جستجوی استاد راهنمای دانشگاهی

Florian Müllerمشاهده پروفایل

استادیار

• Extended Reality
• Human-Computer Interaction
• Mobile Interaction
• +۴ مورد دیگر

Florian Müller serves as Assistant Professor for Mobile Human-Computer Interaction at Technical University of Darmstadt, leading the Urban Interaction Lab. His research pursues seamless integration of digital information, AI agents, and physical environments into unified Extended Reality (XR), with focus on novel interaction techniques for mobile XR and human-AI collaboration through body-centric, tangible, and haptic interfaces. Müller's primary research spans Extended Reality, Human-Computer Interaction, and Mobile Interaction with specialization in Haptic Interfaces, Tangible Interfaces, and Body-Centric Interaction. He investigates how multimodal sensory feedback enhances user experiences in immersive environments, particularly examining AI-driven adaptation for context-aware XR systems and the social implications of pervasive interactive technologies in urban settings. Recent publications (2024-2025) demonstrate strong interdisciplinary trends across multisensory XR (taste modulation, thermal feedback), social VR/AR applications (privacy, content sharing), and human-AI co-creation systems. Key domains include health (motor control assessment), education, and urban interaction contexts, with consistent emphasis on user experience, safety, and practical implementation challenges. Müller directs the Urban Interaction Lab at TU Darmstadt, which develops and evaluates interactive technologies for public spaces through prototyping novel mobile and XR interfaces. The lab's work emphasizes real-world deployment in urban environments, focusing on social engagement, situational awareness, and seamless interaction between physical and digital layers of city infrastructure.

Thomas Lischeidمشاهده پروفایل

استاد

• German Didactics
• Language Reflexion
• Text and Media Didactics
• +۷ مورد دیگر

Thomas Lischeid is a Professor for German Language Learning at the Weingarten University of Education since 2010. His work spans German didactics, media symbolism, and discourse analysis, with a focus on text/media interplay and orthography education. Education: Dr. phil. (1998, Ruhr University Bochum) Teaching Experience: Lecturer roles at Ruhr University Bochum (1997-2001), University of Wuppertal (2002), University of Siegen (2003-2005), Acting Chair at University of Hildesheim (2007-2009) and Greifswald (2009-2010) Research Interests include multimodal representation (infographics), language reflexion, crisis narratives, and orthography education. His publications explore intermediality, Nazi book burning symbolism, and Kafka's pedagogical relevance. Recent Publications address media oscillations during crises, poetic film analysis, and intermedial diagrammatics. He also contributed to seminal handbooks like Kernbegriffe der Sprachdidaktik . Scientific Focus Discourse-semiotic analysis of normalism Media symbolism in educational contexts German as a second language

Valentin Wernerمشاهده پروفایل

استاد

• Applied Linguistics
• Learner Englishes
• Sociolinguistics
• +۷ مورد دیگر

Prof. Dr. Valentin Werner is an associate professor at the University of Bamberg , holding a chair in English Linguistics . His work bridges applied linguistics, corpus studies, and pop culture analysis, with a focus on language education and sociolinguistic variation. Expertise in learner Englishes, telecinematic language, and multimodal discourse Director of multiple DFG-funded projects including 'Young German Learner English' Recipient of Taylor & Francis Outstanding Research Monograph Award (2020) His recent research explores: Pop lyrics as pedagogical tools (2023-2024 projects) Political live blogs and hybrid news discourse (2025 publications) Country music authenticity and register analysis (2024 studies) Scientific awards include: Taylor & Francis Outstanding Research Monograph Prize (2020) Habilitationspreis by German Association for the Study of English (2022)

Moritz Herrmannمشاهده پروفایل

پژوهشگر

• Empirical Machine Learning
• Manifold Learning
• Representation Learning
• +۵ مورد دیگر

