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Stefanie Mueller is the TIBCO Career Development Associate Professor at MIT's Electrical Engineering and Computer Science Department, with joint affiliation in Mechanical Engineering. She leads the HCI Engineering Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL), focusing on advancing fabrication techniques through hardware/software innovations that enable novel object interactions. Develops computational fabrication methods combining photochromic dyes, lenticular lenses, birefringent materials, and optical illusions Co-chaired ACM CHI 2023 and ACM UIST 2020 program committees Recipients of 9 MIT EECS Best Undergraduate Researcher Awards among mentees Her research spans four key directions: Appearance-changing Objects: Photo-Chromeleon (ACM UIST 2019), Lenticular Objects (ACM UIST 2021), and Polagons (ACM CHI 2023) demonstrate reprogrammable surfaces through advanced materials and optical engineering. Tracking Systems: InfraredTags (ACM CHI 2022) and G-ID (ACM CHI 2020) enable passive object tracking via infrared markers and slicing artifacts. Embedded Sensing: MechSense (ACM CHI 2023) and Sprayable User Interfaces (ACM CHI 2020) integrate sensing capabilities into complex geometries. Curved Surface Prototyping: FlexBoard (ACM CHI 2023) and CurveBoard (ACM CHI 2020) develop specialized tools for non-planar electronics. Her recent publications focus on functionality segmentation (UIST 2023), fluorescent markers (UIST 2023), and machine-knitted haptics (UIST 2023). These works combine machine learning, material science, and interactive design principles to push fabrication boundaries. Scientific recognition includes: 2022 MIT Technology Review Innovators Under 35 2020 Microsoft Research Faculty Fellowship 2020 Alfred P. Sloan Research Fellowship 2019 ACM UIST Best Paper Award 2019 NSF CAREER Award 2018 MIT EECS Outstanding Educator Award 2017 Forbes 30 Under 30 in Science Mentoring 9 PhD students and over 20 master's students, her lab has produced 20+ publications at top HCI conferences. She redesigned MIT's 6.810 Engineering Interactive Technologies course during the pandemic, maintaining hands-on learning through home electronics kits and Slack-based collaboration.

• Human-Computer Interaction
• Computational Fabrication
• 3D Printing
• +7 more

Dr. Benjamin Busam is a Senior Research Scientist at the Technical University of Munich , affiliated with the Chair for Computer Science Applications in Medicine under Prof. Nassir Navab. Starting September 2025, he will hold the Professorship for Photogrammetry and Remote Sensing at TUM. His career includes leadership roles at FRAMOS Imaging Systems and Huawei Research in London. Education: Mathematics (TUM), Mathematics and Physics (ParisTech, University of Melbourne), PhD in Mathematics (TUM, 2014) His research focuses on 3D computer vision , multi-modal sensor fusion , and their applications in collaborative robotics and augmented reality . He specializes in projective geometry , 6D pose estimation , and neural radiance fields , with a particular emphasis on photometrically challenging environments. Recent publications highlight advancements in 3D scene understanding , neural rendering , and medical imaging , often leveraging machine learning and vision-language models . His work has been recognized through awards like the EMVA Young Professional Award (2015) and Innovation Pioneer of the Year (2019) , along with multiple Outstanding Reviewer distinctions at leading conferences. Dr. Busam has supervised numerous PhD and MSc students on topics including 6D pose estimation , medical augmented reality , and robotic ultrasound , collaborating with institutions like MIT , École Polytechnique , and University of Padova .

• 3D Computer Vision
• Multi-Modal Sensor Fusion
• Collaborative Robotics
• +6 more

