جست‌وجوی استاد و پژوهشگر

• Mobile Robotics
• Underwater Robotics
• Autonomous Systems
• +۴ مورد دیگر

John Folkesson is an Associate Professor at the Department of Robotics, Perception and Learning at KTH Royal Institute of Technology. His research focuses on mobile robotics, underwater autonomous vehicles (AUVs), and Simultaneous Localization and Mapping (SLAM), particularly addressing challenges in dynamic underwater environments. He leads the AUV group within the Swedish Maritime Robotics Centre (SMaRC2.0) and supervises multiple PhD projects, including those funded by Ocean Infinity and Vinnova. Folkesson has pioneered work on sonar-based SLAM, bathymetric mapping, and autonomous underwater navigation without human intervention. He teaches courses such as Probabilistic Graphical Models (DD2420) and Applied Estimation (EL2320). Recent projects include developing neural rendering techniques for sidescan SLAM and automatic launch systems for AUVs in collaboration with Purdue University and SAAB. His research emphasizes long-term autonomy, environmental ambiguity, and sensor data interpretation in unstructured underwater scenarios. Education: PhD in Robotics (2005, KTH Royal Institute of Technology) Recent Funding: 2024 projects include ALARS (Vinnova), WASP WARA-PS, and industrial collaborations. Research Interests Folkesson's work spans underwater robotics, SLAM algorithms, and sensor fusion. Key areas include: Underwater SLAM and sonar modeling Bathymetric reconstruction using neural networks Autonomous decision-making in AUV missions Real-time terrain modeling and localization Articles Trends Recent publications emphasize neural networks for SLAM optimization, sonar data processing, and autonomous underwater systems. Themes include real-time bathymetric mapping, sensor fusion in dynamic environments, and neural rendering techniques for improving navigation accuracy. Folkesson's work bridges theory and practice, with applications in marine robotics and industrial surveys. Advising & Grants PhD supervision: AUV perception (2024), SLAM with Ocean Infinity, event-response AUV systems. Collaborations: Purdue University, SAAB, Ocean Infinity. Course responsibilities: Over 10 advanced robotics and engineering courses at KTH. Labs & Teams Lead of SMaRC2.0, KTH's official research center for maritime robotics. Active in developing AUV systems for long-duration missions, including ice-covered and deep-sea exploration.

Véronique Hosteمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Véronique Hoste is Senior Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy, where she serves as Department Head of Translation, Interpreting and Communication and Director of the LT3 language technology research team. She also holds the position of Research Director for the Faculty of Arts and Philosophy. Her educational background includes a PhD in Computational Linguistics from the University of Antwerp (2005) focused on optimization in machine learning for coreference resolution. Key research areas encompass machine learning for natural language processing, semantic and discourse modeling, including specialized work in event detection, entity/event coreference resolution, irony detection, and emotion analysis. Hoste's publication trends reveal strong interdisciplinary focus, with recent work bridging NLP with crisis communication, digital humanities, and ethical AI. Her team develops high-quality datasets (e.g., EmoTwiCS for emotion trajectories) and collaborates extensively with commercial partners on projects like SentEMO for aspect-based sentiment analysis. Current research emphasizes multimodal emotion analysis, fuzzy rough set methods for sentiment detection, and cross-document event coreference. Elected member of the Royal Flemish Academy of Belgium for Science and the Arts (KVAB) Francqui Chair appointment by Université Libre de Bruxelles (2023-2024) Co-founded LT3 spin-off AlfaSent (2024) for customer feedback analysis Authored first Dutch-language book on NLP: "Taaltechnologie ontrafeld" She actively supervises multiple PhD students on projects including Common-sense knowledge in irony detection (Common-sense), cross-document event coreference (Encore), and empathy modeling in conversational agents (FlandersAI). Her team secures funding through interdisciplinary collaborations like NewsDNA for news recommendation and METRICS for emotion trajectory analysis. Hoste also engages in public outreach through the "AI at school" initiative and advises on language technology integration in high school curricula. The LT3 laboratory under her leadership maintains strong industry partnerships and develops practical NLP tools including EmotioNL and Automatic Term Extraction systems, while advancing core research through projects like CLARIAH-VL for data curation.