Dr. Moritz Herrmann is a postdoc researcher and Reproducibility & Open Science Transfer Coordinator at the Munich Center for Machine Learning (MCML). He is affiliated with the Biometry in Molecular Medicine working group led by Prof. Anne-Laure Boulesteix at Ludwig-Maximilians-Universität München, and contributes to initiatives like the LMU Open Science Center , Open Science Initiative in Statistics (OSIS) , and Open Science Initiative in Medicine (OSIM) . Ph.D. in Statistics from LMU (2022), M.Sc. in Statistics (2018), and B.Sc. in Mathematics/Sports Science (2014) His research focuses on Empirical Machine Learning , Manifold Learning , and Metascience , with emphasis on epistemological foundations and reliability in ML research. He advocates for open science practices and data literacy, as outlined in his ICML 2024 position paper on rethinking empirical ML research. As a member of the Empirical Machine Learning research focus group and the Statistical Learning and Data Science Chair , Herrmann bridges statistical methodology with biomedical applications. His work spans outlier detection, cluster analysis, and reproducibility frameworks, reflected in his recent publications in journals like Biometrical Journal and Data Mining and Knowledge Discovery .

Karsten Borgwardtمشاهده پروفایل

استاد

• Machine learning
• Systems biology
• Bioinformatics
• +۳ مورد دیگر

Prof. Dr. Karsten Borgwardt is Director of the Research Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany. A leading figure in the intersection of machine learning, bioinformatics, and systems biology, he heads a multidisciplinary team that develops novel computational methods to extract knowledge from large biomedical data sets. Research Mission: The Borgwardt lab converges big data analytics and biomedical research . Two overarching goals drive their work: (1) Automatically generating new biological and medical knowledge from massive data via state-of-the-art machine-learning algorithms. (2) Understanding the molecular underpinnings of biological system function, with emphasis on personalized medicine and biomarker discovery. Their methodological toolbox spans graph neural networks, kernel methods, conformal prediction, deep learning on sequences and structures, and topological data analysis . Application domains include antimicrobial resistance prediction, protease engineering, acute-kidney-injury forecasting, coronary-artery-disease diagnostics, single-cell spatial proteomics, and Long-COVID immune profiling. Recent Publication Landscape (2023-2025): The group’s latest articles demonstrate a clear trend toward translationally relevant machine learning . High-impact venues such as Nature Communications , Science , ICLR , and RECOMB feature their work on: Data-driven protein engineering using DNA-recorded deep mutational scanning. Guaranteed antimicrobial resistance detection from MALDI-TOF spectra via conformal prediction. Graph-based biomarker discovery with theoretical guarantees. Deep phenotyping of human iPSC-derived neuronal networks to study disease mutations. Multi-modal learning that fuses genomics, proteomics, and clinical data for patient stratification. These contributions collectively advance both the theoretical foundations and real-world deployment of machine learning in medicine. Scientific Awards & Honors: While no explicit award list is provided, the breadth and impact of publications, invited book chapters, and keynote-level conference presentations (ICLR, RECOMB, ISMB/ECCB) testify to sustained international recognition. Laboratory & Collaboration Ecosystem: The Borgwardt lab operates at the Max Planck Institute of Biochemistry —a world-leading biomedical research campus. Collaborations span multiple Max Planck centers, university hospitals across Europe, and international consortia such as the EyeConic study on optogenetics therapy. The lab’s open-source footprint includes the Multi-SConES R package for multi-task network-regularized feature selection, fostering reproducible science across the community.

