جست‌وجوی استاد و پژوهشگر

• Cognitive Psychology
• Event Perception
• Visual Cognition
• +۲ مورد دیگر

Tolgahan Aydin is a Researcher and PhD student at the Leibniz Institute for Media Education (IWM) and the University of Tübingen, working in the Perception and Action Lab since November 2023. His doctoral research focuses on modal and amodal event representations and their role in dynamic event comprehension, conducted jointly between the IWM and University of Tübingen. Aydin holds a Bachelor's and Master's degree in Psychology from Yaşar University in Izmir, Turkey. His master's thesis, titled "Perceptual and Conceptual Features of Event Segmentation", established his research foundation in event cognition. His primary research explores cognitive mechanisms of event perception, visual cognition, and meta-cognition. He investigates how humans segment continuous events, process spatial judgments in cinematic contexts, and represent events through modal (directly visible) and amodal (occluded) mechanisms. His work bridges experimental psychology with real-world media applications, emphasizing perceptual load, viewpoint dependency, and boundary processing in dynamic event comprehension. Recent publications reveal a cohesive research trajectory examining spatial cognition in film, event boundary effects on memory completion, and viewpoint dependencies in visual events. These studies employ rigorous experimental designs to uncover how continuity editing influences spatial judgments, how endpoints trigger event completion, and how perspective shifts affect event representation—collectively advancing understanding of dynamic event processing in naturalistic contexts. Aydin actively contributes to two major research initiatives: "ArchiveGPT" (2024–present), which evaluates multimodal large language models for archival image cataloguing from psychological and technical perspectives, and "Modal and amodal event representations" (2023–2026), a three-year project investigating meta-cognitive contributions to event comprehension. His collaborative approach involves partnerships with researchers across German universities and international conferences. As a core member of the IWM's Perception and Action Lab, Aydin participates in a multidisciplinary research ecosystem examining human perception, action, and learning in media environments. The lab's work spans multimodal interaction, realistic depictions, and knowledge construction, providing an integrated framework for his event cognition research.

• Intelligent Tutoring Systems
• Educational Data Mining
• Machine Learning for Education
• +۳ مورد دیگر

Walid El Hefny serves as a Research Fellow at the Leibniz Institute for Media Research (IWM) within the Language and AI in Education Lab since January 2025. He is a core contributor to the AI2Teach project (2020-2026), maintaining FeedBook—an intelligent tutoring system for English as a foreign language instruction. Education: Computer Science and Engineering, M.Sc. (2022), German University in Cairo Computer Science and Engineering, B.Sc. (2018), German University in Cairo His research focuses on developing machine learning models to predict student performance through logging data analysis in intelligent tutoring systems, specifically targeting adaptivity in language education. This work bridges computational linguistics and educational technology to enhance personalized learning experiences in digital classrooms. As part of the Language and AI in Education Lab, he collaborates on multimodal interaction frameworks and AI-driven pedagogical innovations. His prior experience includes four years as a teaching assistant/researcher at the German University in Cairo followed by two years as a research associate in the Computational Linguistics department at the University of Tübingen.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Vajira Thambawita is a researcher at the University of Oslo's Department of Informatics, specializing in medical image analysis and AI-driven healthcare solutions. With over 90 publications since 2019, their work spans gastrointestinal endoscopy, reproductive medicine technology, and multimedia systems for clinical applications. Research focuses on medical image segmentation (polyp detection, sperm tracking), anomaly detection in time-series data, and multimodal analysis for clinical decision support. Key projects include the Medico Multimedia Task at MediaEval (sperm tracking), VISEM-Tracking dataset, and ImageCLEFmedical challenges for GI tract analysis. Their work bridges computer vision with clinical practice through collaborations with Oslo University Hospital and Simula Research Laboratory. Recent publications demonstrate strong trends in generative AI for medical data augmentation (SinGAN-Seg, PolypConnect), explainable AI for clinical validation , and multimodal integration (SoccerNet-Echoes). The research consistently targets real-world clinical problems with emphasis on transparency and robust evaluation. Thambawita actively contributes to major benchmark challenges including ImageCLEF, Medico, and Medical AI competitions, serving as organizer and participant in advancing evaluation standards for medical AI systems.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +۶ مورد دیگر

