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Stefan MichelView profile

Lecturer

• Aviation security and human factors
• Human-technology interaction
• Perception and cognition
• +4 more

Dr. Stefan Michel is a Lecturer and Research Associate at the School of Applied Psychology of the University of Applied Sciences Northwestern Switzerland (FHNW), where he has been working since August 2008. He serves as program director for the CAS Master's Thesis and Process Integration since 2016 and is affiliated with the Institute for Humans in Complex Systems (MikS). His educational background includes: Doctorate in Psychology from the University of Zurich under Prof. Dr. Wolfgang Marx Studies in psychology, neuropsychology and psychopathology of childhood and adolescence at the University of Zurich Dr. Michel's research focuses on human-technology interaction in security contexts, particularly aviation security screening. His work examines visual inspection strategies, the role of everyday object knowledge in X-ray screening, automation trust, and performance measurement. He investigates how cognitive processes like perception and attention influence security screening effectiveness, with field studies conducted with airport security personnel. His research bridges cognitive psychology with practical security applications, developing evidence-based approaches to improve threat detection and screening efficiency. His publication record shows a consistent focus on aviation security screening, with recent work (2024) examining night work effects on cognitive performance and ultrasound training methods, while earlier publications (2016-2020) extensively investigated baggage screening procedures, automation effects, and training methodologies. His research demonstrates how human factors principles can be applied to optimize security operations. Dr. Michel teaches across multiple educational levels, supervising bachelor's and master's theses while teaching modules on scientific work, experimental psychology, and socio-technical systems. In continuing education, he leads the CAS Master's Thesis program and teaches general psychology modules for career counseling programs. His teaching emphasizes practical application of psychological principles to real-world problems. His research is conducted through the Institute for Humans in Complex Systems, which examines the challenges and opportunities presented by automation and digitalization in human-technology-organization interactions, with particular focus on security applications.

Felix KlotzscheView profile

Researcher

• Neuroscience
• Cognitive Science
• Virtual Reality
• +3 more

Felix Klotzsche is a Research Scientist at the Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, and an affiliated researcher at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin. His work focuses on cognitive neuroscience, with a particular emphasis on short-term memory, attention, and virtual reality (VR) applications in clinical and experimental settings. Education: B.Sc. Psychology & B.A. Philosophy (2009–2015), Universität Regensburg M.Sc. 'Mind and Brain' (Track 'Brain', 2015–2017), Berlin School of Mind and Brain | Humboldt-Universität zu Berlin Research Interests: Neural mechanisms of visual short-term memory EEG analysis in VR environments Clinical VR applications for neurological assessment Emotional arousal decoding via neurophysiological signals 3D face perception and deep encoding models Cardiac-phase modulated perception Publications reflect a focus on integrating VR with neuroscience methodologies, particularly in evaluating spatial memory, threat perception, and emotional processing. Key tools include Python, R, Unity3D, and advanced EEG signal processing. No scientific awards explicitly noted. No listed advisees or grants. Active in the Research Group Mind-Body-Emotion, contributing to labs and teams developing VR-based cognitive assessment tools.

Joke DaemsView profile

Assistant Professor

• Human-Computer Interaction
• Machine Translation
• Empirical Translation Studies
• +6 more

Joke Daems serves as Assistant Professor in Human-Computer Interaction within Empirical Translation & Interpreting Studies at Ghent University. Affiliated with both the EQTIS research team (Empirical and Quantitative Translation and Interpreting Studies) and the LT3 Language and Translation Technology team, their academic career spans over a decade at Ghent University. Current roles include co-supervising major research projects including ArisToCAT (FWO), WiLMa (BOF), and DUAL-T (MSCA). Research focuses on the impact of translation technology across multiple domains: Machine translation integration in literary translation workflows Gender-inclusive translation strategies using adaptive technology Cognitive aspects of post-editing through eye-tracking and keystroke logging L2 writing processes enhanced by machine translation ( WiLMa project ) Bias detection in neural machine translation outputs Recent publication trends reveal a strategic shift toward gender-inclusive language applications (2022-2025), with 40% of recent work addressing gender bias in machine translation. Literary translation technology remains a consistent thread since 2016, now intersecting with ethical AI development concerns. Methodologically, Daems combines advanced process-tracing techniques with corpus linguistics approaches. Awarded the CIUTI PhD Award in 2017, Daems has also demonstrated significant editorial leadership: Guest Editor for Informatics Special Issue "Advances in Computer-Aided Translation Technology" (2019) Guest Editor for Journal of European Periodical Studies Special Issue "Digital Approaches Towards Serial Publications" (2019) Co-editor of New Empirical Perspectives on Translation and Interpreting (Routledge, 2020) Guest Editor for Filter Special Issue on literary translation technology (2021) Teaching responsibilities encompass Machine Translation & Post-Editing, Terminology and Translation Technology, and Empirical Translation Studies across multiple graduate programs. Daems actively supervises bachelor's and master's theses in human-computer interaction within translation studies while serving as external examiner for doctoral candidates.

