Find Academic Supervisors

• Human-Computer Interaction
• Accessibility Technology
• Augmented and Virtual Reality
• +4 more

Yuhang Zhao is a researcher affiliated with the University of Wisconsin-Madison, focusing on advancing accessibility in augmented reality (AR), virtual reality (VR), and human-computer interaction. His work emphasizes assistive technologies for individuals with visual impairments, disabilities, and transgender users, addressing challenges in navigation, communication, and social inclusion. Key contributions include systems like VisiMark (AR landmark augmentation), AI-Vision (image exploration for the visually impaired), and GazePrompt (gaze-aware reading enhancements). Zhao collaborates extensively with institutions like Beijing Institute of Technology and leads projects funded by the National Research Data Infrastructure (NFDI) in Germany. Affiliations : University of Wisconsin-Madison, Shanghai IC Research and Development Center, Beijing Institute of Technology Education : Not explicitly stated in text, but inferred to hold advanced degrees in computer science or related fields Research interests span AR/VR accessibility, assistive technologies, and inclusive design for marginalized groups. His publications address topics like low-vision navigation, disability representation in social VR, and ethical AI applications. Zhao’s methods integrate eye-tracking, multimodal interfaces, and user studies to validate practical solutions. Current projects explore AI-driven accessibility tools and inclusive avatar systems for people with disabilities. Grants and collaborations involve multidisciplinary teams addressing challenges in healthcare, emergency response, and education. Future work aims to scale accessibility solutions through open frameworks and participatory design.

• Natural Language Processing
• Cyberbullying Detection
• Semantic Web
• +3 more

Dr. Maral Dadvar is a Research Fellow at Stuttgart Media University, specializing in Natural Language Processing (NLP) and its applications to social challenges. She holds a PhD in Computer Science from the University of Twente, Netherlands, where her doctoral work focused on cyberbullying detection in social networks. Her current research integrates NLP with identity resolution techniques for the FID Judaica project, aiming to enhance data integration in Jewish cultural heritage domains. Key areas of expertise include cyberbullying detection algorithms, domain-specific knowledge bases (e.g., JudaicaLink), and ethical AI applications. Her work bridges technical innovations with societal needs, addressing issues like online safety and cultural heritage digitization. Dadvar collaborates internationally with institutions like LIBER and the European Research Libraries network. Publications span interdisciplinary topics such as conservation conflict analysis using NLP, linked open data standards, and multilingual entity disambiguation. She actively mentors students pursuing Bachelor’s and Master’s theses in these domains. Her research emphasizes practical solutions for real-world challenges, combining computational methods with human expertise.

Gero LückemeyerView profile

Professor

• Business Process Management
• Augmented Reality/Virtual Reality
• Digitalization
• +3 more

Professor Gero Lückemeyer serves as Dean of the Faculty of Surveying, Computer Science and Mathematics at HFT Stuttgart since 2024. Previously, he held the position from 2020–2024 and has been the Dean of Studies for the Bachelor of Computer Science since 2013. He leads the VirtuS Laboratory for Virtualized Systems, established in 2010, and holds a professorship in Business Informatics with a focus on Business Process Management since 2006. His expertise spans agile process management, AR/VR integration in digitalization, and innovative teaching/learning methodologies. Academically, he earned a PhD in Natural Sciences (Dr. rer. nat.) from the University of Cologne in 2000 and a Diploma in Business Information Systems in 1998. His professional background includes IT consulting and project leadership in telecommunications at sd&m AG (later CapGemini sd&m) from 2000–2006. Research interests include cybersecurity compliance (e.g., ISO/IEC 27001), digital teaching tools (e.g., Moodle integrations), and applications of machine learning in smart systems. His work emphasizes AR/VR for education and industry, as seen in projects like SensAR and DigiLab4U. He has received awards for research relevance (Cologne Prize, 2005) and educational excellence (Helmholtz-Gymnasium Award, 1995). Key contributions include developing the VirtuS Lab, leading the MoJEC and Moodle DTT tools for education, and pioneering COaaS frameworks for HPC. His articles focus on optimization using reinforcement learning, cybersecurity standards, and educational technology innovation.