Prof. Dr. Didier Stricker is a distinguished Professor of Computer Science at Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU) and serves as Scientific Director and Head of the Augmented Reality Research Department at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Augmented Vision Group, which comprises approximately 30 researchers working across various domains of computer vision and augmented reality. His work bridges academic research with industrial applications through collaborations with major companies including Sony, Google, and John Deere. His educational background includes electrical engineering studies at the Polytechnic Institute of Grenoble and the Technical University of Karlsruhe. He earned his doctorate from the Technical University of Darmstadt in 2002 with a dissertation on "Computer Vision-Based Calibration and Tracking Methods for Augmented Reality Applications." Prof. Stricker's research spans virtual and augmented reality, computer vision, human-computer interaction, cognitive interfaces, and on-body sensor networks. His work focuses on developing practical applications that enhance human capabilities through advanced visual computing technologies. He has pioneered approaches in video and sensor analytics, particularly in creating cognitive interfaces that respond intelligently to user needs and environmental contexts. His recent publications reveal a strong emphasis on 3D scene understanding, real-time processing for augmented reality applications, and the integration of large language models with spatial reasoning capabilities. There's a clear trend toward more sophisticated multimodal approaches that combine vision, language, and spatial understanding to create more natural and intuitive human-computer interactions. Among his notable achievements: Innovation Prize of the German Society of Computer Science (2006) Organized the first IEEE & ACM International Symposium on Mixed and Augmented Reality (ISMAR) in 2002 Member of the ISMAR steering committee from 2000-2007 Multiple best paper and demonstration awards at major conferences Several registered patents in tracking and augmented reality technologies Prof. Stricker has supervised numerous PhD and Master's students through his leadership of the Augmented Vision Group. His research is supported by significant funding from both European and national research organizations, as well as through industrial partnerships. He serves as an expert reviewer for various research funding bodies and contributes to the academic community through editorial roles for journals and conferences in VR/AR and computer vision. The Augmented Vision Group under his direction maintains strong connections with industry partners and participates in numerous collaborative research projects including LUMINOUS, SHARESPACE, I-Nergy, BIONIC, and VIDETE. These projects span applications in language-augmented XR systems, social experiences in hybrid spaces, AI for energy systems, personalized body sensor networks, and 4D scene analysis.

Lin Geng Fooمشاهده پروفایل

پژوهشگر

• Video Analysis
• Activity Understanding
• Diffusion Models
• +۲ مورد دیگر

Lin Geng Foo is a postdoctoral researcher at the Max-Planck Institute for Informatics under the Visual Computing and Artificial Intelligence (VCAI) Department, supervised by Prof. Christian Theobalt. He earned his PhD and Bachelor's from the Singapore University of Technology and Design (SUTD) with the President's Graduate Fellowship and top honors. His research focuses on video analysis , activity understanding , diffusion models , and dynamic neural networks , particularly for 3D human generation/editing . His work includes groundbreaking papers on adversarial attacks, human mesh recovery, and pose estimation. Key trends in his recent publications include applications of diffusion models dynamic networks 3D human modeling video analysis action recognition neural rendering for tasks like mesh recovery, controllable video editing, and sign language translation. Scientific awards include PREMIA Best Student Paper 2023 Commendation Prize Keppel Awards of Excellence (2018, 2019) EY Business Analytics Award He serves as a reviewer for major conferences (CVPR, ICCV, NeurIPS, ICML) and has presented talks at ICME 2024 Metaverse Workshop and SDSC Industry & Demo Day 2024.

Patrick Gebhardمشاهده پروفایل

پژوهشگر

• Affective Computing
• Natural Language Processing
• Human-Computer Interaction
• +۳ مورد دیگر

Dr. Patrick Gebhard is a researcher at the Cognitive Assistance Systems department of the German Research Center for Artificial Intelligence (DFKI), focusing on affective computing, human-computer interaction, and psychological modeling. His work bridges computational linguistics, mental health, and wearable technologies. Key projects: UBIDENZ (digital empathetic therapy assistance), MITHOS (mixed-reality teacher training), SocialWear (interactive smart fashion), MindBot (mental health in Industry 4.0), EmmA (emotional coaching avatars). Recent research explores emotion regulation strategies, psychodynamic conflict recognition via LLMs, and multimodal sign language corpora development. Collaborates with institutions like Saarland University, contributing to conferences such as CLPsych, LREC-COLING, and ACII. Contact: Patrick.Gebhard@dfki.de