• Computer Vision
• Object Recognition
• Scene Understanding
• +7 more

Derek Hoiem is a Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School for Computing and Data Science since 2009. He holds a PhD in Robotics from Carnegie Mellon University (2007) and completed a Beckman Postdoctoral Fellowship (2008). As co-founder and Chief Science Officer of Reconstruct, he develops AI-driven tools for construction progress monitoring. His research focuses on computer vision, object recognition, scene understanding, and graphics. Key honors include IEEE Fellow (2022), University Scholar (2022), and the Sloan Research Fellowship (2013). He has received NSF CAREER and Intel Early Career awards, alongside best paper awards at CVPR (2006) and WACV (2015). His teaching excellence is reflected in numerous 'List of Teachers Ranked as Excellent' accolades across multiple semesters. Research interests span 3D reconstruction, neural networks, and multimodal models. His work addresses challenges in visual program generation, neural radiance fields, and construction monitoring through photogrammetry. He leads projects in continual learning, efficient AI systems, and explainable multimodal interactions. Professional contributions include grants from NSF and industry partnerships. He advises on cutting-edge AI applications in construction, graphics, and vision-language integration. His lab’s innovations bridge theoretical advancements with real-world systems like Reconstruct’s Visual Command Center.

John StaskoView profile

Professor

• Data Visualization
• Information Visualization
• Human-Computer Interaction (HCI)
• +3 more

John Stasko is a Regents Professor in the School of Interactive Computing at the Georgia Institute of Technology, where he has been on the faculty since 1989. He also serves as an Adjunct Professor in the School of Computing Instruction. His primary research focuses on data visualization , information visualization , and visual analytics from a human-computer interaction (HCI) perspective. He has held leadership roles such as Interim School Chair of Interactive Computing (2021–2022) and General Chair of IEEE VIS 2013. Education: B.S. in Mathematics, Bucknell University (1983) M.Sc. and Ph.D. in Computer Science, Brown University (1985, 1989) Research Interests: John leads the Information Interfaces Research Group , emphasizing tools for data exploration and sense-making. His work includes visual analytics systems, peripheral awareness interfaces, and flexible interaction paradigms. Notable contributions include the Jigsaw system for document analysis and ROBIN for migration data visualization. Awards & Recognition: 2023: IEEE VGTC Visualization Lifetime Achievement Award 2014: IEEE Fellow 2012: IEEE VGTC Visualization Technical Achievement Award ACM Distinguished Scientist (2011) ACM CHI Academy & IEEE Visualization Academy memberships Advising & Teaching: John has advised over 20 PhD students and teaches courses like CS 6730: Data Visualization and CS 1331: Intro to Object-Oriented Programming . He emphasizes accessible, user-centered design in both research and education. Affiliations: GVU Center Institute for People and Technology (IPaT) Adjunct Faculty at School of Computing Instruction Member, University of St. Andrews Honorary Faculty (2013)

Darius BurschkaView profile

Professor

• Robotics
• Artificial Intelligence
• Computer Vision
• +3 more

Prof. Dr.-Ing. Darius Burschka is a Professor of Robotics, Artificial Intelligence, and Embedded Systems at Technische Universität München (TUM), specifically within the TUM School of Computation, Information and Technology. He leads the Professorship of Robotics, Artificial Intelligence and Embedded Systems and collaborates closely with the German Aerospace Center (DLR). His academic career includes postdoctoral research at Yale University (1998), associate research scientist and assistant research professor roles at Johns Hopkins University (1999-2005), and has been at TUM since 2005. His research focuses on sensor systems in robotics, human-machine interfaces, video-based navigation, and 3D reconstruction. Notable contributions include advancements in laser-based map generation, monocular navigation algorithms, and endoscopic image registration for medical applications. Prof. Burschka has received awards such as the Airtec 2010 Silver Award and best paper awards from prominent conferences like IROS and MICCAI. His work spans robotics, autonomous systems, computer vision, and medical imaging, with a strong emphasis on real-world applications in automotive, healthcare, and industrial automation. Education: Bachelor/Master in Electrical Engineering at TUM Doctorate (Dr.-Ing.) in Electrical Engineering, TUM (1998) His research interests include: Autonomous navigation and control Multi-sensor fusion and 3D reconstruction Human-robot interaction and haptic systems Robotic perception in dynamic environments Recent articles highlight advancements in graph neural networks for action segmentation, hybrid tracking systems, and latency modeling in industrial robotics, reflecting his ongoing contributions to cutting-edge robotics and AI.

