جست‌وجوی استاد و پژوهشگر

• Human–robot interaction
• Spoken language processing
• Image and audio processing
• +۳ مورد دیگر

Tetsunori Kobayashi is a Professor in the School of Fundamental Science and Engineering at Waseda University, Japan, where he has served since 1997. He is renowned for pioneering research in human–robot interaction, spoken language processing, and multimodal conversational systems, leading to over 230 refereed papers and an h-index of 35 (Google Scholar). Education: 1980 B.Eng. in Electrical Engineering, Waseda University 1982 M.Eng. and 1985 Dr.Eng. from Graduate School of Science and Engineering, Waseda University Research Interests: His work spans intelligent robotics , perceptual information processing , pattern recognition , image and audio processing , and conversational AI . He develops algorithms for real-time dialogue systems, multi-party conversation facilitation robots, and non-autoregressive speech recognition leveraging CTC and pre-trained language models. Recent Publication Trends: Since 2020 his group has advanced non-autoregressive end-to-end ASR (Mask-CTC, Intermpl, BECTRA), noise-robust attention , multi-look-ahead conversational ASR , and neural speaker diarization . They integrate BERT-style pre-training with CTC losses to accelerate inference while maintaining accuracy. Parallel work explores vision-and-language topics such as scene-graph generation, video semantic indexing, and personalized summarization for spoken news delivery. Scientific Awards: IEICE Fellow 2023 – for multi-modal multi-party conversation research IPSJ Fellow 2016 – for pioneering robot conversation studies JST Award for Academic Start-ups 2024 Best Paper Awards from IEICE, IEEE BTAS, ACM SIGGRAPH VRCAI, and several IPSJ workshop prizes Advising & Grants: He has mentored dozens of PhD and Master’s students who now lead in academia and industry. Major funded projects include JST CREST on conversational robotics, NEDO and JST-support for AI-based speech interfaces, and industry collaborations with NHK, OKI, and NEC. Labs & Teams: Kobayashi heads the Perceptual Computing Laboratory at Waseda, conducting interdisciplinary research with domestic and international partners such as MIT, ATR, and NHK Science & Technology Labs.

• Computational Linguistics
• Text Summarization
• Information Extraction
• +۶ مورد دیگر

Horacio Saggion is the Chair in Computer Science and Artificial Intelligence at the Department of Information and Communication Technologies, Universitat Pompeu Fabra. He leads the TALN Group and the Large Scale Text Understanding Systems Lab. His research focuses on Computational Linguistics, with specialties in Text Summarization, Information Extraction, and Semantic Analysis. He coordinates the Horizon Europe iDEM project on inclusive democratic spaces and previously led the SignON project for Sign Language Translation. Key technologies include the SUMMA Summarization system and the Dr Inventor Text Mining Library. Education: PhD, MSc, and Licenciatura in Computer Science. Research Interests: Text simplification for accessibility, sign language translation, misinformation detection, and ethical AI applications. His work bridges natural language processing with societal needs such as clear communication in public administration. Grants & Projects: Coordinator of iDEM (Horizon Europe), PI of SignON, Simplext, and Able to Include. Involved in BEA shared tasks and CLEF labs. Active in organizing workshops like TSAR at EMNLP. Labs & Teams: Head of TALN Group and Text Understanding Lab. Collaborations include Universitat Pompeu Fabra's interdisciplinary initiatives and industry partnerships for technology commercialization.

Shweta Yadavمشاهده پروفایل

استادیار

• Natural Language Processing
• Healthcare Informatics
• Biomedical Text Mining
• +۳ مورد دیگر

Shweta Yadav is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago (UIC). Prior to this, she was a Bridge to the Faculty (B2F) fellow at UIC and a postdoctoral research fellow at the U.S. National Library of Medicine, NIH. She holds a Ph.D. in Computer Science from the Indian Institute of Technology Patna, India. Education: Ph.D. in Computer Science, Indian Institute of Technology Patna, India Research Interests Her research focuses on the intersection of Natural Language Processing (NLP), Healthcare Informatics, Biomedical Text Mining, and Computational Social Science. She develops machine learning algorithms to advance AI applications in healthcare, particularly in medical document summarization , disease progression modeling , and health outcome prediction using electronic health records and social media data. Her work emphasizes interdisciplinary collaboration to address real-world healthcare challenges. Recent Publications Her recent publications highlight advancements in Multimodal Mental Health Analysis , Perspective-aware Healthcare Summarization , and Biomedical Relation Extraction . She employs techniques like Transformer models , Contrastive Learning , and Attention Frameworks to tackle low-resource settings and extract insights from complex data sources.

