Find Academic Supervisors

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +7 more

Jing Jiang is a prominent researcher at Singapore Management University, specializing in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence. His work spans diverse areas including Vision-Language Models, Machine Translation, Knowledge Graph Reasoning, Sentiment Analysis, and Social Media Discourse Modeling. Key contributions include frameworks for consistent client simulation in mental health counseling and counterfactual contrastive prefix-tuning for many-class classification. He has pioneered methods in zero-shot VQA with interpretable reasoning graphs , cross-lingual understanding with universal syntax , and modularized zero-shot architectures . His research often combines theoretical insights with practical implementations, as seen in works on stereotypical bias in vision-language models (VLStereoSet), tensorized self-attention for dependency modeling, and collaborative relation-augmented attention for knowledge graph completion. Jing Jiang's collaborations span global experts in NLP and AI, with co-authors from institutions like SMU, Waseda University, and Microsoft Research.

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +8 more

Bing Qin is a Researcher specializing in computational linguistics, artificial intelligence, and multimodal learning. Their work focuses on enhancing large language models' capabilities in temporal knowledge graph forecasting, cross-lingual alignment, and safety mechanisms. Core Research Areas: Knowledge graphs, multimodal systems, reasoning frameworks Technical Innovations: Analogical replay, gain signal estimation, cross-modal attention intervention Recent Trends: 2025 publications emphasize training-free methods and preference alignment in LLMs

• Natural Language Processing
• Graph Neural Networks
• Large Language Models
• +7 more

Liang Zhao is a Professor in the Department of Computer Science at Harbin Institute of Technology's School of Computer Science and Technology. His research spans multiple areas of natural language processing, artificial intelligence, and machine learning, with a particular focus on large language models, graph neural networks, and explainable AI systems. His research interests include natural language processing, graph neural networks, large language models, model explainability, federated learning, sparse attention mechanisms, hallucination mitigation, transformer length extrapolation, and uncertainty quantification. His work addresses fundamental challenges in modern AI systems, particularly in improving the reliability, efficiency, and interpretability of large language models. Professor Zhao's research output shows a clear trend toward addressing critical limitations in large language models, with recent work focusing on hallucination mitigation, model attribution, uncertainty quantification, and efficient attention mechanisms. His publications demonstrate strong interdisciplinary connections between NLP, machine learning theory, and practical system implementation. Through his extensive collaboration network with researchers including Xiaocheng Feng, Bing Qin, Weihong Zhong, and Yuntong Hu, Professor Zhao has established himself as a leading researcher in the Chinese NLP community. His work appears consistently in top-tier conferences including ACL, EMNLP, and NAACL.

Tim SchneiderView profile

Researcher

• Robotics
• Reinforcement Learning
• Robotic Manipulation
• +2 more

Tim Schneider is a Ph.D. student at the Intelligent Autonomous Systems lab of Technische Universität Darmstadt , working in collaboration with École Centrale de Lyon under the Aristotle project. His research focuses on robotics , particularly at the intersection of active perception , tactile sensing , and robotic manipulation . His work investigates how robots can develop environmental understanding through physical interaction , drawing parallels to children's exploratory behavior . Key projects include tactile reinforcement learning , active inference for manipulation , and visuo-tactile sensor fusion in dynamic tasks. Recent publications emphasize foundation models for tactile learning , sensor simulation (TacEx), and multi-modal control systems (AllmAN). He supervises theses on topics ranging from teleoperation feedback to haptic MNIST benchmarks , with a focus on reinforcement learning applications in robotic systems.