• Computer Vision
• Machine Learning
• Multimedia Retrieval
• +۵ مورد دیگر

Michel Crucianu is a Professor at the Conservatoire national des arts et métiers (CNAM) in Paris, France, affiliated with the CEDRIC laboratory (Centre d'Études et de Recherche en Informatique et Communications). His research spans computer vision, machine learning, and multimedia information retrieval, with a focus on developing advanced techniques for image and video analysis. Crucianu's research interests include computer vision, deep learning, generative models, zero-shot learning, and cross-modal retrieval. His work often addresses fundamental challenges in representation learning, with applications ranging from fashion recognition to disaster monitoring. He has made significant contributions to GAN-based techniques, particularly in semantic editing and attribute control within latent spaces. His research combines theoretical insights with practical applications, demonstrating strong interdisciplinary connections between computer vision and machine learning. Analysis of his recent publications reveals a strong focus on generative models (particularly GANs), zero-shot learning, and compositional visual reasoning. His work shows an evolution from traditional image retrieval techniques toward more sophisticated deep learning approaches, with increasing emphasis on interpretability, multimodal representations, and efficient learning strategies. The breadth of his research spans theoretical advances in representation learning to practical applications in areas like flood detection and fashion recognition. While specific awards are not mentioned in the available information, Crucianu's extensive publication record in top-tier conferences and journals demonstrates significant recognition within the computer vision and machine learning communities. His consistent publication output over two decades reflects sustained research excellence and impact. Crucianu has collaborated extensively with researchers at CEDRIC and other institutions, particularly with colleagues like Hervé Le Borgne, Nicolas Audebert, and Marius Ferecatu. His work often involves interdisciplinary collaborations spanning computer vision, machine learning, and domain-specific applications. His research has been supported by various projects addressing multimedia indexing, content-based retrieval, and advanced learning techniques. As a member of the CEDRIC laboratory, Crucianu contributes to one of France's leading research centers in computer science and communications. The laboratory's research axes include complex data analysis, machine learning representations, data mining and statistics, and information decision systems, all areas where Crucianu has made substantial contributions through his research and collaborations.

Shmuel Avidanمشاهده پروفایل

استاد

• Object Tracking
• 3D Object Modeling
• Computer Vision
• +۳ مورد دیگر

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

• Rehabilitation Sciences
• Special Education
• Educational Psychology
• +۵ مورد دیگر

Katharina Weiland is a Researcher at the Institute of Rehabilitation Sciences , Humboldt University of Berlin, within the Faculty of Humanities, Social Sciences and Education . Her work bridges rehabilitation psychology, language pedagogy, and educational technology. 2001-2007: First State Examination in Special Education/German Teaching 2010-2017: Doctoral studies in Rehabilitation Sciences Research focuses on language and communication impairments , digital participation , and diagnostic methods for reading/spelling disorders, with empirical work spanning: Eye movement analysis in literacy acquisition Geographical disparities in educational outcomes Augmentative communication strategies Vocational training optimization Her recent publications analyze digital literacy support , refugee student integration , and stress experience in adolescents . She provides statistical consultation and coordinates research in the Interdisciplinary Competence Center for Rehabilitation Sciences .

• Human Activity Understanding
• Computer Vision
• Sensor Fusion
• +۶ مورد دیگر

Rahul Chaudhari is a Senior Researcher at the Chair of Media Technology, Technical University of Munich (TUM), where he has been working since August 2019. He is affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI) and contributes to research in human-computer interaction, particularly in human activity understanding. Dr. Chaudhari earned his doctoral degree (Summa cum Laude) in Communications and Signal Processing from TUM in 2015, following a Master's degree in Communications Engineering from TUM in 2009 and a Bachelor's degree in Electronics and Telecommunications from the University of Pune, India. His academic journey reflects a strong foundation in communications engineering and signal processing that has evolved into interdisciplinary research spanning haptics, computer vision, and artificial intelligence. His research focuses on Human Activity Understanding using Computer Vision, Sensor Fusion, and AI techniques. His current work centers on understanding Human-Object Interactions using camera data (RGB and Depth) recorded in indoor environments, with potential integration of wearable sensors or environmental sensors. His work has significant applications in improving human well-being, comfort, and convenience through intelligent environments and ambient assisted living systems. Dr. Chaudhari's publication record shows a clear evolution from foundational work in haptic communications to cutting-edge research in human-object interaction and 3D pose estimation. His recent publications demonstrate expertise across multiple domains including computer vision for activity recognition, synthetic data generation for training models, and neurosymbolic approaches to human-AI collaboration. This interdisciplinary approach reflects the increasingly connected nature of modern AI research. Best student paper award for Yujun Wang (2025) LMT Spin-off Wins Third Place at euRobotics Technology Transfer Award 2025 LMT featured in Süddeutsche Zeitung (2025) Diego Fernandez Prado selected as finalist for IEEE CASE Best Paper Award (2024) Dr. Chaudhari actively supervises student research, including Master's theses on advanced topics such as 'Simulation and Optimization for 6G Network Planning using Digital Twins.' His supervision work involves guiding students through complex technical challenges in simulation software evaluation, digital twin implementation, and network optimization. He also serves on thesis committees and provides mentorship to students working on related research topics. His research group operates within the broader context of TUM's initiatives in robotics and machine intelligence, contributing to projects like the Centre for Tactile Internet with Human-in-the-Loop (CeTI) and 5G Testbed Bayern. The group maintains a strong focus on both theoretical foundations and practical implementations, with research that bridges the gap between academic innovation and real-world applications in intelligent environments.

