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Raffaella Bernardi is an Associate Professor at the Faculty of Engineering, Free University of Bozen-Bolzano, Italy. She previously held academic positions at the University of Trento and the Free University of Bozen-Bolzano. Her research is centered on Computational Linguistics, with a focus on the interplay between language and reasoning in multimodal and conversational contexts. Research Interests: Her primary research areas include Natural Language Processing, Visually Grounded Conversational Models, Large Language Models (LLMs), Vision-Language Models (VLMs), and the cognitive abilities of AI systems. She investigates how language and reasoning interact, particularly in dialogue and multimodal settings. The analysis of her 15 most recent publications reveals a strong emphasis on multimodal reasoning, human-AI interaction, and the cognitive evaluation of language models. Her work frequently explores how AI systems understand and generate language in context, particularly in grounded and interactive environments. There is a consistent trend toward benchmarking, evaluation, and understanding the reasoning capabilities of modern LLMs and VLMs. Fellow of ELLIS (European Laboratory for Learning and Intelligent Systems) Best Paper Award, INLG 2023 Best Paper Nomination, Clic-it 2020 Best Paper Nomination, NL4AI 2020 Raffaella Bernardi has supervised numerous PhD and Master’s students, including Leonardo Bertolazzi, Claudio Greco, Alberto Testoni, and Sandro Pezzelle. She has been involved in significant research projects, often funded by industrial partners like SAP and EU initiatives. She has also been active in organizing scientific events and serving in leadership roles within the ACL and AILC communities. She leads a research group focused on multimodal language understanding and grounded interaction. Her team has developed benchmarks such as BD2BB (Be Different to Be Better) to evaluate the complementarity of language and vision in AI systems.

• Natural Language Processing
• Computational Linguistics
• Visually Grounded Conversational Models
• +9 more

Prof. Dr. Bernd Ludwig is a faculty member at the Institute for Information and Media, Language and Culture (I:IMSK) within the Faculty of Languages, Literature and Cultural Studies at the University of Regensburg. He has held the Professorship for Information Linguistics since August 2011, following a visiting role in the summer semester of that year. His academic journey began at Friedrich-Alexander-Universität Erlangen-Nürnberg, where he earned his PhD in 2004 and completed his habilitation in 2010. His research spans the interdisciplinary domains of natural language processing, human-computer interaction, and artificial intelligence, with a focus on spoken dialogue systems, user modeling, and context-aware assistive technologies. He leads projects like URWalking and ROSE, contributing to advancements in indoor navigation and intelligent mobile assistance. His recent publications reflect a diverse portfolio, combining AI and NLP with health informatics, social media analysis, and industrial applications. Trends show a growing emphasis on large language models, health recommendation systems, and the societal impact of AI. Scientific Awards: Zeitschrift für Medizinische Physik (ZMP) Autorenpreis 2022 AMiner Most Influential Scholar Award 2018 Nomination for Best Paper Award at MUM 2016 Nomination for Best Paper Award at KI 2010 SEW-EURODRIVE-Stiftung Studienpreis for Diploma Thesis (1997) Advising and Grants: Prof. Ludwig has supervised multiple doctoral candidates to completion, including Dr. Karema Al Subari and Dr. Robert Jackermeier, and currently mentors PhD students. He has secured research funding, notably a BMBF grant for the OPTAPEB project (2018–2021), and contributes to academic service as co-editor of Künstliche Intelligenz and organizer of major conferences such as CHIIR 2022 and COSIT 2019. Labs and Teams: He is a core member of the research team at the University of Regensburg, collaborating closely with Prof. Udo Kruschwitz, Prof. David Elsweiler, and others. His workgroup focuses on intelligent user interfaces, multimodal interaction, and practical applications of AI in everyday contexts.