• Machine Learning
• Internet of Things
• Biomedical Engineering
• +۷ مورد دیگر

Md. Mehedi Hasan is a researcher at Daffodil International University's Department of Computer Science and Engineering. With over 17 years of research activity, his work spans multiple domains including Internet of Things (IoT), machine learning, biomedical informatics, and environmental technology. Current affiliations include collaborations with institutions in Bangladesh, Japan, and Malaysia. Research areas: Machine Learning, IoT Security, Medical Imaging, Aquaculture Monitoring Key technologies: Deep Learning, Time Sensitive Networking, Kerberos Authentication Notable contributions: NAND Flash memory analysis, Pediatric health applications, Urbanization impact studies His 2025 publications focus on medical data preservation ( Comput. Biol. Medicine ), pedestrian risk assessment ( Complexity ), and toddler screen time management ( SoftwareX ). Recent work explores the intersection of environmental analysis with deep learning and cryptographic solutions for smart infrastructure. Scientific contributions include: Radiation tolerance analysis of neuromorphic systems (IRPS 2020) Smart meter security protocols (Earth Sci. Informatics 2024) Acoustic breathing phase detection (Proc. ACM IMWUT 2021)

• Computer Science
• Artificial Intelligence
• Operations Research
• +۵ مورد دیگر

Wei Cui is an active researcher with affiliations to multiple institutions, including Beijing Institute of Technology, Shandong University of Science and Technology, and others. Their work spans diverse domains such as artificial intelligence , robotics , quantum computing , and environmental engineering . Research Interests include multimodal fake news detection, vehicle routing optimization, biomedical signal processing, and remote sensing applications. Recent publications focus on machine learning for urban infrastructure , humanoid robotics , and quantum key distribution . Article Trends reveal a strong emphasis on interdisciplinary AI (e.g., healthcare waste logistics, 3D perception) and data-driven optimization . Collaborations include experts in control systems , quantum physics , and environmental science .

• Computer Vision
• Pattern Recognition
• Biometrics
• +۳ مورد دیگر

Zhen Lei is a researcher at the National Laboratory of Pattern Recognition , Chinese Academy of Sciences, Beijing, China. His work focuses on Computer Vision , Biometrics , and Pattern Recognition . Research trends from his publications include Face Recognition , Anti-Spoofing , Person Re-Identification , 3D Face Reconstruction , and Vision Transformers . He has contributed to Information Fusion , Neural Networks , and Image Processing journals. Recent awards or affiliations with honorifics were not explicitly mentioned. Collaborative efforts are evident through co-authorship with experts in Face Detection , Domain Adaptation , and Medical Imaging .

• Wireless Communications
• Polarization Techniques
• Knowledge Graph Completion
• +۷ مورد دیگر

Fangfang Liu is an active researcher with an extensive publication record spanning from 2005 to 2025, demonstrating significant contributions across multiple domains in computer science and engineering. Their work shows consistent collaboration with researchers including Weimin Li, Caili Guo, Zhimin Zeng, and Chunyan Feng, suggesting strong institutional ties within their research community. Fangfang Liu's research spans several key areas including wireless communications, knowledge graph completion, semantic communications, and fake news detection. Their work demonstrates expertise in applying machine learning techniques to solve complex problems in network security, IoT systems, and multimedia analysis. The research portfolio shows a progression from foundational work in wireless communications and polarization techniques toward more recent applications in AI-driven security and knowledge representation. The publication trends reveal a strategic expansion from traditional communications engineering into cutting-edge AI applications. Early work focused on polarization techniques and wireless channel modeling, while recent publications emphasize knowledge graphs, multimodal fake news detection, and semantic communications. This evolution demonstrates adaptability to emerging research frontiers while maintaining technical depth in signal processing and network analysis. Fangfang Liu has made substantial contributions through numerous publications in prestigious venues including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. Their collaborative approach is evident through extensive co-author networks across academia and research institutions. While specific advising information isn't detailed in the publication record, Fangfang Liu appears to lead research groups working on precision assembly, knowledge graph applications, and IoT security. The research program demonstrates strong connections between theoretical foundations and practical implementations in communication systems and AI applications.

Xiao Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Statistical Machine Learning
• Convex Optimization
• +۴ مورد دیگر

Xiao Zhang is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). He obtained his Ph.D. in Computer Science from the University of Virginia in 2022 under Prof. David Evans, following an M.S. in Statistics from the same institution and a B.S. in Mathematics from Tsinghua University. Research Focus Zhang's research spans foundational and applied aspects of trustworthy machine learning, with emphasis on: Security vulnerabilities in ML systems (adversarial attacks, data poisoning) Robustness guarantees for learning algorithms Optimization methods for deep learning Privacy-preserving techniques Multimodal and physical-world applications His recent work focuses on developing theoretically-grounded defenses against emerging threats to AI systems. Publication Trends Zhang's 15 most recent publications (2019-2025) demonstrate consistent focus on adversarial machine learning, with growing emphasis on: 1) Physical-world attacks/defenses, 2) Multimodal model security, and 3) Certified robustness guarantees. The work consistently appears at top-tier venues (NeurIPS, ICLR, CCS). Academic Activities He actively recruits students and collaborators for trustworthy ML research, with open positions for PhD candidates and research assistants. No awards or specific grant information is documented in the provided materials.

