جستجوی استاد راهنمای دانشگاهی

Prof. Dr. Pia Knoeferle is a leading academic at Humboldt-Universität zu Berlin , where she serves as a Professor in the Department of German Language and Linguistics . She is a principal investigator in the Collaborative Research Center (CRC 1412) focused on register phenomena and leads the Reaction Time, Eye-tracking, and EEG Laboratories . Her research spans psycholinguistics , cognitive neuroscience , and computational modeling of language , with a central interest in how real-time language comprehension interacts with social context , formality-register congruence , and morphosyntactic processing . Appointments: Professor at Humboldt-Universität (2024), CRC 1412 member Methodologies: Eye-tracking, EEG, Visual World Paradigm, ERP Her work investigates lifespan language processing (children, adults, older adults) through contextual cue integration , including emotional , spatial , and social context such as gender cues , eye gaze , and facial expressions . Key questions include: How do pragmatic and contextual factors modulate lexical and grammatical processing ? What representations underlie register sensitivity in spoken and written comprehension ? Recent articles (2022-2024) focus on register congruence effects in German sentence processing, age-related differences in formality-register anticipation , and interactions between register and morphosyntactic knowledge . Using eye-tracking and visual world paradigms , her team examines incremental integration of socially-situated context with verb-argument relations and grammatical constraints . Findings suggest subtle late-stage register effects and interference between pragmatic and syntactic processing . Her lab collaborates with researchers like Katja Maquate , Valentina Nicole Pescuma , and Camilo Ronderos , contributing to the Frame text of the Second Phase Proposal for CRC 1412 (2020) and subsequent reviews in Frontiers in Psychology (2023). The work emphasizes complementary methods to model register variability across languages , modalities , and cultural contexts .

• Psycholinguistics
• Language Processing
• Register Studies
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Tianmin Shu is an Assistant Professor in the Department of Computer Science at Johns Hopkins University , with a joint appointment in the Department of Cognitive Science . He is the founding director of the Social Cognitive AI (SCAI) Lab and was previously a Research Scientist at MIT, working with Josh Tenenbaum and Antonio Torralba. His research goal is to advance human-centered AI by engineering machine social intelligence —building systems that understand, reason about, and interact with humans in real-world settings. His work is inherently interdisciplinary, integrating machine learning, computer vision, robotics, and social cognition. Research Interests: Theory of Mind Reasoning: Developing models that infer human mental states from multimodal behavioral data. Embodied Assistance: Creating agents capable of assisting humans in physical environments through verbal and non-verbal collaboration. Learning from Human Feedback: Extracting reward-relevant preferences from rich human input to guide agent behavior. Social Scene Understanding: Recognizing and reasoning about group activities and social roles from visual and physical cues. Computational Social Cognition: Modeling how humans perceive and interpret social and physical interactions. Scientific Awards: Outstanding Paper Award at ACL 2024 for "MMToM-QA: Multimodal Theory of Mind Question Answering" Grants & Collaborations: Tianmin Shu has led or co-organized several high-impact workshops and tutorials, including the NeurIPS 2023 Tutorial on "Language Models Meet World Models" and the RSS 2024 Workshop on "Social Intelligence in Humans and Robots". His lab has also developed open-source platforms like VirtualHome-Social and SimWorld for multi-agent interaction research. Lab & Team: As director of the Social Cognitive AI (SCAI) Lab at Johns Hopkins University, Tianmin Shu leads a multidisciplinary team focused on building socially intelligent systems. His lab is located in Malone Hall 213 and collaborates closely with the Departments of Computer Science and Cognitive Science.

• Human-centered AI
• Machine social intelligence
• Theory of mind
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Prof. Jochen Hartmann holds the Digital Marketing professorship at the TUM School of Management (Munich). Previously, he was an assistant professor at the University of Groningen's School of Business and Economics and worked as a management consultant at McKinsey & Company. He earned his doctorate from the University of Hamburg and coordinated the DFG research group FOR 1452 (2019-2022). His research focuses on digital marketing and machine learning, particularly analyzing unstructured data (computer vision, NLP) and generative AI. Key themes include social media, algorithmic fairness, diversity in advertising, and human-machine interactions. Education: Ph.D. in Business Administration (University of Hamburg), Management Consulting experience at McKinsey & Company. Research interests combine cutting-edge AI techniques with marketing challenges. Recent work explores generative AI's impact on advertising, algorithmic bias in finance, and visual search innovations. His text/image mining studies rank among top-cited articles in marketing journals like the International Journal of Research in Marketing and Journal of Marketing Research. Awards include the EMAC-Sheth Sustainability Award, Lindau Nobel Laureate Meetings' Young Economist distinction, and multiple best dissertation awards. Grants: Led DFG-funded research group (2019-2022). Affiliated with Columbia Business School (visiting scholar) and Mannheim Business School (lecturer in machine learning). Labs/Teams: Active in interdisciplinary research groups focusing on AI applications in marketing and business analytics.