Anna Wilbikمشاهده پروفایل

استاد

• Data Fusion
• Intelligent Interaction
• Fuzzy Logic
• +۴ مورد دیگر

Anna Wilbik is a Professor in Data Fusion and Intelligent Interaction at the Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University (The Netherlands). Her research bridges data understanding and human-machine synergy in complex systems, focusing on multi-criteria decision making, explainable AI, and data fusion techniques. PhD in Computer Science (with honors), Systems Research Institute, Polish Academy of Science (2010) Postdoctoral Fellow, University of Missouri (2011) Stanford University TOP500 Innovators Program Alumnus Research Pillars: Intelligent human-machine interaction for joint decision making Data fusion methods for heterogeneous data integration Contextualized multi-criteria decision frameworks Fuzzy logic and linguistic summaries for explainability Federated learning systems Article Trends: Recent work focuses on intuitionistic fuzzy sets for knowledge-intensive processes, federated learning with uncertainty handling, and linguistic summarization techniques for interpretable AI. She actively explores explainability , collaborative business models , and driver behavior analysis through attention-based models. Professional Leadership: Vice-chair of IEEE Fuzzy Systems Technical Committee Organizer of IEEE World Congress on Computational Intelligence (2024)

Amal Zouaqمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Semantic Web
• +۵ مورد دیگر

Dr. Amal Zouaq is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. She holds the FRQS (Dual) Chair in AI and Digital Health, serves as Director of the LAMA-WeST research laboratory, and is an Associate Member of MILA. Her work bridges artificial intelligence with applications in digital health, cultural heritage, and educational technologies, positioning her at the forefront of interdisciplinary AI research in Canada. Her research focuses on Artificial Intelligence , particularly Natural Language Processing and the Semantic Web . Specific interests include knowledge representation, ontology learning, SPARQL query generation, bias mitigation in language models, and clinical text processing. Her work spans multiple domains including healthcare, cultural heritage, and educational technology, with emphasis on developing practical AI solutions that address real-world challenges in knowledge management and information extraction. Analysis of her recent publications reveals a strong trajectory in advancing NLP techniques for knowledge-intensive applications. Her work increasingly focuses on domain-specific applications in healthcare and cultural heritage, with growing emphasis on ethical AI considerations like bias mitigation. The research demonstrates progression from foundational semantic web technologies toward more sophisticated neural approaches while maintaining strong theoretical grounding in knowledge representation. Scientific Recognition: Holder of the FRQS (Dual) Chair in AI and Digital Health Dr. Zouaq has supervised 23 graduate students to completion, including 1 PhD and 22 Master's theses, with research spanning ontology learning, knowledge representation, and NLP applications. Her supervision record demonstrates consistent mentorship in cutting-edge AI research with practical applications across multiple domains. She actively serves on program committees for major conferences in knowledge engineering, data mining, and semantic web technologies. She directs the LAMA-WeST (Web, Semantics and Text) laboratory , which specializes in natural language processing and artificial intelligence research. The lab focuses on knowledge representation, semantic technologies, and their applications in healthcare, cultural heritage, and educational contexts. As a member of IVADO and MILA, she collaborates with leading AI researchers across Montreal's vibrant AI ecosystem.

Hao-Wen Dongمشاهده پروفایل

استادیار

• Generative AI
• Multimodal Learning
• Music Generation
• +۳ مورد دیگر

Hao-Wen Dong is an Assistant Professor in the Department of Performing Arts Technology at the University of Michigan, with an affiliation to the Computer Science and Engineering Department. His research focuses on Human-Centered Generative AI for content creation, emphasizing music, audio, and video domains. He holds a Ph.D. in Computer Science from UCSD, advised by Julian McAuley and Taylor Berg-Kirkpatrick. Affiliations: University of Michigan (Primary), UCSD (Ph.D.), National Taiwan University (B.S.) Research Pillars: Generative AI models for new domains, AI-assisted creative tools, and multimodal content creation His work spans music generation (e.g., MuseGAN), audio synthesis (e.g., ViolinDiff), and multimodal systems (e.g., TeaserGen). He has led over 25+ publications in top venues like ISMIR, ICASSP, and ICLR. He advises students in interdisciplinary projects and teaches courses on AI Music and Generative AI for Music/Audio Creation. Notable awards include the Doctoral Award for Excellence in Research (2024) and Rising Stars in AI (2024).