Karsten MorisseView profile

Professor

• Media Informatics
• Theoretical Computer Science
• Algorithms and Data Structures
• +7 more

Prof. Dr. Karsten Morisse is a Professor of Media Informatics at Osnabrück University of Applied Sciences, where he has been teaching since 2000. Starting in March 2025, he will serve as Dean of the Faculty of Engineering and Computer Science. His academic career spans over three decades with significant contributions to computer science education, particularly in the development and implementation of the Inverted Classroom Model (ICM). Morisse has established himself as a leading figure in educational innovation, blending traditional computer science instruction with modern pedagogical approaches. Professor of Media Informatics (since 2000) Dean of Faculty of Engineering and Computer Science (from March 2025) Program Coordinator for Computer Science - Media Informatics (2019-2025) Head of eLearning Competence Center (2009-2021) Prof. Morisse's research spans several interconnected domains with a strong emphasis on educational technology and human-computer interaction. His work focuses on developing innovative teaching methodologies, particularly the Inverted Classroom Model, which he has refined over more than a decade of implementation. Recent research has expanded into clinical movement analysis for musicians, creating diagnostic tools that bridge computer science with healthcare applications. His publications reveal a consistent thread connecting educational innovation with practical applications in healthcare, agriculture, and multimedia systems. Morisse has successfully secured numerous third-party funded projects, demonstrating the practical relevance and impact of his research. Analysis of Prof. Morisse's recent publications (2022-2025) shows a clear evolution from pure educational technology toward interdisciplinary applications. While maintaining his strong foundation in computer science education methodologies, he has increasingly focused on applying these approaches to healthcare contexts, particularly in physiotherapy and movement analysis for musicians. His work demonstrates a sophisticated integration of agile development methods with educational theory, creating novel learning frameworks that prepare students for real-world software development challenges. The publications also reveal growing interest in AI applications for agricultural technology and clinical decision support systems. Scientific Awards Deutsch-Österreichischer Software-Preis (1993) for the MuPAD computer algebra system Europäischer Software-Preis (1994) for the MuPAD computer algebra system Wissenschaftspreis des Landes Niedersachsen (2009) for collaborative work with Prof. O. Vornberger Campusemerge Award (2011) for innovative use of multimedia elements in higher education teaching Throughout his career, Prof. Morisse has supervised numerous student theses and developed innovative teaching approaches that emphasize active learning. His current research projects demonstrate significant external funding success, with major grants from DFG, BMBF, and other organizations. The PA.H|Lifetime.ai project (DFG, 2024-2029) represents his most substantial current funding, focusing on AI applications in healthcare education. His leadership extends beyond research, as evidenced by his upcoming role as Dean and his previous service on multiple university committees focused on IT infrastructure and educational innovation. The Media Laboratory at Osnabrück University of Applied Sciences serves as the primary research environment for his team's work on educational technology and movement analysis applications.

Katrin WolfView profile

Professor

• Human-Computer Interaction
• Virtual Reality
• Augmented and Mixed Reality
• +4 more

Prof. Dr.-Ing. Katrin Wolf is a Professor of Human-Computer Interaction at Berlin University of Applied Sciences and Technology (BHT). Previously, she held professorships at Hamburg University of Applied Sciences and Berlin University of Art and Design, serving as Vice Dean of Research. Her work focuses on novel technologies in HCI, including VR/AR/MR, wearable systems, and interactive exhibitions. She has led projects funded by DFG, BMBF, and EU, such as Real-time Intersensory Discomfort Compensation (DFG) and HapticIO (BMBF). Wolf’s research emphasizes intuitive interaction paradigms for digital tools, with applications in healthcare, cultural education, and smart environments. Education: PhD from T-Labs (TU Berlin), Postdoc at University of Stuttgart (2014-2015) Key roles: Program Chair (CHI 2021, TEI 2021), General Chair (MuC 2019) Research interests span haptic feedback, gesture control, and mixed reality interfaces. Notable projects include WINK (gesture wristband) and KIMRA (mixed reality exhibition design). Her work bridges art and science, as seen in exhibitions like 'art meets science' (2014) and 'Media Spaces' (ARS Electronica 2016). Publications emphasize multimodal interaction, including haptic surfaces, microgestures, and avatar-based self-perception. Awards include recognition through conference leadership and funded projects totaling over €10M in grants.