Nina WesterholtView profile

Researcher

• International Social Work Education
• Colonial-Critical Pedagogy
• Racism Critique in Higher Education
• +3 more

Nina Westerholt is a researcher and academic counselor at Niederrhein University of Applied Sciences, with a focus on international social work education, decolonial methodologies, and racism critique in academia. She coordinates the BASA (dual) study program and leads research projects on anti-racist teaching and curriculum reform. Her work spans collaborations with institutions in New Zealand, Fiji, Sweden, and Germany. Her educational background includes a diploma in education and additional qualifications in German as a foreign language. She has conducted seminars on counseling in migration contexts, social work practice, and academic writing, with international partnerships in New Zealand and Fiji. Current projects include decolonizing social work curricula and implementing anti-discrimination strategies. Westerholt is co-speaker of the German Association for Social Work's expert group on international social work and chairwoman of the university's Applied Social Sciences support association. She has presented at conferences on decolonial education, critical discourse analysis, and student diversity, with publications on study progress mentoring and curriculum development. Her research emphasizes discourse analysis of international social work programs, exploring how colonial and racist power structures shape knowledge production. She advocates for pluriversalism in teaching and research, challenging traditional hierarchies in academic internationalization.

• Natural Language Generation
• Natural Language Processing
• Low-Resource Language Modeling
• +12 more

David M. Howcroft is an Advanced Research Fellow in Natural Language Generation at the School of Natural and Computing Sciences, University of Aberdeen . He previously held research fellowships at Edinburgh Napier University, Heriot-Watt University, and Saarland University, contributing to major NLP projects including ASICA, NLG for Low-Resource Domains, and Madrigal. His work bridges computational linguistics, psycholinguistics, and statistical modeling. His research focuses on natural language generation , particularly in low-resource settings . He develops machine learning methods for data-to-text generation, creates novel corpora (e.g., for Scottish Gaelic), and improves human evaluation practices via crowdsourcing and rigorous statistical analysis. A key interest is the application of Bayesian nonparametrics and ordinal mixed-effects models to better understand and evaluate generated text. He also explores readability, referring expressions, and AI planning for rule-based NLG systems. His recent publications reveal a strong trend toward methodological rigor and inclusivity in NLP. He advocates for better evaluation standards, transparency in metric usage, and participatory design in NLP research. His work spans corpus development , evaluation methodology , low-resource language support , and human-centered NLP . He has led efforts to create datasets for under-resourced languages and to standardize best practices in human assessment. He has received small grant funding for projects such as Scottish Gaelic Generation for Exhibits and has contributed to software tools for data collection and evaluation. He mentors and collaborates widely, though no formal advisees are listed. He has developed backend systems and Android apps for healthcare applications, notably in melanoma patient support via the ASICA project. He is actively involved in research labs and teams including: ASICA Project Team (University of Aberdeen) NLG for Low-Resource Domains (Edinburgh Napier University) Madrigal Project (Heriot-Watt University) SFB 1102 Project A4 (Saarland University) Language Science and Technology (LSV, Saarland University) His scientific contributions are widely disseminated through top-tier venues such as ACL, EMNLP, and INLG. He maintains an active online presence with tutorials and technical blog posts on tools like OpenCCG and Treex.

Julia WunderleView profile

Researcher

• Natural Language Processing
• Large Language Models
• Deep Learning
• +5 more

Julia Wunderle is a doctoral researcher at the Chair of Data Science (Informatik X) at the University of Würzburg, affiliated with the Faculty of Mathematics and Computer Science and the Institute of Computer Science. She joined the chair in December 2024 after completing her master's degree in Computer Science at the same university. Office : Room 50.03.020, Center for Artificial Intelligence and Data Science (CAIDAS) Contact : Telephone +49 931 31-80351 Her research focuses on Natural Language Processing, Large Language Models, and deep learning techniques for German language tasks. Recent work includes hierarchical multi-label classification architectures, German opinion extraction via pointer networks, and developing language-specific encoder models. Key publication trends show specialization in German NLP, model architecture innovation, and applications of LLMs to semantic evaluation tasks. Her collaborations include researchers like Andreas Hotho, Jan Pfister, and Anton Zehe.