Grzegorz Chrupałaمشاهده پروفایل

دانشیار

• Computational Linguistics
• Artificial Intelligence
• Cognitive Science
• +۳ مورد دیگر

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence , Tilburg University, where he leads research in computational approaches to multimodal communication. Previously, he was a postdoctoral researcher at Saarland University's Spoken Language Systems group and earned his PhD from Dublin City University's School of Computing. His research bridges biological and artificial computation , focusing on enabling machines to learn language from multimodal data (speech, gestures, visual-auditory stimuli) as children do naturally. This involves developing and interpreting deep learning architectures, analyzing emergent representations, and advancing speech technology for under-resourced languages. Key themes include Visually grounded speech modeling Feature attribution and model interpretability Human-inspired learning paradigms BlackboxNLP workshop leadership His recent publications examine speech model reliability , lexical tone encoding , and contextual dependencies in NLP systems. He mentors a team of PhD candidates and alumni working on topics like user-centric interpretability, bioacoustics, and disentangled speech representations. He also serves on the board of the Dutch Open Speech Technology Foundation, chairs Interspeech 2025 tutorials, and contributes as an Action Editor for TACL.

Raphaël Troncyمشاهده پروفایل

استادیار

• Semantic Data Integration
• Knowledge Graphs
• Semantic Web
• +۳ مورد دیگر

Raphaël Troncy is an Assistant Professor at EURECOM's Data Science Department, specializing in Semantic Web technologies, Knowledge Graphs, and Natural Language Understanding. He teaches courses like 'Human-computer interaction for the Web' and 'Semantic Web technologies.' His research focuses on semantic data integration, knowledge graph applications, and recommender systems. Notable projects include DOREMUS (musical work graph), entity2rec (knowledge graph-based recommendations), and 3cixty (city exploration knowledge bases). He actively contributes to semantic web challenges and conferences, winning multiple awards including the 2018 Best Poster Award at ESWC and 2015 First Prize in the Semantic Web Challenge. Troncy's work spans cultural heritage digitization (e.g., Odeuropa olfactory data modeling), cybersecurity anomaly detection (NORIA-O ontology), and interdisciplinary projects like SILKNOW's silk textile knowledge graph. He leads development of tools like DAGOBAH for semantic table interpretation and KG Explorer for knowledge graph exploration. Education: Not explicitly stated in text Labs/Teams: Active in EURECOM's Data Science group, collaborating on projects involving knowledge graphs, AI, and semantic technologies

• Natural Language Processing
• Semantic Parsing
• Large Language Models
• +۳ مورد دیگر

Rui Zhang is an Assistant Professor in Computer Science and Engineering , with research expertise spanning Natural Language Processing , Large Language Models , and Semantic Parsing . His recent work focuses on enhancing multimodal consistency , fairness in summarization , and mathematical reasoning capabilities of LLMs. Key Research Themes: Text-to-SQL and cross-domain semantic parsing Multimodal learning (vision-language models) Fairness and bias mitigation in NLP tasks Efficient model training and prompt optimization Scientific Awards: National Science Foundation CAREER Award (2024) Grants & Projects: CAREER: Trustworthy Human-Centered Summarization (NSF, 2024-2029) addressing LLM trustworthiness through user-centric summarization frameworks. Article Trends: Recent publications emphasize LLM collaboration , compressed reasoning models , and cross-domain knowledge alignment . He explores multi-agent systems , mathematical reasoning , and vision-language limitations , particularly in geometric perception. Applications span bioinformatics (Alzheimer's biomarker discovery) and democratic AI frameworks.