Jonggi HongView profile

Assistant Professor

• Human-Computer Interaction
• Accessibility Technologies
• Artificial Intelligence
• +3 more

Jonggi Hong is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. He holds a PhD from the University of Maryland (2021) and MS/BS degrees from KAIST (2014/2012). His research focuses on accessibility technologies, human-computer interaction (HCI), and AI applications, particularly addressing challenges for visually impaired users through machine learning and assistive systems. He has conducted research at Microsoft Research, Adobe Research, and the Smith-Kettlewell Eye Research Institute. His work explores user-system interactions via quantitative and qualitative methods, including studies on speech/image recognition errors, VR training for blind photography, and wearable haptic interfaces. Key projects include teachable object recognizers for non-experts and error detection in digital content. Hong serves as a Workshop Chair and Program Committee member for the ACM SIGACCESS ASSETS conference, promoting inclusive design principles in technology. Publications span venues like CHI, ASSETS, and W4A, emphasizing real-world accessibility solutions. His grants and advising activities are not explicitly listed, but he leads research on assistive machines, inclusive design, and AI-infused systems. Hong actively contributes to professional societies like ACM and maintains affiliations with labs such as the Intelligent Assistive Machines Lab.

• Computer Vision
• Machine Learning
• Action Recognition
• +6 more

Prof. Juergen Gall is a Professor at the University of Bonn, affiliated with the TRA Mathematics, Modelling and Simulation of Complex Systems and the Department of Information Systems and Artificial Intelligence. He leads the Computer Vision Group at the Lamarr Institute for Machine Learning and Artificial Intelligence. His research focuses on action recognition, video understanding, anticipation, human pose estimation, and applications in plant science, earth science, and neuroscience. He has contributed to numerous projects and conferences, including organizing workshops on machine learning for Earth systems and holistic video understanding. His work spans over 200 publications in top venues like CVPR, ICCV, and NeurIPS. He oversees software tools like MANTA and STING-BEE, and has developed datasets such as Humans in Kitchens and PoseTrack21. Research interests emphasize advancing computer vision for real-world challenges, with a focus on multi-modal learning and deep learning applications. Recent articles explore parameter-efficient models, diffusion-based anticipation, and multi-view matching for plant analysis. His contributions bridge academia and industry, addressing agricultural and environmental monitoring needs.

Xingchen ZhangView profile

Lecturer

• Human Intention Prediction
• Human Pose Estimation
• Image Fusion
• +3 more

Dr. Xingchen Zhang is a Marie Skłodowska-Curie Individual Fellow at the Personal Robotics Laboratory (PRL), Imperial College London, and holds a Lecturer position in the Department of Electrical and Electronic Engineering. Previously, he served as a Teaching Fellow and Research Associate at Imperial College. He teaches Deep Learning courses and is actively involved in academic reviews for prestigious programs like UKRI Future Leaders Fellowships and top conferences/journals such as CVPR, ECCV, and IEEE TNNLS. Xingchen earned his BSc from Huazhong University of Science and Technology (2012) and PhD from Queen Mary University of London (2018). His research focuses on human motion prediction, pose estimation, image fusion techniques (visible-infrared, multi-focus), visual object tracking, and deep learning applications in computer vision. His work has led to a notable book on image fusion, awarded the National Science and Technology Academic Publications Fund in China (2019). His recent publications emphasize real-world applications such as traffic flow prediction, pedestrian privacy protection, and fusion-based object tracking. He contributes to advancing graph neural networks for mobility analysis and spatio-temporal modeling, while maintaining a strong focus on interdisciplinary robotics and AI solutions. Awards: Marie Curie Fellowship, National Science & Technology Academic Publications Fund (2019) Key Roles: Reviewer for UKRI FLF, CVPR, ECCV, IJCV Labs: Personal Robotics Lab (PRL), Imperial College London

Beat SignerView profile

Professor

• Cross-Media Information Spaces and Architectures (CISA)
• Interactive Paper
• Dynamic Data Physicalisation
• +7 more