• Natural Language Processing
• Human-Computer Interaction
• Speech Recognition
• +13 more

Bruno Dumas is a Professor at the Faculty of Computer Science, University of Namur, where he serves as Vice President of the Namur Digital Institute (NADI) and leads the EXUI research group at the Research Center on Information Systems Engineering. He obtained his MSc (2004) and PhD (2010) in Computer Science from the University of Fribourg, Switzerland, specializing in multimodal interfaces. His research explores human-computer interaction through multimodal interfaces, augmented/mixed reality, tangible interfaces, and AI explainability. Current investigations examine how computing evolution impacts daily life, spanning smart cities, adaptive interfaces, and information visualization. Dumas leads projects like Wal4XR (immersive tech for industry) and ARIAC (trustworthy AI foundations). Recent publications demonstrate focus areas: Wearable interaction in vehicular XR environments Voice assistant contextual understanding Medical AI for bronchoscopy diagnostics Rehabilitation interface engineering He teaches courses including Human-Computer Interaction, Information Visualization, and Internet of Things. As research lead, he has supervised 69 works and currently directs projects funded by Wallonia's Digital Innovation initiatives.

• Human-Computer Interaction
• Multimodal Interfaces
• Augmented Reality
• +5 more

Prof. Dr. Maximilian Röglinger is Chair of Business Informatics and Value-Oriented Process Management at the University of Bayreuth and Managing Director of the FIM Research Institute for Information Management. He is also Deputy Director of the Business Informatics branch at Fraunhofer FIT and previously served as an Adjunct Professor at Queensland University of Technology. He co-directs the Finance & Information Management program with TUM and co-moderates the Master's program in Digitalization & Entrepreneurship. His research spans digital transformation , process mining , AI in business processes , strategic IT management , and digital innovation . He investigates how data-driven methods can enhance process decisions, how digital technologies reshape customer interactions, and how organizations can structure digital transformation initiatives. His work combines theoretical rigor with practical application across industries. His recent publications (2023–2025) reveal a strong focus on generative AI integration in process mining and chatbots, human-AI collaboration in healthcare and logistics, process improvement systems , and digital ecosystems . He explores emerging topics like triadic delegation, explainable AI, and object-centric process mining from text. Scientific recognitions include: Paper of the Year 2019, Electronic Markets Best Associate Editor Award, ECIS 2021 Top 1.5% in BWL-Forschungsranking 2024 Certificate of Outstanding Contribution in Reviewing, European Journal of Information Systems 2023 Röglinger leads numerous publicly funded and industry-applied research projects with partners such as BMW, Siemens, Deutsche Bahn, Allianz, and Celonis. His work is supported by BMBF, ERDF, ERASMUS+, and Bavarian Research Foundation. He has co-founded the Digital Innovation Workshop, Digital Leadership Academy, and Fraunhofer Center for Process Intelligence. He is active in the international research community as Associate Editor of Business & Information Systems Engineering and editorial board member of Electronic Markets and Journal of Strategic Information Systems , and regularly organizes tracks at ECIS and BPM conferences.

• Business Process Management
• Process Mining
• Digital Transformation
• +15 more

Dr. Andy Lücking is a postdoctoral researcher at Goethe University Frankfurt, specializing in multimodal communication and cognitive semantics. He serves as Principal Investigator for the GeMDiS project within the ViCom SPP initiative, and previously held research fellowships at Université Paris Cité's Laboratoire de Linguistique Formelle and Frankfurt's Text Technology Lab. Current research focuses on neurocognitive semantics Developed iconic gesture theory with TTR/RTT frameworks Created multimodal corpora (FraGA, DoTT, TGVCorp) Active in computational educational linguistics Contributed to annotation tools (TextAnnotator, DependencyAnnotator) His work combines theoretical modeling with experimental methods, spanning over a decade of contributions to dialogue semantics, gesture-speech integration, and semantic role labeling. Recent projects explore VR-based multimodal research (Va.Si.Li-Lab) and diachronic dependency parsing. He has co-authored 20+ publications including proceedings in SemDial, LREC, and Springer HCI volumes. Collaboration network includes: Jonathan Ginzburg (dialogue theory), Alexander Mehler (computational linguistics), Alexander Henlein (VR research), and Max Planck Institute researchers. Key methodologies involve corpus creation, machine learning, and cognitive modeling of referential phenomena.