Kede Ma is an Associate Professor in the Department of Computer Science at City University of Hong Kong (CityUHK). He received his B.E. from the University of Science and Technology of China (USTC) in 2012, and MASc and Ph.D. degrees from the University of Waterloo in 2014 and 2017, respectively. From 2018 to 2019, he was a Research Associate with the Howard Hughes Medical Institute and New York University. Prof. Ma has been named to the Highly Cited Researchers list by Clarivate Analytics in 2024 and currently serves on the editorial boards of IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, and IEEE Signal Processing Letters. Prof. Ma leads the Multimedia Analytics (MA) Laboratory, an interdisciplinary research group focused on computational vision, computational modeling of human visual perception, perceptual multimedia signal processing, quality assessment, and multimedia forensics. His research spans computational photography, high dynamic range imaging and rendering, omnidirectional video analysis, camera processing pipeline design, and artificial intelligence safety in multimedia systems. His work integrates machine learning techniques including reinforcement learning, generative modeling, self-supervised learning, and continual learning for multimedia signal processing applications. His recent publications demonstrate a strong focus on image quality assessment, deep learning for multimedia processing, and multimedia forensics. His work bridges theoretical computer vision principles with practical applications in multimedia systems. The research trends show increasing integration of foundation models with specialized multimedia processing tasks, particularly in quality assessment and security applications. Highly Cited Researchers list by Clarivate Analytics (2024) Best Paper Award at IEEE International Conference on Virtual Reality and Visualization (2021) Best Paper Runner-Up at International Joint Conference on Artificial Intelligence Workshop (2021) Top 10% Award at IEEE International Conference on Image Processing (2015) Finalist for the Governor General's Gold Medal, University of Waterloo (2017) Spotlight presentation at NeurIPS (2022) Highlight paper at ICCV (2025) Oral presentation at ICLR (2025) Prof. Ma advises numerous PhD students and postdoctoral fellows in the MA Laboratory. His research is supported by various grants enabling work in multimedia analytics, image processing, and computer vision. The laboratory maintains active collaborations with researchers at institutions including SUSTech, ZJU, and HIT. Current projects focus on advancing image quality assessment methodologies, developing more robust deep learning techniques for multimedia forensics, and exploring new approaches to HDR imaging and omnidirectional video processing. The Multimedia Analytics Laboratory maintains a strong focus on both theoretical foundations and practical applications of multimedia processing. Current research directions include integrating large language models with image quality assessment, developing more robust deepfake detection methods, and advancing techniques for continual learning in multimedia applications. The lab emphasizes rigorous evaluation methodologies and maintains multiple datasets for multimedia quality assessment research.

• Image Quality Assessment
• Computational Photography
• High Dynamic Range Imaging
• +7 more

Thomas Wachtler-Kulla is a Professor in the Department of Biology II at Ludwig-Maximilians-University Munich, where he leads the Computational Neuroscience research group. He is a GSN full member and serves as the GSN Ombudsperson, offering neutral and confidential counseling for students. He is also the Group Leader and Scientific Director at the German Neuroinformatics Node (G-Node), contributing significantly to neuroscience data infrastructure. His research focuses on how the brain processes sensory signals, particularly in the visual system, aiming to understand neural coding, perceptual stability, and color vision under natural conditions. He employs neurophysiology, psychophysics, and computational modeling to investigate sensory processing, eye movement compensation, and efficient coding mechanisms. At G-Node, he develops software and hardware tools for organizing, storing, analyzing, and sharing neurophysiological data, promoting reproducible research. His recent work spans Bayesian models of hue perception, data management frameworks like DataLad and odML, and studies on honeybee neuroethology. He actively supervises graduate students and contributes to major initiatives such as NFDI-Neuro and the International Neuroinformatics Coordinating Facility, advancing standards for open and FAIR neuroscience. Scientific Contributions and Leadership: Scientific Director, German Neuroinformatics Node (G-Node) GSN Ombudsperson for student conflict resolution Key contributor to NFDI-Neuro and INCF Developer of tools for metadata management and data sharing He advises several current and former graduate students and is deeply involved in shaping data policies and infrastructure for the neuroscience community, ensuring scientific rigor and accessibility.

