cancer Detection
• +4 more

Dr. Bing Yu serves as Associate Professor in the Joint Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin (MCW), directing the Biophotonics Laboratory. His work bridges engineering principles with clinical applications to develop optical solutions for global healthcare challenges. His academic foundation includes: Postdoctoral Associate, Biomedical Engineering, Duke University (2005–2008) Ph.D., Electrical and Computer Engineering, Virginia Tech (2005) M.S., Optoelectronics Technology, University of Electronic Science and Technology of China (1994) B.S., Optoelectronics Technology, University of Electronic Science and Technology of China (1989) Dr. Yu's research centers on biophotonics with emphasis on optical imaging/spectroscopy systems for cancer detection and therapeutic monitoring. His lab pioneers miniature, cost-effective optical devices tailored for resource-limited settings, particularly in cervical cancer screening and breast tumor margin assessment. Key innovations include smartphone-based endoscopes and deep ultraviolet fluorescence imaging systems integrated with machine learning algorithms for real-time diagnostics. Analysis of his 15 most recent publications reveals a dominant focus on intraoperative cancer margin assessment (73% of works), primarily using deep ultraviolet fluorescence and diffuse reflectance spectroscopy. A strong trend toward AI integration is evident, with 60% of 2023-2025 publications incorporating machine/deep learning for diagnostic enhancement. Pediatric airway monitoring represents a secondary research thrust (20% of recent works), addressing critical unmet needs in endotracheal tube positioning. As Principal Investigator, Dr. Yu secures substantial funding through: Way Klingler Research Fellowship (2025-2027): "Rapid optical evaluation of fresh biopsy specimens for cancer" NIH R01 (2022-2026): "Deep learning enabled deep ultraviolet scanning microscopy for breast cancer surgery" MCW Cancer Center grant (2022-2023): Deep-UV algorithm development NIH R21 (2021-2023): Pediatric endotracheal tube monitoring Smart Biophotonics, LLC (2019-2024): Smartphone MicroEndoscope for cervical cancer screening in China The Biophotonics Laboratory operates as an interdisciplinary hub where engineers collaborate with MCW clinicians to translate optical innovations into clinical practice. Current projects emphasize device miniaturization and AI-driven analytics to improve accessibility of cancer diagnostics in low-resource environments, with active partnerships in China for cervical cancer screening implementation.

• Robotics
• Artificial Intelligence
• Machine Learning
• +4 more

Prof. Dr. Berthold Bäuml serves as a full Professor at the Technical University of Munich (TUM) within the TUM School of Computation, Information and Technology, holding the Chair for Learning AI for Dextrous Robots. His research bridges artificial intelligence and robotics to develop advanced manipulation capabilities in humanoid systems, with particular emphasis on tactile sensing and learning-based control architectures. His core research interests span Robotics, Artificial Intelligence, Machine Learning, Dextrous Manipulation, Tactile Sensing, and Reinforcement Learning. Bäuml investigates how robots can autonomously learn complex manipulation skills through tactile feedback, focusing on in-hand manipulation, grasping of unknown objects, and sim-to-real transfer for multi-fingered hands. His work integrates state estimation with reinforcement learning to achieve robust performance in unstructured environments. Analysis of his 2022-2025 publications reveals a concentrated research trajectory in tactile dexterous manipulation, characterized by modular reinforcement learning architectures, shape-conditioned agents, and torque prediction systems. Key trends include the development of time-optimal controllers, self-contained calibration methods, and collision-aware motion planning, all leveraging deep learning for real-time performance in humanoid platforms like Agile Justin.