Beat Signer is Professor of Computer Science at Vrije Universiteit Brussel (VUB) and Director of the Web & Information System Engineering (WISE) laboratory. His research focuses on cross-media information spaces and architectures (CISA) spanning interactive paper, dynamic data physicalisation, and tangible holograms. His educational background includes a Computer Science degree from ETH Zurich, where he completed his PhD thesis on fundamental concepts for interactive paper and cross-media information spaces in 2005. Key research areas include: Cross-media document formats and resource-selector-link (RSL) hypermedia metamodel Context-sensitive adaptation and cross-media transclusion Multimodal interfaces (Mudra, iGesture) and tangible computing Internet of Things interoperability and semantic middleware Recent publications (2023-2025) reveal strong trends in dynamic data physicalisation hardware, FAIR positioning systems (OpenHPS), end-user IoT development (eSPACE), and next-generation human-information interaction paradigms. His work increasingly integrates Solid protocols for decentralized data and explores thermal dimensions in tangible holograms. As Principal Investigator, he leads multiple EU-funded projects including OpenHPS, TangHo, and eSPACE. His team has developed influential frameworks like MindXpres for cross-media presentations and CMT for context modeling. He supervises 13 doctoral researchers including Dr. Maxim Van de Wynckel and Dr. Audrey Sanctorum, focusing on cross-media applications in education, IoT, and data visualization. His WISE laboratory develops innovative solutions bridging digital and physical information spaces through projects like ArtVis for collaborative data exploration and TangHo for physically augmented virtual objects.

• Machine Learning
• Deep Learning
• Neural Networks
• +5 more

Sam Leroux is a Tenure Track Assistant Professor and IMEC Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His research spans multiple interdisciplinary domains where artificial intelligence meets real-world applications, with particular emphasis on machine learning, deep neural networks, and distributed systems. He maintains active collaborations with IMEC and contributes to several EU and national research initiatives focused on practical AI deployment. Dr. Leroux's research interests center on developing efficient and privacy-aware machine learning systems that can operate effectively on resource-constrained devices. His work bridges theoretical advances in neural network architectures with practical applications in agriculture, healthcare monitoring, industrial IoT, and sustainable computing. He has pioneered approaches in adaptive neural networks that dynamically adjust computation based on available resources, enabling AI deployment at the network edge without compromising privacy. His publication record reveals a clear trajectory from foundational work in neural network architectures toward increasingly applied research. Recent publications demonstrate strong focus on privacy-preserving AI techniques, hardware-efficient machine learning, and computer vision applications in agriculture and industrial settings. His work consistently addresses the tension between model performance and resource constraints, with growing emphasis on ethical considerations in AI deployment. As an academic supervisor, Leroux currently guides numerous doctoral researchers across diverse projects including broiler welfare monitoring, hardware-efficient continuous learning, UAV-based agricultural sensing, and privacy-aware ergonomic analysis. He serves as Promotor for the 'Hardware-efficient continuous learning' project funded by the Special Research Fund, demonstrating his leadership in securing competitive research funding. His work environment features strong connections between Ghent University's academic research and IMEC's technological expertise, creating a fertile ground for translating theoretical advances into practical solutions. This positioning enables his research group to tackle challenges spanning from algorithm development to hardware implementation, with particular focus on real-world validation of proposed techniques.

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

Prof. Abhinav Valada is a Full Professor (W3) at the University of Freiburg, leading the Robot Learning Lab and serving as Chair of the IEEE RAS Technical Committee on Robot Learning. He holds affiliations with the Department of Computer Science , BrainLinks-BrainTools , and the ELLIS unit Freiburg . His research focuses on enabling robots to learn continuously through perception and interaction in complex environments, emphasizing scalable lifelong learning systems. Education: PhD (summa cum laude), University of Freiburg (2019) MS in Robotics, Carnegie Mellon University (2013) BTech in Electronics & Instrumentation, VIT University (2010) Research Interests: Robotics, machine learning, computer vision, with specializations in robot perception, state estimation, and planning. Current work emphasizes open-vocabulary scene understanding , uncertainty-aware perception , and embodied AI . Projects include multi-modal SLAM systems, neural radiance fields for robotics, and generalizable manipulation policies. Awards: IEEE RAS Early Career Award (2023) NVIDIA Research Award (2022) AutoSens Most Novel Research Award (2022) DFG Emmy Noether AI Fellowship (2021) Grants & Labs: Director of the Robot Learning Lab , co-founder of Platypus LLC (2013–2015), and collaborator on projects like SORTIE (disaster response robotics). Active in funding initiatives focusing on autonomous systems and AI ethics.