• Computational Neuroscience
• Visual System
• Sensory Coding
• +7 more

Prof. Dr.-Ing. Reimar Lenz is an Associate Professor at the Technical University of Munich (TUM) within the TUM School of Computation, Information and Technology. His research focuses on digital image acquisition, cooled cameras for microscopy, color image reconstruction, and videometry. He founded CCD Videometrie GmbH in 1999 and co-developed the 'Arriscan' film scanner, earning a Technical Oscar in 2010. Education: Studied electrical engineering at Technical University of Stuttgart and TUM (diploma 1980). PhD in 1986, habilitation in videometry/image processing (1989). IBM postdoc (1987-1988). Key achievements include the microscanning patent (1990), high-resolution museum cameras (MARC project), and CMOS sensor innovations. Awards include the Academy Scientific & Engineering Award (2010) and Heinz Maier-Leibnitz Medal. Manages CCD Videometrie GmbH and holds adjunct roles. Active in both academia and industry, bridging sensor technology and digital imaging applications.

• Digital Image Processing
• Microscopy Technology
• Videometry
• +3 more

Karl R. Gegenfurtner is a Professor of General Psychology at the Department of Psychology, Justus Liebig University Giessen. His research focuses on information processing in the visual system, particularly the interplay between low-level sensory processes, high-level visual cognition, and sensorimotor integration. He investigates how complex scenes are perceived, represented in the brain, and used to drive motor systems, with a specialization in color perception, material property recognition, and eye movement dynamics. Ph.D. in Experimental Psychology, New York University (1990) Diploma in Psychology, University of Regensburg (1986) Habilitation in Medical Psychology and Behavioral Neurobiology, University of Tübingen (1998) His work bridges visual neuroscience with computational modeling, examining color categorization in neural networks, cortical mechanisms of color vision, and dynamic recalibration of visual perception during eye movements. Recent projects include Color 3.0: An object-oriented approach to color (ERC Advanced Grant) and Dynamics in Vision and Touch (Marie Curie Actions). Publications highlight advancements in understanding saccadic suppression, predictive eye movements, and chromatic adaptation timelines. Scientific awards include the Wilhelm Wundt Medal (2016), Rank Prize Funds Lecture (2014), and ERC Advanced Grant (2020–2025). He has served on editorial boards of Journal of Vision , Vision Research , and Perception , and led initiatives like the Neuroscientific Workflow Assistance (NOWA) project. Collaborations span institutions in Germany, the U.S., Australia, and the U.K., with a focus on perception-action loops and neural mechanisms underlying visual stability.

• Visual Perception
• Color Vision
• Sensorimotor Integration
• +4 more

Dongheui Lee is an Assistant Professor at the Institute of Automatic Control Engineering (LSR) within the Faculty of Electrical Engineering and Information Technology at Technische Universität München (TUM). She leads the Dynamic Human Robot Interaction for Automation System Lab. Her research focuses on human motion understanding, physical human-robot interaction, and machine learning in robotics. Education: B.S. and M.S. in Mechanical Engineering from Kyunghee University (2001-2003), PhD in Mechano-Informatics from the University of Tokyo (2007). Prior roles include research scientist at KIST Korea (2001-2004) and project assistant professor at the University of Tokyo (2007-2009). Research Interests: Human-robot collaboration, probabilistic robotics, motion recognition, and incremental lifelong learning mechanisms. She has contributed to advancements in motion primitives, compliant physical interaction, and real-time object tracking. Selected Awards: Finalist for KUKA Service Robotics Best Paper Award (2009), Hirose Scholarship (2006-2007), and multiple grants from KRF, KOSEF, and international robotics competitions. Key Publications: Focus on prioritized inverse kinematics, motion imitation, and adaptive control systems. Her work bridges robotics theory and practical applications in humanoid robots and human-robot interaction.

• Human motion understanding
• Physical human-robot interaction
• Machine learning in robotics
• +5 more

Jens Herder is a Professor of Virtual Studio/Virtual Reality at the Düsseldorf University of Applied Sciences since 2000. As former Dean of the Faculty of Media , he pioneered the Bachelor's in Media and Applied Information Technology and Master's in Virtual Reality programs. He founded the Journal of Virtual Reality and Broadcasting and leads research at the Virtual Sets and Virtual Environments Laboratory . Studied Computer Science with Architecture at TU Darmstadt PhD in Engineering from University of Tsukuba (1999) His research spans Virtual/Augmented/Mixed Reality , Spatial Media , and Interactive Environments . Key projects include: Sound Spatialization Framework development 3D Monument digitization with ethical participation Augmented Reality lighting control systems Volumetric capture integration for virtual teleportation He supervises 6+ graduate theses annually, focusing on: Real-time virtual production Photorealistic rendering Immersive media workflows AR/VR hardware integration Current affiliations: Head of Virtual Studio/VR Laboratory Internationalization Commissioner for Faculty of Media