• Computer Vision
• Artificial Intelligence
• Pattern Recognition
• +۶ مورد دیگر

Andrey Vladimirovich Savchenko is a prominent researcher and educator in computer vision and artificial intelligence at the National Research University Higher School of Economics (HSE) in Nizhny Novgorod. He holds multiple positions including Professor at the Faculty of Informatics, Mathematics, and Computer Science, Leading Researcher at the Faculty of Computer Science and Institute of Artificial Intelligence and Digital Sciences, and Academic Director of the "Artificial Intelligence and Computer Vision" educational program. His educational background includes: 2016: Doctor of Technical Sciences from Nizhny Novgorod State Technical University 2015: Academic title of Associate Professor 2011: Candidate of Technical Sciences 2008: Specialist degree in Applied Mathematics and Computer Science Savchenko's research focuses on computer vision, pattern recognition, and artificial intelligence, with particular emphasis on facial recognition, emotion analysis, and efficient deep learning algorithms. His work bridges theoretical foundations with practical applications, especially in mobile computing environments where computational resources are limited. He has developed innovative methods for making AI systems more efficient without significant loss in accuracy. His recent publications demonstrate a strong trend toward multimodal analysis, combining visual, audio, and textual data for more robust recognition systems. There's a clear emphasis on making AI systems more efficient, especially for mobile devices, and on developing methods that can work with limited computational resources while maintaining high accuracy. His work spans fundamental research on neural network architectures and practical applications in education, healthcare, and human-computer interaction. Among his notable scientific achievements: Gratitude from the Governor of Nizhny Novgorod region (2022) Multiple gratitude awards from HSE (2021-2022) Best Teacher Award (2018-2019) Leaders of IT Industry Award from NEYMARK IT Campus (2023) Academic Success Bonus at HSE (2011-2013) Savchenko has successfully supervised numerous master's students and currently mentors PhD candidates working on cutting-edge topics like large language models for recommendation systems and document analysis. He has secured significant research funding, including projects with Huawei, Sberbank, and the Russian Science Foundation, totaling millions of rubles. His laboratory focuses on developing efficient algorithms for computer vision and multimodal data analysis. He leads the Laboratory of Theoretical Foundations of Artificial Intelligence Models and has established strong industry partnerships that ensure his research has practical impact. His NVIDIA Deep Learning Institute certification demonstrates his commitment to staying current with the latest AI technologies.

Mo El-Hajمشاهده پروفایل

دانشیار

• Natural Language Processing
• Financial NLP
• Arabic NLP
• +۹ مورد دیگر

Dr. Mo El-Haj is a Reader (Associate Professor) in Natural Language Processing (NLP) at the College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam, and holds a visiting role at Lancaster University. He specializes in NLP with a focus on Financial NLP, Arabic NLP, and multilingual systems for under-resourced languages. Education: PhD in Computer Science (University of Essex, 2012), MSc in Information Systems (University of Jordan, 2008), BSc in Computer Information Systems (University of Jordan, 2005). Awards include the FHEA Fellowship (2021) and the 2016 BBC NewsHack Best Tool award. Research interests include text summarization, financial narrative processing, biomedical NLP, and corpus linguistics. He leads the VinNLP research group and has supervised/co-supervised over 20 PhD students. Notable projects include the Welsh Automatic Text Summarisation tool (ACC) and the FreeTxt bilingual analysis toolkit, funded by the Welsh Government and AHRC. Publications span 92+ works in top journals/conferences like Computational Linguistics and LREC. Active in organizing workshops (e.g., WACL-4, FinNLP) and has served as external/internal PhD examiner at UK universities.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Ahmed AbuRa'ed is a Researcher at the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra (UPF), Barcelona. He is affiliated with the TALN research group and the Large-Scale Text Understanding Systems Lab. His work focuses on advancing knowledge in scientific text summarization, information extraction, and machine learning. Education: PhD in Computer Science (2020), UPF, Barcelona, Spain M.Sc. in Computer Science (2015), University of Trento, Italy B.Sc. in Computer Information Systems (2007), An-Najah University, Nablus, Palestine Research Interests: Natural Language Processing (NLP), Machine Learning/Deep Learning, Semantic Web, Information Extraction, Data Mining, and Scientific Document Summarization. His projects include developing systems for automatic generation of state-of-the-art reports, scientific text summarization, and cross-document relation discovery. Publications Focus: His 15 most recent articles (2016–2021) emphasize advancements in scientific literature analysis, including citation detection, text simplification, and cross-document summarization. Notable works involve systems like LaSTUS/TALN for scientific text processing and OlloBot for Arabic health dialogue agents. Labs & Teams: Active member of the TALN research group and the Large-Scale Text Understanding Systems Lab at UPF's DTIC department. Open to collaborations in NLP, Machine Learning, and related fields via email or Skype.