• Federated Learning
• Cybersecurity
• Edge Computing
• +4 more

Dr. Meikang Qiu is a faculty member at Texas A&M University Commerce (Department of Computer Science) and Columbia University (Department of Electrical Engineering). He received his PhD in Computer Science from the University of Texas at Dallas in 2007. His research spans distributed intelligence systems with current focus areas including federated learning security, efficient AI for edge devices, and multimodal learning applications in healthcare and finance. Qiu's research integrates machine learning with systems engineering, emphasizing privacy-preserving AI , robust distributed systems , and resource-constrained optimization . His recent work explores: (1) Defenses against adversarial attacks in federated environments, (2) Quantum-classical hybrid computing frameworks, and (3) Efficient model compression techniques for large language models. Publication analysis (2024-2026) reveals three dominant themes: (1) Security enhancements for distributed learning systems, (2) Efficiency optimizations for edge AI deployment, and (3) Cross-modal applications in medical and financial domains. His work consistently addresses scalability challenges in real-world AI systems.

• Deep Learning
• Audio/Video Content Analysis
• Biomedical Data Analysis
• +3 more

Dr. Markus Mühling is a Research Fellow in the Department of Mathematics and Computer Science at Philipps-Universität Marburg, working under Prof. Bernd Freisleben. His research focuses on deep learning applications for audio/video content analysis and biomedical data interpretation. He joined the group in 2015 and has contributed to projects involving wildlife monitoring, medical imaging, and heritage digitization. His work spans sensor networks, neural network architectures, and edge computing solutions for real-world challenges. Key research areas include: Deep learning for bioacoustic and visual species recognition Automated analysis of biomedical and histological images Edge-based AI systems for resource-constrained environments Content-based retrieval in video and image archives Publications highlight advancements in biodiversity monitoring (e.g., acoustic sensor networks), medical imaging (e.g., blood cell counting), and cultural heritage (e.g., organ tablature transcription). His work bridges theoretical machine learning with practical ecological and biomedical applications.

• Natural Language Processing
• Machine Learning
• Biomedical Informatics
• +7 more

Ning An is an active academic researcher primarily affiliated with Hefei University of Technology in China, with additional connections to Shandong University and Xi'an Jiaotong University. With over two decades of publication history spanning from 1998 to 2025, Dr. An maintains a highly productive research trajectory, particularly evident in the substantial publication output in recent years (2023-2025). Dr. An's research spans multiple interdisciplinary domains at the intersection of computer science and health informatics. Key research interests include natural language processing (particularly few-shot learning and named entity recognition), multimodal sentiment analysis, time series forecasting, causal inference, and health monitoring technologies. A significant portion of recent work focuses on applications in elder care technology and medical diagnostics, demonstrating a commitment to translating technical innovations into practical healthcare solutions. The publication trends reveal a strong focus on few-shot learning approaches for natural language processing tasks, with numerous 2024-2025 papers addressing nested named entity recognition. There's also a notable thread of research in multimodal sentiment analysis and time series forecasting, particularly using frequency domain approaches. The consistent collaboration pattern with researchers like Jiaoyun Yang, Lian Li, and Lili Jiang suggests a well-established research group or laboratory environment. Dr. An's work demonstrates significant impact across multiple venues including top conferences like ACL, NeurIPS, and IJCAI, as well as prestigious journals such as Engineering Applications of Artificial Intelligence and Expert Systems with Applications. The research has practical applications in healthcare, particularly in elder care monitoring, depression assessment, and non-invasive health monitoring systems. As a mentor, Dr. An has guided numerous researchers including Jiaoyun Yang, Lian Li, and Lili Jiang, who frequently appear as co-authors on publications. The collaborative nature of the work extends to international partnerships, with publications involving researchers from institutions across China and potentially beyond.

• Spiking Neural Networks
• Brain-Computer Interfaces
• EEG Analysis
• +4 more

Gang Pan is a Professor at Zhejiang University's College of Computer Science and Technology, where he leads research in neural networks, brain-computer interfaces, and neuromorphic computing. His work bridges computer science, neuroscience, and biomedical engineering, focusing on developing novel AI approaches inspired by biological neural systems. He maintains extensive collaborations with researchers including Shijian Li, Qian Zheng, and Huajin Tang. Professor Pan's research centers on spiking neural networks (SNNs) and their applications in brain-computer interfaces, medical diagnostics, and efficient neuromorphic computing. His work explores how SNNs can model biological neural processes while offering energy-efficient alternatives to traditional deep learning. Recent projects include EEG-based mental health diagnostics, neural decoding of visual perception, and battery-free neural recording systems. His approach integrates computational neuroscience with practical AI applications, particularly in healthcare contexts. Analysis of his 15 most recent publications reveals strong trends in neuromorphic computing, with particular emphasis on spiking neural networks for medical applications. His work spans from theoretical advances in SNN architectures to practical implementations in EEG analysis, mental health diagnostics, and neural interface hardware. The interdisciplinary nature of his research connects computer science, neuroscience, and biomedical engineering, with increasing focus on clinical applications of neural decoding technologies. Professor Pan actively mentors students and researchers, as evidenced by his numerous collaborative publications across multiple labs. His research is supported by significant grants enabling work on neuromorphic hardware, brain-computer interfaces, and medical AI applications. The consistent high-impact output demonstrates sustained funding support for his innovative research directions. His laboratory focuses on neuromorphic computing systems, brain-computer interface development, and neural signal processing. The research environment integrates theoretical AI development with practical hardware implementation, creating a pipeline from algorithm design to clinical application. The lab maintains strong connections with neuroscience researchers and medical professionals to ensure clinical relevance of their technological innovations.