• Digital Marketing
• Machine Learning
• Computer Vision
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Dr. Anett Hoppe is a research staff member at the Leibniz Information Centre for Science and Technology (TIB) in Hannover, Germany, where she works in the Visual Analytics research group. Her research focuses on the intersection of artificial intelligence, education technology, and information science, with particular emphasis on how people learn through search processes and educational video consumption. Dr. Hoppe completed her academic journey with: Ph.D. in Semantic Web technologies for online user profiles from the University of Burgundy, Dijon, France Her primary research interests span Search as Learning, software-based support for scientific reproducibility, and ethical considerations in computer-based decision making. She investigates how visual elements, reading sequences, and AI technologies impact knowledge acquisition during web search and educational video consumption. Her work bridges human-computer interaction, educational psychology, and information retrieval to create more effective learning experiences, with recent publications examining the role of large language models, vision-language models, and visual complexity in educational contexts. Analysis of her recent publications (2024-2025) reveals a strong interdisciplinary focus combining computer science, educational psychology, and information science. Her research examines video-based learning effectiveness, knowledge gain prediction, educational resource discovery, and the impact of visual elements on learning outcomes. She consistently explores how AI technologies can be leveraged to enhance educational experiences while maintaining attention to ethical considerations and scientific reproducibility. Dr. Hoppe maintains active collaborations with researchers across multiple institutions, with frequent co-authorship patterns indicating strong research partnerships, particularly with Ralph Ewerth and other members of the Visual Analytics group at TIB. Her work supports TIB's mission to advance knowledge infrastructure and scholarly communication through innovative technological solutions while directly addressing practical challenges in educational technology and information retrieval.

• Search as Learning
• Educational Technology
• Artificial Intelligence in Education
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Dr. Hilde Kuehne is a Professor at the University of Tuebingen and a key researcher at the Tuebingen AI Center. She holds affiliations with MIT-IBM Watson AI Lab and Goethe University Frankfurt, with a focus on computer vision, multimodal learning, and explainable AI. Her work bridges vision-language models, audio-visual alignment, and self-supervised methods. Co-organizer of the New Frontiers in Associative Memories workshop @ ICLR 2025 Member of the Scientific Advisory Board of the Carl-Zeiss-Foundation Contributor to CVPR 2025's UTD dataset for unbiased video benchmarks Her research addresses critical challenges in: Explainability for Vision Transformers (LeGrad) Fine-grained audio-visual alignment (CAV-MAE Sync) Zero-shot visual recognition automation (Meta-Prompting) Spatio-temporal grounding without annotations Recent collaborative work spans 15+ publications across CVPR, NeurIPS, ICCV, and ICLR, with emphasis on multimodal foundation models, dataset bias mitigation, and differentiable logic networks. She actively contributes to workshop organization and peer review as evidenced by her involvement in CVPR 2025 and ICLR 2025 program committees.

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
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Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.

• Computer Science
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Zhongliang Jiang is a senior research scientist and leader of the Robotics and Ultrasound team (RobUSt) at the Chair of Computer Aided Medical Procedures (CAMP) at Technische Universität München. He holds a Ph.D. in computer sciences (summa cum laude) and has authored/co-authored over 40 top-tier publications in robotics and medical imaging. His research focuses on robotic ultrasound systems, medical image processing, and robotic learning. Education: Ph.D. in Computer Sciences, TUM (2022, summa cum laude) M.Eng. in Harbin Institute of Technology (2017) Research Assistant at SIAT (2017-2018) Research Interests: Medical Robotics: autonomous robotic ultrasound systems Image Processing: RGB-D/ultrasound segmentation, registration Robotic Learning: reinforcement/imitation learning Robotic Control: MPC, shared control, human-robot interaction Professional Contributions: Associate Editor for ICRA 2024/2025 Guest Editor for IEEE TRO special issue on Robot-Assisted Medical Imaging Main organizer of RAMI workshops at ICRA (2023-2025) Awards: MICCAI 2023 Best Paper Runner-up Gold Medal for Master's Thesis (2017) Lab & Teaching: Leading the RobUSt team developing advanced robotic ultrasound solutions Teaching courses like Computer Aided Medical Procedures and Medical Augmented Reality Supervised over 15 Master/PhD projects in robotic ultrasound and medical imaging

• Medical Robotics
• Image Processing
• Robotic Learning
• +۳ مورد دیگر

Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.

• Medical Image Understanding
• Radiology Report Generation
• Deep Learning
• +۳ مورد دیگر

Prof. Dr. Bernd Skiera holds the first chair for electronic commerce in Germany at Goethe University Frankfurt am Main since 1999. He serves on the board of efl - The Data Science Institute and the Schmalenbach Society, while representing Germany at the European Marketing Community (EMAC). Chair of Marketing, Goethe University Frankfurt am Main Board member, efl - The Data Science Institute National representative, European Marketing Community (EMAC) His research focuses on MarTech/SalesTech integration, customer value management, online advertising analytics, data-driven pricing models, and the economic implications of internet privacy regulations. He leads the ERC Advanced Grant project on cookie usage restrictions' economic consequences. Recent publications examine dynamic pricing in digital markets, competition visualization using big data, and AI applications in marketing analytics. The ERC Advanced Grant research has produced empirical analyses of GDPR impacts on advertising ecosystems. 2015 Journal of Marketing Best Paper Award 2013 International Journal of Research in Marketing Best Paper Award Multiple MSI/H. Paul Root Award recognitions He has mentored 17 doctoral students who became professors globally, including at London Business School and LMU Munich. His work bridges marketing analytics with financial valuation through customer equity modeling.