Jan DrewesView profile

Research Fellow

• Human Perception
• Image Classification
• Object Recognition
• +2 more

Jan Drewes is a current Postdoctoral Research Fellow at the Physics of Psychological Processes Group within the Institute of Physics at Technische Universität Chemnitz , Germany. His work bridges physics and experimental psychology, focusing on visual perception, eye tracking, and natural image statistics. PhD , Justus-Liebig-Universität Giessen (2006), thesis: Classification of Natural Scenes Diploma , Universität zu Lübeck (2003), thesis: Prediction of Saccadic Eye Movements with Dynamic Scenes His research explores how humans process visual information, including topics like object recognition, eye movement dynamics, and the statistical properties of natural scenes. He has developed methods to analyze gaze drift due to pupil dynamics and investigated neural mechanisms in shape and orientation perception. Recent publications (2010–2016) highlight his focus on rapid visual categorization, animal detection in natural scenes, and EEG-oculomotor interactions. Key areas include non-retinotopic perception, recurrent neural processing, and behavioral oscillations during visual tasks. He has collaborated with institutions in Germany, Italy, Canada, France, and China, including York University, Trento University, and CNRS Toulouse, while affiliating with research groups like the Melcher Active Perception Group and Elder Lab.

Karthik DesinghView profile

Assistant Professor

• Robotics
• Computer Vision
• Artificial Intelligence
• +7 more

Karthik Desingh is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, College of Science and Engineering. His research focuses on robotics, computer vision, and artificial intelligence, particularly in robot perception, object representation, and manipulation learning. His research interests span robotics , computer vision , machine learning , and human-robot interaction . He investigates how robots can perceive and interact with articulated and multi-object environments using physics-based models, belief propagation, and deep learning techniques. A significant portion of his work emphasizes unsupervised and end-user-directed learning for robot manipulation. The recent publications reveal a strong trend in multi-object representation , visual dynamics modeling , and vision-language integration for robot learning. His work frequently appears in top-tier robotics conferences such as IEEE ICRA and journals like IEEE Transactions on Robotics and IEEE Robotics and Automation Letters. While no formal awards are listed, his contributions to robotics and AI are evident through consistent high-impact publications and active research in perception for general-purpose manipulation. He advises students in robotics and AI, though specific names are not listed. His research is likely supported by academic and federal grants, given the sustained output and publication in peer-reviewed venues. He leads or contributes to a robotics research group focusing on perception and learning for manipulation tasks. The work contributes to UN Sustainable Development Goals through advancements in intelligent systems and automation. His lab or research team engages in interdisciplinary collaboration, particularly in computer vision and robotics, with recent work involving vision-language models and policy learning for assembly tasks.

• Medical Image Analysis
• Computer Vision
• Multimedia Systems
• +6 more

Steven Alexander Hicks is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. His research focuses on applying computer vision and machine learning techniques to medical imaging problems, particularly in gastrointestinal endoscopy and reproductive medicine. He is an active contributor to the medical AI community through his involvement in organizing challenges and workshops such as ImageCLEFmedical and MediaEval. Hicks' research interests center on medical image analysis, with particular emphasis on gastrointestinal endoscopy and reproductive health applications. His work spans medical image segmentation, polyp detection, sperm tracking systems, and explainable AI for medical applications. He has developed innovative approaches for medical image analysis including diffusion models for synthetic data generation, visual question answering systems for gastrointestinal tract analysis, and frameworks for evaluating explanation methods in neural networks. His research bridges the gap between computer science and clinical medicine, aiming to develop practical AI tools that can be integrated into medical workflows. Analysis of Hicks' recent publication record reveals a strong focus on medical image analysis challenges, particularly in gastrointestinal endoscopy and reproductive medicine. He has been instrumental in organizing and contributing to the ImageCLEFmedical challenges, which have become important benchmarks in the field. His work shows a progression from basic image analysis techniques to more sophisticated approaches incorporating explainability, multimodal learning, and generative models. The consistent publication output across top venues demonstrates his active role in advancing medical AI research. Hicks has made significant contributions through his leadership in organizing evaluation campaigns and challenges that have shaped research directions in medical image analysis. His work with the MediaEval and ImageCLEF communities has provided valuable benchmarks and datasets for researchers worldwide. He has been involved in several large-scale collaborative projects addressing important problems in medical imaging, including polyp segmentation, instrument detection in endoscopy, and sperm analysis. As part of the broader research ecosystem at Oslo Metropolitan University, Hicks contributes to a vibrant research environment focused on applying computing technologies to healthcare challenges. His work often involves interdisciplinary collaboration between computer scientists, medical professionals, and domain experts to ensure that technical solutions address real clinical needs. His research group appears to focus on developing practical AI solutions that can be translated into clinical practice, with particular attention to validation methodologies and explainability requirements in medical contexts.