Harald Baayenمشاهده پروفایل

استاد

• Computational Linguistics
• Psycholinguistics
• Morphological Productivity
• +۷ مورد دیگر

Harald Baayen is a Professor at the University of Tübingen within its Faculty of Humanities, Department of Linguistics . His work spans computational linguistics, psycholinguistics, and phonetics, with a focus on discriminative learning models for lexical processing. After studying under Geert Booij in Amsterdam (PhD 1989), he held positions at the Max Planck Institute for Psycholinguistics (1990-1998), Radboud University (Associate Professor via Dutch Science Foundation award), and the University of Alberta (Full Professor 2007-2011). His 2011 Alexander von Humboldt Award and 2025 Honorary Doctorate from Tartu reflect his scholarly impact. Education: PhD in General Linguistics (Amsterdam, 1989) Awards: Alexander von Humboldt (2011), Dutch Science Foundation Career Advancement (1998) Teaching: Courses in Linguistics for Cognitive Science and Statistical Methods since 2011 at Tübingen Research Synthesis Baayen's research bridges computational modeling and empirical phonetics . Notable projects include: Linear Discriminative Learning (2019): A unified framework for morphological processing without morphemes Articulatory Phonetics : Using EMA and ultrasound to study dialect variation and articulation practice effects Statistical Methods : Pioneering generalized additive models and quantile regression in psycholinguistic analysis His 2025 publications explore Mandarin-English semantic differences , Ukrainian grammatical number , and deep learning alternatives to discriminative frameworks. His work consistently challenges traditional morphological theories by integrating discriminative learning with real-world phonetic data .

Richard Lemoine-Rodríguezمشاهده پروفایل

استاد

• Urban Ecology
• Remote Sensing
• Geoinformatics
• +۹ مورد دیگر

Dr. Richard Lemoine-Rodríguez is a postdoctoral researcher at the Institute of Geography and Geology under the Chair of Remote Sensing , Faculty of Philosophy, University of Würzburg. He also collaborates with the German Aerospace Center (DLR) . His interdisciplinary research bridges urban ecology , geoinformatics , and digital humanities to advance understanding of cities as complex socio-ecological systems. Education: PhD in Geography (2017-2022, Ruhr-Universität Bochum) MSc in Geography (2013-2015, UNAM) BSc in Biology (2008-2012, Universidad Veracruz) His methodological expertise includes multimodal data analysis , social media analytics (Twitter/BlueSky), NLP , and remote sensing techniques for studying urban heat islands, green infrastructure, land use changes, and socio-spatial inequalities. Current work focuses on the Geolingual Studies project (since 2022), exploring language-urban morphology interactions. Key research partnerships include collaborations with: Jakob Schwalb-Willmann (Earth Observation Research Cluster) Hannes Taubenböck (DLR/Technical University Munich) John F. Mas (UNAM) Luis Inostroza (University of Hamburg) Recent publications examine: Urban heat patterns through social media and satellite data Global urban form homogenization Migrant mobility analysis via Twitter Geospatiality of text data Ecological integrity assessments