• Virtual Reality
• Augmented Reality
• Mixed Reality
• +5 more

Arash Akbarinia is a researcher at the School of Psychology and Sports Science at Justus Liebig University Giessen. His work operates at the intersection of biological and artificial neural networks , focusing on visual information processing and the psychophysics of perception. Primary research areas include computational modeling of vision, contrast sensitivity, color categorization, and haptic saliency Active in developing deep learning frameworks for sensory processing analysis Recent publications highlight collaborations with Prof. Karl Gegenfurtner and colleagues, exploring phenomena like color constancy , gloss perception , and haptic stimulus modeling through deep neural architectures. His repository contributions include tools for analyzing sensorimotor processing and tactile perception using PyTorch implementations.

• Computational Neuroscience
• Visual Perception
• Machine Learning
• +6 more

Markus Richter serves as Professor of Horticultural Plant Production and Production Technology at the Berlin University of Applied Sciences (BHT), within Department V - Life Sciences and Technology. His academic career combines extensive industry experience with scholarly research, focusing on the practical application of botanical knowledge to solve horticultural challenges. Since joining BHT in 2008, he has established himself as a specialist in ornamental plant cultivation technology and precision irrigation systems. Prof. Richter earned his foundational education as a Horticultural Engineer from the Technical University of Applied Sciences Berlin (1987-1990), followed by an MSc in Technology of Crop Protection from the University of Reading (1990-1991). He completed his doctorate (Dr. rer. hort) at Humboldt University of Berlin between 1996 and 2001. Prior to his academic appointment, he gained valuable industry experience as a test engineer at the Chamber of Agriculture Westphalia-Lippe (1991-2003), and later as Head of the Horticultural Research Center in Münster-Wolbeck (2003-2006) and Head of ornamental plant trials at GBZ Straelen/Cologne-Auweiler (2006-2007). His research interests center on ornamental plant cultivation , technology in horticulture , and irrigation systems , with a particular emphasis on developing sensor-based solutions for resource-efficient plant production. Prof. Richter has pioneered work in photogrammetric monitoring systems for irrigation management, with applications spanning both greenhouse and field production environments. His expertise bridges plant physiology, engineering, and practical horticulture, addressing critical challenges in water conservation and plant health monitoring. Analysis of his recent publications reveals a dominant focus on the PLANTSENS research initiative, which has evolved through multiple phases (2017-2023). This work demonstrates a consistent trajectory toward increasingly sophisticated multi-sensor systems for detecting plant water stress and automating irrigation. His research spans agricultural engineering, plant physiology, and computer vision, with applications in both ornamental and vegetable production systems. A secondary research thread examines nutrient management issues in specialty crops like Helleborus and Hydrangea. At BHT, Prof. Richter actively supervises student theses on topics including sensor systems for irrigation management, growth control of ornamental plants, and sustainable production methods. His research has been supported through multiple projects, including the PLANTSENS project (2017-2020), PlantSens II (2020-2023), and ongoing doctoral research on Helleborus cultivation. He serves on the Training Commission and Audit Committee, and acts as an academic advisor and officer for recognition of academic achievements.

• Ornamental plant cultivation
• Technology in horticulture
• Irrigation systems
• +4 more