• Special Education
• Reading Comprehension
• Digital Learning Environments
• +۲ مورد دیگر

Natalia Latini is a postdoctoral fellow at the Department of Education , University of Oslo, Norway. Her research focuses on reading comprehension , digital learning environments , and special education with an emphasis on specific learning disabilities. She actively contributes to research groups like Development, Equity, and Literacy in Learning Contexts (DEAL) , and projects including CrossREAD (Reading across mediums, devices, and contexts) . Education: Ph.D., Department of Education, University of Oslo (2022) Master in Special Education, Specific Learning Disabilities, University of Oslo (2017) Bachelor in Special Education, University of Oslo (2015) Psychology, year unit, University of Oslo (2012) Her research investigates how digital reading impacts comprehension, with a focus on multimedia integration , media multitasking , and self-efficacy in academic writing . She utilizes methodologies like eye-tracking and verbal protocols to analyze behavioral engagement and cognitive processing across mediums. Recent studies examine belief bias in adolescents and the role of main idea summarization in multiple-document comprehension. Key collaborations include work with researchers such as Ivar Bråten , Ymkje Elisabeth Haverkamp , and Helge Ivar Strømsø . Her publications span journals like Journal of Research in Reading , Literacy Research and Instruction , and Contemporary Educational Psychology . Natalia's research contributes to understanding how technology-mediated learning affects educational outcomes, particularly for individuals with learning challenges. She explores the interplay of screen size , text movement , and media multitasking on comprehension and motivation, offering insights for adaptive teaching strategies in digital contexts.

Mahdi Jampourمشاهده پروفایل

پژوهشگر

• Computer Vision
• Artificial Intelligence
• Digital Humanities
• +۳ مورد دیگر

Dr. Mahdi Jampour is a Researcher at the Centre for the Study of Manuscript Cultures (CSMC), University of Hamburg, and a member of the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA). He holds a Ph.D. in Computer Science (Artificial Intelligence) from Graz University of Technology (2016), with postdoctoral research at Iran Telecommunication Research Center (ITRC) (2016–2017). He previously served as Assistant Professor at Quchan University of Technology (2017–2024) and led Project RFA05 (2022–2025) focusing on visual pattern similarity in written artefacts. Education: Ph.D. in Computer Science (Artificial Intelligence), TU Graz, Austria (2016) Postdoctoral Fellowship, ITRC, Iran (2016–2017) Assistant Professor, Quchan University of Technology (2017–2024) Research Interests: Dr. Jampour specializes in applying AI and computer vision to cultural heritage preservation, including palimpsest analysis, historical document digitization, and pattern recognition. His work integrates generative models, deep learning, and semi-supervised methods to address challenges in manuscript analysis and multispectral imaging. Publications Trends: Recent work focuses on generative AI for palimpsest deciphering, dataset creation for sports and cultural heritage analysis, and facial expression recognition surveys. His articles bridge computer science with digital humanities, emphasizing cultural artifact preservation through technological innovation. Awards: Kazemi-Ashtiani Award (2019) Chamran Award (2017) KUWI Prize (2015) Marshal Plan Fellowship (2015) Best MSc Thesis Award (2009) Advising & Grants: Led UWA’s Project RFA05 (2022–2025) and contributed to international preservation initiatives like the Timbuktu Manuscript Training Project. His research is supported by grants from the Iran National Elites Foundation and the Iranian Ministry of Science. Labs & Collaborations: Active in CSMC’s labs, including the Written Artefact Profiling Guide and Mobile Lab Container projects. Collaborates with institutions globally on digitization and cultural heritage safeguarding.

Eugene Yangمشاهده پروفایل

پژوهشگر

• Information Retrieval
• Cross-Language Information Retrieval
• Multilingual Information Retrieval
• +۴ مورد دیگر

Eugene Yang is a Research Scientist at the Human Language Technology Center of Excellence (HLTCOE) at Johns Hopkins University, where he focuses on cross language and multilingual information retrieval, multilingual multimodal report generation, and retrieval-augmented generation systems. He received his Ph.D. in Computer Science from Georgetown University in 2021 under the supervision of Ophir Frieder, David D. Lewis, and Jeremy Fineman. His research spans multiple domains within information retrieval, with particular emphasis on high recall retrieval systems, technology-assisted review frameworks, and multilingual processing. He is the developer of TARexp, an open-source Python framework for Technology-Assisted Review experiments, which demonstrates his commitment to creating practical tools for the research community. Yang's publication record shows a clear trend toward increasingly sophisticated multimodal and multilingual retrieval systems, with his recent work focusing on retrieval-augmented generation evaluation, cross-language model distillation, and modular fusion approaches for complex information needs. His research bridges theoretical advances with practical applications in legal technology, healthcare informatics, and multilingual information access. As an active contributor to the information retrieval community, Yang has presented at numerous conferences including SIGIR, ECIR, and TREC, and has collaborated extensively with researchers across institutions. His work demonstrates a strong commitment to reproducibility and practical evaluation methodologies in information retrieval research.