• Marketing Analytics
• Electronic Commerce
• Online Advertising
• +۱۱ مورد دیگر

Mathias Niepert is a Professor at the Institute for Artificial Intelligence within the Faculty of Computer Science, Electrical Engineering and Information Technology at the University of Stuttgart. His research focuses on advancing machine learning techniques with applications in scientific computing, graph neural networks, and medical imaging. He is particularly known for contributions to physics-informed neural networks, equivariant models, and graph learning frameworks. Key research areas include: Scientific Machine Learning for PDEs and molecular modeling Graph neural networks and their theoretical limitations Medical vision-language models and multimodal learning Efficient neural network architectures (transformers, FNOs) Domain knowledge integration in deep learning His work often bridges theoretical foundations with practical applications, as evidenced by extensive publications (2018–2025) on topics like adaptive message passing, equivariant networks, and medical imaging systems. He has contributed to benchmark development through initiatives like PDEBench and pioneered methods for equivariant diffusion models and molecular representation learning. His current projects emphasize: Improving generalization in Fourier Neural Operators Addressing oversmoothing in graph networks Combining physics principles with neural architectures Medical AI applications through multimodal fusion

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
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Dominik Schnaus is a PhD Student at the Computer Vision Group within the School of Computation, Information and Technology at the Technical University of Munich. His research focuses on computer vision and deep learning, particularly in vision-language correspondence and uncertainty estimation in neural networks. Research Interests: 3D/4D reconstruction, vision-language models, neural network uncertainty, robotics Contact: dominik.schnaus@tum.de

• Computer Vision
• Deep Learning
• 3D/4D Reconstruction
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Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.

• Machine Learning
• Data Mining
• High-Performance Computing
• +۵ مورد دیگر

Prof. Torsten Wolfgang Kuhlen serves as a Universitätsprofessor at RWTH Aachen University, leading the Teaching and Research Area for Virtual Reality and Immersive Visualization within the Department of Computer Science. He is affiliated with Chair of Computer Science 12 (High Performance Computing), the Visual Computing Institute, and remains an integral part of the RWTH IT Center where his research group operates one of the world's largest Virtual Reality laboratories including the 30 sqm aixCAVE visualization chamber. The group maintains strong connections with Computational Science & Engineering Division, National High Performance Computing Center for Computational Engineering Science (NHR4CES), and VR in Science and Industry Network NRW e.V. Prof. Kuhlen's research spans virtual reality, immersive visualization, and multimodal 3D user interfaces with applications across simulation science, production technology, neuroscience, and medicine. His work combines basic research on advanced methods and algorithms with interdisciplinary collaborations involving RWTH Aachen institutes, Forschungszentrum Jülich, and industry partners. Recent publications demonstrate strong focus on audiovisual perception, immersive analytics, collaborative virtual environments, and practical VR applications in education and manufacturing. His research group has produced significant work on listening effort in virtual environments, immersive authoring techniques, and VR applications for scientific visualization. Notable projects include VRScenarioBuilder for automated vehicle testing and applications in monitoring additive manufacturing processes. The group actively participates in major conferences including IEEE VIS and EuroVis, with several award-winning contributions. Prof. Kuhlen has advised PhD students including Martin Bellgardt who recently completed his doctoral degree on "Increasing Immersion in Machine Learning Pipelines for Mechanical Engineering". The research group maintains state-of-the-art VR infrastructure including the aixCAVE facility which is open to all RWTH research groups.

• Virtual Reality
• Immersive Visualization
• Scientific Visualization
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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
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Joakim Nivre is a Professor at Uppsala University's Department of Linguistics and Philology. He is a leading researcher in computational linguistics, with a focus on dependency parsing, Universal Dependencies (UD) framework development, and multilingual NLP applications. His recent work explores LLMs in climate change discourse analysis, pharmacovigilance explainability, and historical text processing. Key research areas: Dependency parsing theory, Universal Dependencies standardization, LLM evaluation Collaborations: SweSAT-1.0 benchmark development, ClimateEval project, PARSEME integration His 2025-2023 publications demonstrate expertise in explainable AI for healthcare, synthetic data generation for idioms, and multilingual benchmark design. Notably, he co-developed SweSAT-1.0 to evaluate Swedish LLMs and contributed to typology-informed UD revisions. Despite extensive work in NLP, no scientific awards are mentioned in available texts.

• Computational Linguistics
• Natural Language Processing (NLP)
• Dependency Parsing
• +۵ مورد دیگر