Dr. Mahdi Jampour is a Researcher at the Centre for the Study of Manuscript Cultures (CSMC), University of Hamburg, and a member of the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA). He holds a Ph.D. in Computer Science (Artificial Intelligence) from Graz University of Technology (2016), with postdoctoral research at Iran Telecommunication Research Center (ITRC) (2016–2017). He previously served as Assistant Professor at Quchan University of Technology (2017–2024) and led Project RFA05 (2022–2025) focusing on visual pattern similarity in written artefacts. Education: Ph.D. in Computer Science (Artificial Intelligence), TU Graz, Austria (2016) Postdoctoral Fellowship, ITRC, Iran (2016–2017) Assistant Professor, Quchan University of Technology (2017–2024) Research Interests: Dr. Jampour specializes in applying AI and computer vision to cultural heritage preservation, including palimpsest analysis, historical document digitization, and pattern recognition. His work integrates generative models, deep learning, and semi-supervised methods to address challenges in manuscript analysis and multispectral imaging. Publications Trends: Recent work focuses on generative AI for palimpsest deciphering, dataset creation for sports and cultural heritage analysis, and facial expression recognition surveys. His articles bridge computer science with digital humanities, emphasizing cultural artifact preservation through technological innovation. Awards: Kazemi-Ashtiani Award (2019) Chamran Award (2017) KUWI Prize (2015) Marshal Plan Fellowship (2015) Best MSc Thesis Award (2009) Advising & Grants: Led UWA’s Project RFA05 (2022–2025) and contributed to international preservation initiatives like the Timbuktu Manuscript Training Project. His research is supported by grants from the Iran National Elites Foundation and the Iranian Ministry of Science. Labs & Collaborations: Active in CSMC’s labs, including the Written Artefact Profiling Guide and Mobile Lab Container projects. Collaborates with institutions globally on digitization and cultural heritage safeguarding.

• Computer Vision
• Artificial Intelligence
• Digital Humanities
• +3 more

Prof. Dr. Karl R. Gegenfurtner is a full Professor for General Psychology at the Department of Psychology, Faculty of Psychology and Sports Science, Justus-Liebig-University Giessen. He has held this position since 2001 and leads a prominent research group on visual perception. His work bridges low-level sensory processing with higher cognitive functions and motor control. Education: Psychology Student at the University of Regensburg, Diploma in Psychology, 1986 Ph.D. in Experimental Psychology, New York University, 1990 Postdoc at Howard Hughes Medical Institute and Center for Neural Science, NYU, 1990–1993 Research Scientist at Max Planck Institute for Biological Cybernetics, Tübingen, 1993–2000 Habilitation in Medical Psychology and Behavioral Neurobiology, 1998 Professor for Biological Psychology, Otto-von-Guericke University Magdeburg, 2000–2001 His research focuses on the neural and cognitive mechanisms of visual perception, particularly color vision, object recognition, eye movements, and sensorimotor integration. He investigates how humans perceive complex scenes and objects in natural environments, how these are represented in the brain, and how visual information guides motor actions. His recent work explores topics such as color categorization in neural networks, lightness perception, dynamic size recalibration, and the role of eye movements in perceptual decisions. The 15 most recent articles reflect a strong trend toward understanding perception in real-world contexts, integrating computational modeling, psychophysics, and neuroscientific methods. Key themes include color and shape perception, attention, eye movement control, and the interplay between perception and action. Scientific Awards: Member, German National Academy of Sciences Leopoldina (2015) Wilhelm Wundt Medal, German Society for Psychology (2016) Palmer Lecture, Colour Group (UK) (2019) Turrell Lecture Berlin (2019) Russell Devalois Memorial Lecture, UC Berkeley (2024) ICVS Verriest Medal (2024) Pineapple Science Award (2024) Prof. Gegenfurtner has supervised numerous research projects and training networks, including the DFG Collaborative Research Center TRR 135 on 'Cardinal mechanisms of perception' and the International Research Training Group BrainAct. He has received major funding, including an ERC Advanced Grant (2020) for 'Color 3.0'. He has served on editorial boards of top journals such as Journal of Vision , Vision Research , and Psychological Review , and was President of the Vision Science Society (2012–2013). He is actively involved in academic service, including the Alexander von Humboldt Foundation’s fellowship selection committee. He leads the Visual Perception research group at Giessen, which investigates cortical mechanisms of vision, perception of natural scenes, and the integration of sensory and motor information. The lab employs psychophysical experiments, eye tracking, computational modeling, and neuroimaging to study perception in ecologically valid settings.