• Machine Learning
• Computer Vision
• Autonomous Driving
• +۴ مورد دیگر

Ross Greer is an Assistant Professor in the Department of Computer Science & Engineering at the University of California Merced. He leads the Mi³ Lab, focusing on machine intelligence, human-agent interaction, and safe autonomous systems. Education : B.S. and B.A. in EECS, Engineering Physics, and Music from UC Berkeley (2015), M.S. in Electrical & Computer Engineering from UC San Diego (2018), Ph.D. in Electrical & Computer Engineering at UC San Diego (2021) under Mohan Trivedi and Shlomo Dubnov. His research explores computational intelligence for open-world adaptability, robustness to rare events, and safety in chaotic environments. Key applications include autonomous driving, driver state analysis, trajectory prediction, and AI-assisted musical creativity. Recent publications emphasize vision-language models, active learning for 3D object detection, and safety metrics. Awards include the 2024 Interdisciplinary Research Award, Henry Booker Award for Ethical Engineering, and multiple best poster/grand prizes. Scientific Awards : 2024 Interdisciplinary Research Award 2024 Henry Booker Award for Exemplary Ethical Engineering Postdoctoral Networking Fellowship (Germany's Academic Exchange Service) Grand Prize (AWS Automotive Day competition at IEEE Intelligent Vehicles Symposium, 2023) Best Poster Awards (2021/2023 Jacobs Research Expo) He also co-authored the textbook Deep and Shallow: Machine Learning in Music and Audio (Chapman & Hall, 2023) and serves as a music director for UCSD's Symphonic Student Association and UC Merced's marching band.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +۴ مورد دیگر

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

• Medical Informatics
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

David Bani-Harouni is a researcher at the Chair of Computer Aided Medical Procedures at Technische Universität München (TUM). His work focuses on Medical Informatics , Artificial Intelligence , and Deep Learning , with an emphasis on Clinical Decision Support and Medical Image Analysis . Research Interests : Large Language Models (LLMs), Vision Language Models (VLMs), interpretability in deep learning, multimodal clinical decision support, and medical image analysis. Teaching : He contributes to lectures and practical courses such as Computer Aided Medical Procedures I , Medical Augmented Reality , and Deep Learning for Medical Applications . Publications : His research spans reinforcement learning for clinical decision-making, multimodal operating room datasets, toxin prediction systems (e.g., ToxNet), and graph convolutional networks for intoxication prediction. Contact : david.bani-harouni@tum.de

• Medical applications of vision-language models
• Multimodal deep learning for medical applications
• Holistic clinical decision support systems
• +۲ مورد دیگر

Matthias Keicher is a Postdoc and Research Manager at the Chair for Computer Aided Medical Procedures at the Technical University of Munich , affiliated with the IFL Lab at Klinikum Rechts der Isar. His work focuses on deploying AI for clinical applications, particularly vision-language models and large language models for structured report generation and decision support systems. Education: Dipl.-Ing. in Mechanical Engineering and Management from TUM (2006-2013) Industry Experience: Former CTO and Managing Director at SurgicEye GmbH (2016-2018) Research Interests: Medical Vision-Language Models (VQA, structured reporting) Multimodal Deep Learning for diagnostics Interpretable AI with generative models Decision support systems integrating patient data Article Trends: Over 2024-2014, his publications span surgical phase recognition (TeCNO), vertebral fracture grading (iMIMIC best paper), chest X-ray classification (FlexR), radiology report generation (RaDialog), and toxin prediction (ToxNet). Keywords include Medical Imaging, Graph Networks, Language Models , with subfields like 3D Computer Vision, Federated Learning, Clinical Reasoning . Scientific Awards: MICCAI iMIMIC 2023 Best Paper Teaching: He organizes two lectures ( Computer Science for Medical Students , Innovation Generation in Healthcare ) and tutors courses such as Deep Learning for Medical Applications and Machine Learning in Medical Imaging . Labs & Teams: Works at the IFL Lab (Intelligent Future Lab) in Munich, leading a research team funded by the DIVA project focused on vision-language models in clinical settings.