• Computer Vision
• Deep Learning
• Object Detection
• +4 more

Zheng Ge is an active researcher specializing in computer vision and deep learning, with a prolific publication record spanning from 2018 to 2025. They have authored or co-authored 81 publications in top-tier venues including CVPR, ICLR, ECCV, and NeurIPS, demonstrating consistent research productivity and impact in the field of artificial intelligence. Dr. Ge's primary research interests focus on computer vision with particular emphasis on object detection, 3D perception, and multimodal learning systems. Their work bridges theoretical advancements with practical applications, especially in video understanding, image generation, and large vision-language models. Recent research directions show a strong focus on developing more efficient and capable multimodal systems that can handle complex visual tasks with human-like understanding. The analysis of their recent publications reveals a clear trend toward building more sophisticated multimodal systems that integrate vision, language, and temporal understanding. Their work on video generation, perception modeling, and instruction tuning demonstrates a strategic focus on advancing the capabilities of multimodal large language models while addressing practical challenges in scalability and robustness. While specific awards are not documented in the available publication records, Zheng Ge's consistent presence in top-tier computer vision and machine learning conferences indicates significant recognition within the research community. Their work has contributed to advancing state-of-the-art techniques in multiple subfields of computer vision. Dr. Ge appears to be actively collaborating with a large research team, frequently working with researchers such as Xiangyu Zhang, Jianjian Sun, Chunrui Han, and Liang Zhao. These collaborations span multiple institutions and research groups, suggesting a well-integrated position within the computer vision research ecosystem. While specific grant information isn't available in the publication metadata, the scale and scope of their projects indicate substantial research funding support. Based on publication patterns and technical focus, Zheng Ge appears to be part of a large research organization or lab specializing in computer vision and multimodal AI systems. Their work on technical reports for video foundation models and large-scale multimodal systems suggests involvement in industry-leading AI research initiatives with strong engineering capabilities.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Yin Chen is a Professor in the Department of Computer Science at South China Normal University, Guangdong, China. With an extensive publication record spanning over 25 years from 2000 to 2025, Professor Chen has established themselves as a leading researcher in multiple domains of computer science and artificial intelligence. Professor Chen's research spans artificial intelligence, machine learning, computer vision, natural language processing, and deep learning. Their work demonstrates exceptional breadth across theoretical and applied domains, with significant contributions to facial expression recognition, transformer-based language models, 3D human modeling, and optimization algorithms. Recent research shows particular strength in multimodal learning, with numerous publications integrating visual, textual, and audio information for advanced AI applications. The publication trends reveal a consistent output of high-impact research, with a notable acceleration in recent years. Professor Chen's work appears in top-tier venues including IEEE Transactions, ACM Multimedia, and various specialized journals. Their research demonstrates strong interdisciplinary connections, bridging computer science with applications in engineering, bioinformatics, and mathematics. Professor Chen collaborates extensively with researchers across institutions, particularly with Jia Li, Meng Wang, Richang Hong, and Xianghua Fu. These collaborations span multiple projects in affective computing, multimodal translation, and algorithm development. The research group appears to be active in both theoretical advancements and practical implementations of AI systems.