• Linguistic Theories
• Philosophical Semantics
• Multimodal Communication
• +7 more

Øystein Olsen is an Associate Professor at the Radiography BSc program, UiT The Arctic University of Norway, with research expertise in Magnetic Resonance Imaging (MRI) and neuroregenerative applications using manganese-based contrast agents . His work spans Central nervous system injury visualization Optic nerve regeneration tracking Alginate hydrogel contrast delivery systems Academic program evaluation Research trends include neuroimaging innovation , axon regeneration monitoring , and multimodal MRI techniques for neural repair assessment. Recent publications focus on 2019 education policy analysis 2015 radiography curriculum evaluation 2013 glial cell therapy for nerve repair

• Neuroscience
• Medical Imaging
• Regenerative Medicine
• +3 more

Philip S. Yu is a distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago's College of Engineering. He received his PhD from Stanford University and has established himself as a leading researcher in data mining, machine learning, and artificial intelligence. His research spans multiple domains including graph neural networks, recommendation systems, fake news detection, and large language models. Professor Yu's research interests primarily focus on Data Mining , Machine Learning , Artificial Intelligence , Graph Neural Networks , Recommendation Systems , Fake News Detection , and Large Language Models . His work addresses fundamental challenges in knowledge discovery from complex data structures, with particular emphasis on developing robust algorithms for real-world applications. His recent publications demonstrate strong interest in the intersection of traditional data mining techniques with emerging large language model architectures. Analysis of Professor Yu's 2025 publication record reveals significant contributions across multiple high-impact venues including IEEE Transactions, ACM journals, and top conferences. His work shows a clear trend toward integrating traditional data mining approaches with large language models, particularly in areas like recommendation systems, fake news detection, and privacy-preserving technologies. Many publications take the form of comprehensive surveys, indicating his role as a thought leader synthesizing emerging trends in the field. While specific awards aren't listed in the current data, Professor Yu's extensive publication record in top-tier venues and his position as senior author on numerous collaborative projects demonstrate significant recognition within the research community. Professor Yu maintains active research collaborations across multiple institutions, as evidenced by his co-authorship on numerous papers with researchers from different universities. His work often involves interdisciplinary approaches, bridging computer science with applications in security, healthcare, and social media analysis.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +6 more

Catalin Lisa serves as Head of Works at the Department of Chemical Engineering within the Faculty of Chemical Engineering and Environmental Protection at Gheorghe Asachi Technical University of Iasi. Holding a doctorate in chemical engineering (2010) with a focus on AI applications in polymerization, he has maintained continuous academic engagement since 1988 through teaching and research roles. His educational background includes an engineering degree in Macromolecular Compounds Technology (1988) and doctoral studies at the same institution. Research interests span mass/heat/momentum transfer , thermodynamic property evaluation , and pioneering AI applications in chemical engineering and ophthalmology . His publication record demonstrates consistent output in fluid dynamics, mixture thermodynamics, and medical AI applications. Recent scholarly contributions show increasing focus on medical diagnostics (particularly glaucoma progression modeling) while maintaining core chemical engineering research. His work integrates experimental data with neural networks and machine learning across domains from polymer science to ophthalmology. Led 22 competitive grants (2 as director) with significant research impact: 65 publications (43 ISI), 6 patents, 174+ Web of Science citations, and h-index of 10. Active in academic service as member of admission committees, scientific event organizers, and faculty electoral commissions. Maintains modern teaching practices through digital course materials (2024 e-learning modules for Hydrodynamic Operations) and laboratory innovations including new 2024 experiments on solid density measurement and aerodynamic profiling. Supervises course projects in chemical reactor design and process scaling.