• Visual Perception
• Color Vision
• Cognitive Neuroscience
• +7 more

Ralf Salomon is a Professor at the Faculty of Computer Science and Electrical Engineering , University of Rostock , Germany. He holds the title of Prof. Dr.-Ing. habil. and is actively involved in research and teaching in embedded systems, localization, and signal processing. University: University of Rostock School: Faculty of Computer Science and Electrical Engineering Position: Full Professor Contact: Room 201, Tel: +49 381 498 7260 His research focuses on high-precision time measurement , ambient assisted living , industrial embedded systems , and localization technologies . He applies bio-inspired principles, particularly from the barn owl auditory system, to develop low-cost, high-accuracy systems. His work spans health monitoring (e.g., fall detection, cochlear implants), sports technology, and animal experimentation systems. The recent publications (2020–2025) show a strong trend in sensor systems , embedded vision , and real-time measurement , with applications in sports, healthcare, and automation. His work combines theoretical innovation with practical implementation, often using FPGAs and microcontrollers. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He advises students such as Theo Gabloffsky , who co-authors multiple publications and assists in teaching. He has led multiple funded projects including Welisa (DFG Graduate College) , iHeal , CurlDat , and BOUNCE . Many of his projects focus on health, localization, and embedded intelligence. Labs and Teams: He collaborates closely with researchers like Ralf Joost , Matthias Hinkfoth , Gerald Bieber , and Marian Lüder . His team develops FPGA-based systems, embedded sensors, and intelligent algorithms for real-world applications in health, industry, and sports.

• Embedded Systems
• Localization and Positioning
• Time Interval Measurement
• +12 more

Prof. Yu Kang is a Professor of Precision Agriculture at the TUM School of Life Sciences, Technische Universität München (TUM). His research focuses on integrating imaging, sensing, and computational methods to study plant-environment interactions. He aims to enhance resource efficiency and reduce environmental impact through precision crop management. Prior to TUM, he held positions at China Agricultural University (CAU) and conducted postdoctoral research at ETH Zurich and KU Leuven. Prof. Yu's career includes roles such as Associate Professor of Crop Science at CAU and postdoctoral fellowships in physical geography. His educational background includes a doctoral degree from the University of Cologne (2014) and undergraduate studies at China Agricultural University. Key research areas include remote sensing for crop health monitoring, hyperspectral imaging for disease detection, and machine learning applications in agriculture. His work has led to innovations in crop nitrogen management and precision phenotyping. Awards include the Innovation Team Award (2019) from the Crop Science Society of China and the GSGS Fellowship (2014) from the University of Cologne.

• Precision Agriculture
• Remote Sensing
• Plant-Environment Interactions
• +3 more

Prof. Dr.-Ing. Torsten Zesch serves as Deputy Scientific Director, Member of the Executive Board, and Head of the Research Professorship for Computational Linguistics at FernUniversität in Hagen since March 2022. He previously held W2 and W1 Professorships for Sprachtechnologie (Language Technology) at Universität Duisburg-Essen from 2014-2022. Zesch is also Spokesperson of the Advisory Board of the German Society for Computational Linguistics and Language Technology (GSCL) since 2024, having previously served as GSCL President from 2018-2024. Dr. Zesch completed his dissertation (Dr.-Ing.) in Computer Science at Technische Universität Darmstadt in 2009. His research spans robust language processing systems, analysis of non-standard language structures, and educational applications of language technology, with particular focus on automatic content scoring and spelling error correction for learner language. Zesch's recent publications reveal a strong focus on integrating language technology with educational applications. His work demonstrates expertise in developing practical NLP solutions for educational assessment, including transformer-based spelling error feedback systems, hierarchical automatic scoring methods, and leveraging LLMs for educational applications despite cold-start problems. He also maintains significant research in hate speech detection, particularly in multimodal contexts and visio-linguistic models. As leader of the Computational Linguistics research professorship within the CATALPA research center, Zesch directs a team investigating how language technology can support educational processes. His projects DAKODA and KISS-Pro focus on language acquisition and educational applications. He has contributed significantly to the field through numerous publications in top NLP and educational technology venues, with over 100 publications spanning from 2006 to present.

• Computational Linguistics
• Natural Language Processing
• Educational Technology
• +6 more