• Biomedical Signal Processing
• Machine Learning
• Image Processing
• +4 more

Jie Luo is a researcher affiliated with Harvard Medical School's Radiology Department, Brigham and Women's Hospital, and collaborates with institutions like Colorado State University and Beihang University. Their work spans biomedical signal processing, machine learning, computer vision, and wireless communication. Key research interests include neuroscience applications such as epileptic zone localization, AI-driven medical imaging with transformers, and telecommunications advancements in hollow-core fiber transmission. Publications reflect interdisciplinary expertise in robot evolution , steganography , and 3D reconstruction . Recent articles focus on integrating high-frequency EEG analysis robust point cloud merging quantum-classical signal co-transmission lightweight AI for industrial quality control across 2025 journals like Biomedical Signal Processing and Control and IEEE Transactions . No awards or student advising data are currently available.

• Machine Learning
• Computer Vision
• Medical Imaging
• +5 more

Wenwen Zhang is a Professor at the Department of Electronic Engineering, College of Information Science and Electronic Engineering, Zhejiang University. With an extensive publication record spanning from 2016 to 2025, Dr. Zhang has established herself as a prominent researcher in multiple interdisciplinary fields at the intersection of computer vision, machine learning, and sensor systems. Her work demonstrates significant contributions to medical imaging, sensor array systems, wireless communications, and AI-assisted applications. Dr. Zhang's research interests encompass a wide range of topics including medical image analysis, sensor array systems, wireless communications, and AI-assisted applications. Her work demonstrates particular expertise in developing innovative deep learning architectures for medical imaging tasks such as cardiac segmentation and nuclei detection, as well as creating sophisticated models for gas sensing and wireless communication systems. She has made significant contributions to the fields of one-shot object detection, medical image segmentation, and sensor fusion techniques, with her research often bridging theoretical advancements with practical applications in healthcare and engineering. Analysis of Dr. Zhang's recent publications reveals a strong focus on cutting-edge deep learning approaches applied to medical imaging and sensor systems. Her work shows increasing sophistication in model architectures, moving from traditional CNNs to more complex transformer-based and hybrid models. There's a clear trajectory toward more explainable and clinically relevant AI systems, particularly in medical applications. Her research also demonstrates growing interest in multimodal approaches, combining different types of data and sensors to improve system performance. Dr. Zhang maintains active collaborations with researchers at Zhejiang University, particularly with Yuanjin Zheng and Zhiping Lin in the field of electronic engineering and sensor systems. She also collaborates extensively with Fei-Yue Wang from the University of Chinese Academy of Sciences, evidenced by multiple publications on parallel vision frameworks. Her international collaborations include work with researchers from institutions in Canada on intelligent knee sleeves and other biomedical applications. Her publication record shows consistent productivity with 12 publications in 2025 (as of this writing), 25 in 2024, and 26 in 2023, indicating an active and growing research program across multiple high-impact journals and conferences.

• Affective Computing
• Machine Learning
• Human-Computer Interaction
• +12 more

Subramanian Ramanathan is a researcher at the School of Computing, National College of Ireland , specializing in affective computing, multimodal behavior analysis, and human-computer interaction. His work spans machine learning, computer vision, and neuro-signal processing. Key research areas: Affective Computing, Deep Learning, Stress Detection, Depression Biomarkers Notable collaborations: Roland Göcke, Abhinav Dhall, Nicu Sebe His publications focus on: EEG-based cognitive load estimation Head motion pattern analysis for mental health Deepfake detection systems Audio-visual saliency prediction Transformers in behavioral modeling Stress detection via multimodal fusion Recent work includes medical imaging applications for autism detection and computational advertising systems using emotion recognition. He contributes to open science through dataset creation (SALSA, DECAF) and collaborative research in affective computing.

• Speech Processing
• Audio Segmentation
• Speaker Verification
• +4 more

Alfonso Ortega Giménez is a Professor affiliated with the University of Zaragoza, Spain. His primary research focuses on speech and audio processing, including speaker verification, audio segmentation, and machine learning methodologies. He collaborates extensively with institutions like the Autonomous University of Madrid and the University of Arizona, contributing to projects such as the ViVoLab system for diarization and emotion recognition challenges. Key research interests include disentanglement learning, explainable AI, and robust speaker verification under varying conditions. He has authored over 130 publications in venues like ICASSP, INTERSPEECH, and IEEE journals, emphasizing advancements in deep learning for audio applications. His work often addresses challenges like domain adaptation, unsupervised learning, and anomaly detection in audio signals. Ortega’s contributions span system design for NIST challenges, emotion recognition, and broadcast domain analysis. Collaborations with co-authors such as Antonio Miguel and Eduardo Lleida highlight his role in multidisciplinary projects. His research bridges theoretical machine learning with practical audio applications, addressing real-world problems in speech enhancement and robustness.