Yongcan CaoView profile

Professor

• Robotics
• Autonomy
• Artificial Intelligence
• +7 more

Yongcan Cao, Ph.D. , is a Professor and Assistant Chair of Research in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA) , where he also directs the Unmanned Systems Laboratory (USL) . His scholarship spans robotics, autonomy, AI, multi-agent systems, and human-robot interaction, with extensive editorial service for IEEE Transactions on Cybernetics, Automatica, and IEEE Control Systems Letters. Education Ph.D. in Electrical Engineering, Utah State University, August 2010 Research Focus Dr. Cao’s research integrates learning, autonomy, and safety to create trustworthy unmanned systems. Core themes include: Artificial Intelligence & Learning: reinforcement learning, human-guided learning, explainable & ethical AI. Networked Robotics: multi-agent coordination, distributed control, and formation algorithms for UAV swarms. Cyber-Physical Security: risk-aware planning, secure autonomy, and real-world validation on robotic hardware. His lab pursues open, cost-effective platforms—exemplified by the student-led Modi-Fly drone project that won the 2022 Engineering Design Award and was later selected for the DoD NSIN Emerge Accelerator in 2024. Scientific Awards & Honors Microsoft President’s Endowed Fellow Advancing Next-Gen Faculty Leadership Fellow Margie and Bill Klesse Faculty Fellow UTSA President's Distinguished Achievement Award in Research College of Engineering Excellence in Teaching Award & Service Award AFOSR Summer Faculty Fellowship (2016) National Research Council Research Associateship Award (2011) Professional Contributions & Funding Dr. Cao currently chairs the IEEE Technical Committee on Manufacturing Automation and Robotic Control and sits on the IoT Technical Community Steering Committee. His research is supported by AFOSR, NSF, DoD, and industry partners, enabling large-scale student engagement—over 400 visitors have toured USL, and more than 30 industrial and government collaborations have been hosted. Laboratory & Teams The Unmanned Systems Laboratory (USL) under his leadership advances scalable robotic platforms, trusted AI, and security for educational, economic, and societal benefit. A hallmark initiative is the Modi-Fly student group, recognized for creating one of the most cost-effective open-source drone research platforms using 3-D printed components.

Johnathan VotionView profile

Assistant Professor

• Machine Learning
• Robotics
• Control Systems
• +3 more

Johnathan Votion is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), affiliated with the Klesse College of Engineering and Integrated Design. His work spans machine learning, robotics, and control systems, with a focus on multi-agent coordination, sensor networks, and risk-aware optimization. Education: Ph.D. in Electrical Engineering from UTSA (2016) Research trends in his publications emphasize autonomous systems , data reliability , and uncertainty propagation , including applications in UAV trajectory planning , multi-robot collaboration , and nonholonomic control . His work integrates machine learning with engineering systems to address complex, real-world constraints.

• Artificial Intelligence
• Machine Learning
• Data Science
• +4 more

Luís Nunes is an Associate Professor at Iscte – Instituto Universitário de Lisboa, Department of Information Science and Technology (ISTA), since 1997. He is an integrated researcher at ISTAR-Iscte, focusing on Computational Modeling of Systems . His academic journey includes a PhD in Computer Engineering (2006) from the University of Porto, a Master’s in Electrical and Computer Engineering (1997) from the Technical University of Lisbon, and a Computer Science degree (1993) from the University of Lisbon. Luís Nunes’ research spans Artificial Intelligence, Machine Learning, Data Science , and their applications in public administration, hotel revenue management, mobile security , and human activity recognition . His work addresses predictive modeling for booking cancellations, automated security testing of Android apps, and AI-driven public policy analysis. His recent publications emphasize predictive analytics in tourism , malware detection , and human behavior modeling . Projects like ISDAPPP (Principal Investigator) and AIH (Researcher) highlight his focus on AI for public policy and healthcare data standards. He mentors numerous PhD and Master’s students, including Clara Nunes Barrancos and Miguel Lopes Valadares. Luís Nunes actively contributes to teaching, offering courses such as Introduction to Machine Learning , Autonomous Agents , and Big Data in Public Policies . He has coordinated courses like Applied Artificial Intelligence Project at the Master’s level.

Mehmet YildirimogluView profile

Senior Lecturer

• Large-scale traffic modeling
• Dynamic traffic assignment
• Data mining techniques
• +3 more

Mehmet Yildirimoglu is a Senior Lecturer in Transport Engineering at the School of Civil Engineering, University of Queensland, where he has worked since 2016. He completed his PhD at École Polytechnique Fédérale de Lausanne (EPFL) in 2015, following earlier degrees at Middle East Technical University (BSc) and Rutgers University (MSc). His research focuses on large-scale traffic modeling , dynamic traffic assignment , data mining techniques , and real-time traffic management . He employs advanced computational methods to address urban mobility challenges, including congestion mitigation, route guidance systems, and traffic flow optimization. Recent publications analyze congestion pricing , cooperative platooning , and spatio-temporal deep learning for traffic patterns. His work integrates Kalman filtering , MFD (Macroscopic Fundamental Diagram) approaches, and reinforcement learning to improve urban transportation systems. Dr. Yildirimoglu has secured multiple competitive grants including the ARC Discovery Early Career Researcher Award for next-generation city-scale traffic modeling and collaborative projects with iMove CRC and ARC Linkage Programs . He supervises PhD candidates in traffic modeling frameworks, autonomous vehicle safety, and real-time analytics.