Andreas BullingView profile

Professor

• Human-Computer Interaction
• Computer Vision
• Ubiquitous Computing
• +4 more

Prof. Dr. Andreas Bulling is Full Professor of Computer Science at the University of Stuttgart , leading the Collaborative Artificial Intelligence research group at the Institute for Visualization and Interactive Systems. He is also a founding director of the Stuttgart ELLIS Unit and serves on multiple prestigious boards including IEEE Transactions on Visualization and Computer Graphics . Education: MSc in Computer Science (KIT), PhD in Information Technology (ETH Zurich) Research Interests: Human-Computer Interaction, Eye Tracking, Wearable Computing, Computer Vision, and Privacy-Preserving AI Scientific Leadership: UbiComp Steering Committee member, ACM ETRA General Chair (2020), and extensive editorial/guest editor roles Key Awards: ERC Starting Grant (2018), Henriette Herz Scout (2024), and multiple best paper awards at CHI, ETRA, and UIST Technical Contributions: Developed datasets (LPW, VisRecall++), created novel methods for gaze estimation, mental face reconstruction, and saliency prediction in visualizations His work bridges AI and Human-Computer Interaction with applications in immersive systems and healthcare technologies.

Timo GerkmannView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +3 more

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

• Knowledge Representation
• Spatial and Temporal Cognition
• Multimodal Interaction
• +3 more

Prof. Christopher Habel is a Professor in the Department of Computer Science at the University of Hamburg, leading the WSV work area. His research focuses on spatial and temporal cognition, multimodal interaction, and accessibility technologies. He holds the formal title of Head of Department WSV and is affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences. Habel's work bridges cognitive science, artificial intelligence, and human-computer interaction with an emphasis on assistive technologies for visually impaired individuals. Key research projects include the CINACS International Research Training Group, exploring auditory-haptic access to spatial graphics, and the development of multimodal comprehension systems integrating text, graphics, and tactile feedback. His contributions span theoretical frameworks in cognitive linguistics and practical implementations of verbal assistance systems for tactile maps and haptic graph exploration. Over 30 years of academic activity is reflected in his extensive publication record (1984–2015), with recent work emphasizing cross-linguistic studies, event recognition in virtual environments, and the design of multimodal interfaces for cognitive robotics. His research integrates computational models with empirical studies on human perception and language processing.

Stefan VoßView profile

Professor

• Logistics Optimization
• Metaheuristics
• Sustainable Port Operations
• +3 more

Prof. Dr. Stefan Voß is a Professor at the Institute of Information Systems within the Hamburg Business School (HBS) at the University of Hamburg. He serves on the HBS Dean's Office and focuses on logistics optimization, metaheuristics, and sustainable port operations. His work integrates AI, machine learning, and operations research to address challenges in transportation, supply chain management, and public infrastructure. Research Interests: Dr. Voß specializes in: Metaheuristic algorithms (e.g., fixed set search, GRASP, VNS) Optimization of port and maritime logistics (berth allocation, quay crane scheduling) AI applications in transportation (predictive analytics, emotion recognition) Sustainable operations (green port infrastructure, EV charging networks) Public transport efficiency (bus scheduling, delay prediction) Publications: Recent work emphasizes AI-driven logistics, robust optimization under uncertainty, and digital twin applications in ports. His 2025 studies explore domain-generalized emotion recognition and dynamic seaport automation. 2024 articles highlight multi-objective scheduling, EV fleet optimization, and sustainable container storage. Labs/Teams: Leads research at the Institute of Information Systems, collaborating on port sustainability projects (e.g., Port of Hamburg) and digital innovation in logistics systems.