• Mass Transfer
• Heat Transfer
• Momentum Transfer
• +5 more

Giovanni Frisoni is a Full Professor of Neuroscience at the University of Geneva's Faculty of Medicine, specializing in Alzheimer's disease research and diagnostics. He leads multiple European consortia including the AMYPAD project and contributes to European task forces for brain health services. His work bridges clinical neurology, neuroimaging, and biomarker development. Frisoni's research focuses on the intersection of neuroimaging biomarkers, cognitive decline, and Alzheimer's disease pathology. His team investigates amyloid and tau dynamics using PET imaging, develops blood-based diagnostic workflows, and explores novel therapeutic avenues including gut-brain axis interventions. Recent work demonstrates leadership in translating biomarker research into clinical practice through European intersocietal recommendations. His publication record shows a clear evolution from foundational neuroimaging studies (2020-2022) toward clinical implementation frameworks (2023-2025). Current work emphasizes blood biomarkers for tau-PET referral optimization, fecal microbiota transplantation effects, and anti-amyloid therapy guidelines. The research spans basic science (mouse models), translational studies (imaging-biomarker correlations), and health services research (diagnostic pathways). Key Scientific Contributions: Developed clinical implementation frameworks for amyloid and tau PET Pioneered blood biomarker workflows for memory clinic diagnostics Advanced understanding of gut-brain axis in Alzheimer's pathology Shaped European diagnostic guidelines through multisociety consensus Frisoni actively mentors junior researchers including Ribaldi, Altomare, and Ceyzeriat, who frequently co-lead projects. His work receives substantial European funding through consortia like AMYPAD and Euro-PAD. Current projects include the PreTAD dementia risk estimation initiative and RADAR-AD remote monitoring technologies. He maintains strong collaborations across European memory clinics and imaging centers. His laboratory focuses on multimodal biomarker integration, particularly combining PET imaging with fluid biomarkers and EEG. Recent technical advances include AI applications for generating FDG-PET images from amyloid scans and connectome identifiability metrics for Alzheimer's disease.

• Alzheimer's Disease
• Neuroimaging
• Biomarkers
• +4 more

Alberto José Bugarín Diz is a Full Professor of Artificial Intelligence at the University of Santiago de Compostela (USC), where he serves as coordinator of the Intelligent Systems Group (GSI), founded in 1990 and recognized as a competitive reference group by the Xunta de Galicia since 2006. He previously served as Deputy Director of the School of Engineering of the University of Santiago de Compostela (ETSE) and currently acts as the USC coordinator of the Degree in Artificial Intelligence taught across the three universities in Galicia. His research focuses on various areas of artificial intelligence and its applications, including natural language generation (Data-To-Text systems), soft computing, automatic learning, and approximate knowledge and reasoning representation. He has co-authored nearly 230 scientific papers and participated in over 63 R&D projects and innovation contracts, serving as principal researcher in 25 of them. His work spans healthcare applications, manufacturing processes, conversational agents, and linguistic descriptions of data, with particular emphasis on making AI systems more transparent and understandable to users. Prof. Bugarín Diz is actively involved in the AI community, currently serving as a Board Member of the Spanish Society for Artificial Intelligence (AEPIA), Secretary of the Spanish Society for Natural Language Processing (SEPLN), and as a member of the Organizing Committee of the 27th European Conference on Artificial Intelligence (ECAI), which took place in Santiago de Compostela in October 2024. He also participates in the TELSEC4TAI network about 'Trustworthy AI: technical, ethical, legal, cultural and socio-economic challenges.' His recent publications reveal a strong focus on explainable AI, trustworthy systems, and the application of fuzzy logic to complex reasoning problems, particularly in medical diagnosis, automotive manufacturing, and conversational systems. His work consistently bridges theoretical AI concepts with practical applications that address real-world challenges in healthcare, industry, and human-computer interaction. Board Member, Spanish Society for Artificial Intelligence (AEPIA) Secretary, Spanish Society for Natural Language Processing (SEPLN) Organizing Committee Member, 27th European Conference on Artificial Intelligence (ECAI 2024) Coordinator, Intelligent Systems Group (GSI) USC Coordinator, Degree in Artificial Intelligence (Galicia)