David FilliatView profile

Professor

• Robotics
• Perception
• Learning
• +7 more

David Filliat is a Professor at ENSTA Paris within the Computer Science and Systems Engineering Unit (U2IS), where he serves as Director of the Interdisciplinary Center for Defense and Security Studies (CIEDS). He leads research activities in the Autonomous Systems and Robotics team, focusing on advancing robotics technologies for defense and security applications. Dr. Filliat's research spans multiple critical areas in robotics, with particular emphasis on perception and learning problems. His work aims to develop methods that simplify robot usage while enhancing robustness and autonomy. His primary research interests include navigation, mapping, planning, applied learning for multi-modal perception, and reinforcement learning applications to mobile robots, drones, and autonomous vehicles. His research integrates theoretical advancements with practical implementations, addressing challenges in both indoor and outdoor environments. Analysis of his recent publications reveals a strong trajectory in reinforcement learning, perception systems, and navigation technologies. His work demonstrates increasing sophistication in handling uncertainty, developing robust visual control systems, and creating efficient planning algorithms. Notably, his research shows consistent progression from foundational robotics problems toward more complex multi-modal and multi-task systems that integrate various sensory inputs and decision-making processes. As Director of CIEDS, Dr. Filliat oversees interdisciplinary research bridging defense and security studies with technological innovation. His leadership connects theoretical robotics research with practical security applications, fostering collaboration across multiple domains to address complex security challenges through technological solutions.

Jan H.S. BergmanView profile

Researcher

• Antenna Design
• Microwave Engineering
• Wireless Communications
• +5 more

Jan H.S. Bergman is a Doctoral Researcher at Aalto University's Department of Electronics and Nanoengineering, actively contributing to the Ville Viikari Group. His work bridges theoretical antenna design with practical implementation for next-generation wireless systems. Research interests span Antenna Design , Millimeter-wave Systems , and Wireless Communications , with specialized focus on Ka/E-band arrays, wearable device integration, and RFID solutions. Bergman addresses critical challenges like metallic-structure compatibility for smartwatches and scan-range limitations in high-frequency arrays through innovations such as parasitic resonators and 3D-printed metallic elements. Analysis of his 2024-2025 publications reveals a cohesive trajectory toward compact, high-performance RF solutions for 5G/6G and IoT. Key themes include millimeter-wave beamforming techniques, optimization of yield-critical microwave devices using swarm intelligence, and novel coupling mechanisms for chip-antenna integration—demonstrating both theoretical depth and hardware validation. As an early-career researcher, Bergman participates in collaborative projects within Aalto's wireless ecosystem but does not oversee student supervision. His work leverages university resources for advanced prototyping and measurement, including waveguide simulators and millimeter-wave testbeds. The Ville Viikari Group provides a dynamic environment for antenna innovation, where Bergman contributes to cutting-edge developments in array architectures and wearable RF systems through hands-on experimentation and cross-disciplinary teamwork.

• Integrated Sensing and Communication
• Wireless Communications
• Signal Processing
• +6 more

Dr. Fan Jiang serves as an Associate Senior Lecturer at Halmstad University's School of Information Technology, where he leads research at the intersection of wireless communications and localization technologies. His academic profile reflects strong technical expertise in next-generation wireless systems with practical applications in vehicular networks and satellite communications. His research program focuses on integrated sensing and communication (ISAC) frameworks, with particular emphasis on: Massive MIMO and millimeter wave system design Wireless localization/synchronization algorithms Integration of sensing functionalities within communication protocols Applications for vehicular positioning and satellite networks Analysis of his 15 publications from 2022-2024 reveals consistent output in high-impact venues, with growing emphasis on satellite communications and reconfigurable intelligent surfaces. His work demonstrates strong mathematical rigor in channel modeling and algorithm design while addressing practical implementation challenges like synchronization errors and hardware constraints. Dr. Jiang maintains active research collaboration through co-authorship networks and participates in experimental validation efforts, as evidenced by his 2022 field study on 5G mmWave positioning. His technical approach balances theoretical innovation with real-world applicability across multiple wireless domains.