• English Syntax
• Grammar in Discourse
• Discourse and Genre Theory
• +4 more

Professor Heidrun Dorgeloh is affiliated with Heinrich Heine University Düsseldorf (HHU), where she has served as an Außerplanmäßige Professorin for English Language and Linguistics since 2021. She is part of the Faculty of Arts and Humanities and contributes to the Department of English Language and Linguistics through research, teaching, and leadership roles. Education : Habilitation (2014), Ph.D. in English Linguistics (1994), studies at Justus-Liebig-Universität Gießen, London School of Economics, and Université de Paris II. Her research focuses on English syntax , discourse and genre theory , discourse processing , and argumentative discourse . She examines how syntactic structures interact with discourse functions across genres, including medical, scientific, and narrative contexts. Recent work explores genre bias in language models and argument annotation schemes. Her publications and grants reflect interdisciplinary approaches, including: Scientific awards : Beste Dissertation des Jahres 1995 (HHU Düsseldorf). Grants : eLearning and teaching grants for projects like 'Linguistics, Literature, and your Career' and 'Narrativity and evolution of English genres.' Dorgeloh has held leadership roles such as Head of the B.A. Students' Office and Vice President of the HHU Senate. Her career spans full-time lecturing, interim professorships, and visiting scholarship at Ohio State University.

• Phonetics
• Phonology
• Morphology
• +3 more

Prof. Dr. Ingo Plag is a faculty member at the Institute of English and American Studies , Heinrich-Heine-University Düsseldorf, specializing in English Language and Linguistics . His research focuses on Phonetics, Phonology, Morphology, Syntax, Creole languages, and English as a second language . Current Projects : DFG Project 'Form and meaning in English compounds' (2023-2026), CRC Project C08/DFG Project 'The semantics of derivational morphology' (2015-2026) Research Themes : Polysemy in derivation, compositionality of word-formation, semantic mechanisms, role of world knowledge, discriminative analysis of nominalizations His recent publications explore derivational morphology through distributional semantics , frame semantics , and quantitative methods , addressing topics like English prefixation, suffix competition (-ity vs. -ness), and nominalization semantics. He has presented at conferences including the International Cognitive Linguistics Conference and the German Linguistic Society. Contact: ingo.plag@uni-duesseldorf.de

• Digital Accessibility
• Human-Computer Interaction
• Socio-Technical Systems
• +4 more

Sven Bittenbinder is a Researcher at the University of Siegen , affiliated with the Faculty III (Information Systems and New Media). He contributes to the Department of Information Systems and is a core member of the IT for Ageing Society research group. Education: Diploma in Business Informatics (Wirtschaftsinformatik) from the University of Siegen (2003–2009), with specializations in Data Warehousing, IT Project Management, and System Integration. Research Focus: Bittenbinder specializes in Digital Accessibility , Inclusive Design , and Socio-Technical Systems . His work bridges Human-Computer Interaction and Disability Studies , addressing barriers in workplace accessibility, social service access, and international research collaboration. Recent projects include WERTE.IT (establishing inclusive IT practices) and iDESkmu (accessibility in SMEs). Article Trends: His publications since 2025 emphasize language barriers in citizen science , accessible recruitment/onboarding , and safe spaces for inclusion . Collaborative themes include socio-technical workplace adaptations , community-based participatory methods , and policy-driven accessibility . Projects & Labs: He works in PRAXLABS and Fab Lab Siegen , contributing to initiatives like FUSION , Mittelstand-Digital Zentrum Ländliche Regionen , and WERTE.IT . His focus is on creating inclusive digital infrastructures through participatory research.

Floriment KlinakuView profile

Researcher

• Software Engineering
• Cloud Computing
• Elasticity in Cloud Applications
• +5 more

Floriment Klinaku is a Researcher and Doctoral Researcher at the University of Stuttgart , affiliated with the Software Quality and Architecture Group . His work focuses on elasticity modeling , cloud-native systems , and performance optimization for modern software architectures. He contributes to projects like the Slingshot Simulator , which addresses autoscaling and resource management challenges in containerized environments. Research Interests include: Autoscaling mechanisms for cloud applications Elasticity and resilience in microservices Data stream processing in cloud environments Performance prototyping for containerized systems DevOps practices for automotive software His publications emphasize architectural solutions for scalability, resilience, and explainability in cloud-native ecosystems. Current trends in his work involve model-driven engineering approaches to coordinate autoscaling policies and quantify impact of design decisions on system behavior. No scientific awards are explicitly listed, and no advising/grant information is provided. He is part of the Software Quality and Architecture Group , contributing to research on cloud performance and elastic systems.