• Machine Learning
• Natural Language Generation
• Soft Computing
• +5 more

Siva Reddy is an Assistant Professor at McGill University, jointly appointed in the School of Computer Science and Department of Linguistics. He is also a Canada CIFAR AI Chair, core faculty member at Mila (Quebec AI Institute), and a research scientist at ServiceNow Research. His research focuses on advancing natural language understanding through symbolic and deep learning models, emphasizing reasoning, multimodal integration, and safety. Postdoctoral researcher at Stanford University PhD from University of Edinburgh Research themes include: Language representation learning for machine reasoning Conversational AI with retrieval-augmentation for trustworthiness Multi-language and safe AI deployment Integration of language with vision/speech modalities He has received prestigious awards including the Canada CIFAR AI Chair and Google PhD Fellowship. His advising portfolio includes active PhD students like Vaibhav Adlakha, Zichao Li, and Mehar Bhatia, with alumni such as Andreas Madsen (now at Guide Labs) and Edoardo Ponti (Assistant Professor at University of Edinburgh). He contributes to academic leadership as Senior Area Chair for EMNLP 2024 and Ethics Area Chair for COLM 2024.

• Natural Language Understanding
• Representation Learning
• Multimodal AI
• +6 more

Aysenil Belger, PhD is an Adjunct Assistant Professor in the Department of Radiology at the University of North Carolina at Chapel Hill and a Faculty Network Member of the Duke Institute for Brain Sciences as well as a Member of the Center for Brain Imaging and Analysis. Her translational research integrates neuroimaging, electrophysiology, and experimental psychology to elucidate the cortical circuits underlying attention, executive function, and emotion processing and their disruption across neuropsychiatric disorders. Education Ph.D., University of Illinois, Urbana-Champaign (1993) Research Interests Dr. Belger’s laboratory employs functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS) to investigate: Neurobiological substrates of autism spectrum disorder across development Neural signatures predicting conversion from clinical high risk to psychosis Impact of early childhood adversity on adult brain structure, function, and substance abuse trajectories Stress regulation and neural circuitry maturation during adolescence Her interdisciplinary approach combines experimental psychology paradigms with advanced neuroimaging and electrophysiological techniques to map sensory and cognitive deficits onto underlying neural circuit abnormalities. Publication Trends Across 2019-2020, Dr. Belger and collaborators produced a prolific body of work (>15 peer-reviewed articles) concentrating on three major themes: (1) reliability and predictive utility of EEG biomarkers (mismatch negativity, P300, gamma oscillations) for psychosis onset; (2) multimodal neuroimaging investigations of temporal lobe asymmetry, dynamic functional connectivity, and cortical genetics in schizophrenia; and (3) neuroendocrine and stress-response studies relating acute psychosocial stress to cognitive disorganization and psychosis risk in youth. Scientific Awards No specific awards listed in the provided text. Funding & Mentorship Dr. Belger currently holds or has held major federal grants including: A High-Performance 3T MRI for Brain Imaging (2021-2022) fMRI Assessment of Attention and Memory in PTSD (2006-2012) She actively mentors a diverse cohort of trainees ranging from undergraduate and graduate students to postdoctoral fellows and junior faculty, and teaches the graduate course Cognitive Clinical Neuroscience at UNC. Laboratory & Team Dr. Belger leads an interdisciplinary laboratory embedded within the UNC-Chapel Hill radiology department and maintains collaborative ties with the Duke Institute for Brain Sciences and the Center for Brain Imaging and Analysis. Her team participates in multi-institutional consortiums such as the North American Prodrome Longitudinal Study (NAPLS) to leverage large-scale datasets in order to accelerate discovery and translation.