Jan Stefan Kirschkeمشاهده پروفایل

استاد

• Artificial Intelligence in Medical Imaging
• Neurodegenerative Diseases
• Oncological Imaging
• +۳ مورد دیگر

Prof. Jan Stefan Kirschke is the Director of the Institute of Diagnostic and Interventional Neuroradiology at the Technical University of Munich (TUM), leading a research group funded by ERC, DFG, and BMBF grants. He holds a medical degree and doctorate from TUM, followed by a fellowship at UCSF and habilitation in 2012. His work focuses on AI-driven image processing for CNS diseases, spinal disorders, and biomarker development. Key awards include the ERC Consolidator Grant (2022) and ERC Starting Grant (2014). His research emphasizes automated lesion segmentation, stroke imaging, and osteoporosis screening. Recent studies explore AI applications in MRI acceleration, stroke protocols, and clinical decision support systems. Advising and grants involve collaborations on BraTS challenges, spinal biomechanics, and neurovascular interventions. He leads the 'deep spine' initiative, advancing personalized osteoporosis screening via opportunistic CT imaging.

Constance Scharffمشاهده پروفایل

استاد

• Neurobiology
• Animal Behavior
• Vocal Learning
• +۳ مورد دیگر

Constance Scharff is a Professor of Behavioral Biology at the Freie Universität Berlin, affiliated with the Department of Biology, Chemistry, Pharmacy. Her research focuses on the neurobiology of birdsong, vocal learning, and the genetic basis of communication, particularly the role of the FOXP2 gene. Scharff earned her Ph.D. from Rockefeller University and has held positions at institutions like the Max Planck Institute for Molecular Genetics. She is a leading expert in avian vocalization and its links to human language evolution. Awards include membership in the Berlin-Brandenburg Academy of Sciences and the Leopoldina. Current projects include a DFG-funded research group on the neural foundations of vocal communication (2025) and the development of robotic models like RoboFinch for studying songbird behavior. She teaches courses in animal behavior and oversees a dynamic research group with global collaborations. Education: Ph.D. in Neurobiology (Rockefeller University), Postdoc at the Collège de France and Rockefeller University. Research Themes: Birdsong neuroethology, FOXP2 gene function, vocal learning mechanisms, avian genome studies (e.g., zebra finch sequencing). Awards: Leopoldina membership (2019), BBAW membership (2012), German Zoological Society presidency (2007/08). Her work bridges evolutionary biology, neurogenetics, and robotics to explore how vocal communication evolves and is encoded in the brain.

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Mohamed M. Abdallah is a researcher affiliated with Hamad Bin Khalifa University in Doha, Qatar, specifically within the College of Science and Engineering . His work focuses on advanced applications of Machine Learning , Artificial Intelligence , and Cybersecurity in domains such as Smart Grids , Internet of Things , and Wireless Communication . His recent research explores Federated Learning under adversarial conditions, optimization of Multi-Agent Systems for task offloading, and Privacy-Preserving Techniques in networked environments. Key contributions include frameworks for Deep Reinforcement Learning (DRL) in Edge Computing and 6G Networks , addressing challenges in Energy Efficiency , Latency , and Data Distribution Shifts . His publications highlight collaborations with institutions like Texas A&M at Qatar and Hamad Bin Khalifa University , emphasizing solutions for Heterogeneous Networks , Blockchain Applications , and Secure Communication in IoT and critical infrastructure.

• Machine Learning
• Natural Language Processing
• Cybersecurity
• +۵ مورد دیگر

Cécile Paris is a researcher at CSIRO , focusing on interdisciplinary applications at the intersection of Artificial Intelligence , Cybersecurity , and Human-AI Collaboration . Her work spans domains such as biomedical informatics, social media analysis, and robotics. Research Interests: Developing collaborative frameworks for human-AI teams Explainable AI in cybersecurity Trust modeling in social networks Fake news detection via temporal and graph-based networks Image captioning with multimodal learning Stance classification across domains Article Trends: Recent publications emphasize adaptive alert prioritization (2025), systematic reviews on cybersecurity challenges (2024), and foundational work in NLP for epidemic intelligence (2018-2020). Key methodologies include graph neural networks, transformer models, and adversarial training.