• Cognitive Neuroscience
• Neuroimaging
• Autism Spectrum Disorders
• +7 more

Cynthia D'Angelo is an Associate Professor in the Department of Educational Psychology at the University of Illinois, Urbana-Champaign. Her research focuses on science education , technology-enhanced learning environments (including simulations, games, and augmented/virtual reality), and collaborative learning . She leverages multimodal data from online technologies to understand STEM concept acquisition and improve instructional practices. NSF-funded PI for $1.5M project on speech analytics in collaborative learning Division Chair of Curriculum, Instruction, and Teacher Education (CSTL) Active in NGSS-aligned curricular development and assessment Her publications demonstrate expertise in learning analytics for both traditional and immersive environments, with key contributions to collaborative problem-solving models and multimodal educational technologies . While known for science education research, her 2022 hybrid law degree program evaluation extended methodological insights to professional graduate education. Current teaching includes EPSY 555 (Advanced Educational Technologies) and CI 542 (Science Education & Philosophy of Science), emphasizing technology integration and data-driven pedagogy . Her work bridges cognitive science and educational design, creating tools that translate classroom dialogue into actionable insights.

• Science Education
• Technology-Enhanced Learning Environments
• Collaborative Learning
• +3 more

Lora Aroyo is Full Professor in the Department of Computer Science at VU University Amsterdam , leading the Web & Media group and actively contributing to the university’s Network Institute . Her research centers on hybrid human-AI systems for video understanding, crowdsourcing, data quality, and semantic search, with strong impact on digital humanities, cultural heritage, and interactive television. Research Interests: Human computation and large-scale crowdsourcing Hybrid human-AI architectures for multimedia analysis Data quality, annotation reliability, and disagreement modeling Semantic search and recommendation in cultural heritage Responsible AI and pluralistic alignment of generative models Across 2023–2025 her publications reveal a sharp focus on AI safety for generative and multimodal systems, including red-teaming of text-to-image models, evaluation of safety perceptions across demographic groups, and the creation of open benchmarks and datasets to support these goals. Earlier work (2018–2022) addresses video discovery, cultural heritage metadata, and human-centered data practices. Scientific Service & Funding: She has led major Dutch and EU projects in semantic search, event-driven multimedia access, and digital infrastructures for humanities research. She currently serves on the editorial board of PeerJ Computer Science . Labs & Teams: Prof. Aroyo heads the Web & Media group at VU, collaborates closely with the Network Institute , and coordinates interdisciplinary teams spanning computer science, digital humanities, and AI ethics.

• Crowdsourcing and human computation
• Hybrid human-AI systems
• Video understanding and multimedia semantics
• +4 more

Denys J.C. Matthies is an Associate Professor at Technical University of Applied Sciences Lübeck and affiliated with Fraunhofer IMTE Lübeck . He specializes in Human-Computer Interaction , particularly focusing on Wearable Computing , Tactile Feedback Systems , and Activity Recognition through smart footwear and body-worn sensors. Key Collaborations: Augmented Human Lab (NUS), Fraunhofer IGD, City University of Hong Kong Research Themes: Smart wearables, haptic interfaces, physiological sensing, assistive technologies His recent work explores: PhantomFolds (2025) - Spatial tactile feedback via fingernail-mounted LRAs PAVES (2025) - Pneumatic terrain simulation in VR Cyber-Placebo (2024) - Ethical implications of fake-AI in CPHS He has contributed to smart footwear systems like ShoeTect2.0 (2024) and SurfSole (2024), integrating capacitive sensing with neural networks for real-time activity and surface recognition. His work spans medical applications (e.g., 2025 study on hepatic encephalopathy screening) and novel interaction paradigms (e.g., Kavy conversational AI 2024).

• Human-Computer Interaction
• Wearable Computing
• Tactile Interfaces
• +4 more