• Digital Humanities
• Art History
• Urban History
• +۱۱ مورد دیگر

Isabella Di Lenardo is a Lecturer and Scientist at the Digital Humanities Institute (DHI) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as the coordinator of the EPFL Time Machine Unit and the European Local Time Machines. She holds affiliations across multiple departments, including DHI-GE, SAR-ENS, SHS-ENS, and EDDH-ENS, reflecting her interdisciplinary role in teaching and research. Her educational background includes a PhD in Theories and Art History, with postdoctoral and faculty experience at institutions such as INHA (Paris), EPFL, and IUAV (Venice). Her research spans Digital Humanities, Art History, Urban History, and GIS , with a focus on digital urban reconstruction, historical cadastres, and AI applications in cultural heritage. She employs advanced computational methods including machine learning, 4D modeling, and semantic segmentation to analyze historical maps, cadastral records, and art archives. Her work bridges humanities scholarship with computer science, particularly in reconstructing urban evolution and analyzing visual patterns. The recent publications reveal a consistent trend in AI-powered historical data analysis , especially in processing non-standardized historical documents, reconstructing urban spaces, and developing open-source tools for digital heritage. Her work frequently involves large-scale datasets from Venice, Lausanne, Paris, and Jerusalem, demonstrating a transnational and interdisciplinary approach. She has contributed to significant collaborative projects such as the Venice Time Machine , Parcels of Venice , and Time Machine Organization , often acting as a principal investigator or project leader. Her role involves coordinating diverse teams of researchers, engineers, and cultural institutions. Scientific contributions include: Development of the Morphograph tool for visual pattern recognition in art archives Automatic vectorization and analysis of Napoleonic cadastres Creation of 4D models for historical cities AI-driven text and pattern extraction from historical maps Building discovery engines for digital art history She actively teaches ex cathedra courses in Digital Urban History and Art History at EPFL and internationally. Her work in grants and projects emphasizes open data, reproducibility, and interdisciplinary collaboration. She has led research funded by organizations supporting digital heritage innovation. She is a key member of the Digital Humanities Laboratory at EPFL and the Time Machine Organization , where she fosters collaboration between computer scientists, historians, and cultural institutions. Her work in the Replica Project and ARCHiVe center highlights her leadership in digitizing and making accessible large art historical archives.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Els Lefeverمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۵ مورد دیگر

Els Lefever is an Associate Professor at Ghent University, where she works with the LT3 (Language and Translation Technology) research team. Her position focuses on computational linguistics and natural language processing research, with strong ties to both theoretical and applied aspects of language technology. Dr. Lefever earned her PhD in Computer Science from Ghent University in 2012 with her dissertation titled "ParaSense: Parallel Corpora for Word Sense Disambiguation." Her academic journey began as a computational linguist at the R&D department of Lernout & Hauspie Speech Products before transitioning to academia. Els Lefever's research spans multiple areas within computational linguistics with particular expertise in multilingual natural language processing. Her work focuses on computational semantics, cross-lingual word sense disambiguation, and multilingual terminology extraction. Recent research directions include automatic detection of irony in online text, argumentation mining in social media, sentiment analysis of financial news, language modeling for low-resourced languages, and computational approaches to Byzantine Greek epigrams. Her research demonstrates a consistent pattern of bridging theoretical computational linguistics with practical applications across diverse language domains and historical periods. Professor Lefever actively supervises PhD research on several cutting-edge topics including terminology extraction from comparable corpora, event extraction and sentiment mining of financial news, language modeling for low-resourced languages, argumentation mining in social media, and the automatic detection of links between Byzantine Greek epigrams. Her supervision portfolio demonstrates her commitment to advancing multiple frontiers of computational linguistics simultaneously. As an educator, Professor Lefever teaches courses in Terminology and Translation Technology, Language Technology, Localisation, Digital Text Analysis, and Digital Humanities. Her teaching reflects her research interests, providing students with both theoretical foundations and practical skills in language technology applications. The LT3 research group, where Professor Lefever is a key member, maintains strong connections with both academic and industry partners. The group has participated in numerous international conferences and shared tasks including SemEval competitions across multiple years, demonstrating consistent contributions to benchmark datasets and evaluation methodologies in natural language processing.

Marko Tkalčičمشاهده پروفایل

استاد

• Recommender Systems
• Computational Psychology
• User Modeling
• +۷ مورد دیگر

Marko Tkalčič is a Full Professor at the Faculty of Mathematics, Natural Sciences and Information Technologies (FAMNIT) at the University of Primorska in Koper, Slovenia. He is affiliated with the Department of Information Sciences and Technologies and leads research in psychologically-informed recommender systems through the HICUP lab. His academic journey includes prior roles as Associate Professor and Assistant Professor at the Free University of Bozen-Bolzano, and postdoctoral work at Johannes Kepler University and the University of Ljubljana. Research interests include computational psychology, user modeling, personality and emotion modeling, affective computing, and bias mitigation in AI. His work integrates machine learning, data mining, and user studies to enhance personalization systems by incorporating cognitive and emotional models. He focuses on music and film domains, with applications in social media, automotive interfaces, and multimedia. The recent publications demonstrate a strong trend toward human-centric AI , exploring eudaimonic and hedonic user experiences, cognitive load in driver assistance, music relistening behavior, privacy in group recommendations, and emotion-based video and music prediction. His research increasingly blends cognitive science theories (e.g., ACT-R) with practical recommender system design. University of Primorska Golden Plaque Award (2024) Stanford List of Top Scientists Cited Worldwide Best Reviewer Award at ISMIR 2020 Italian National Habilitation for Full Professor (2020) Italian National Habilitation for Associate Professor (2017) He actively supervises PhD students, including Elham Motamedi, and has secured teaching and research roles within his team. He is a member of the editorial board for UMUAI and Frontiers in Psychology, and has co-edited books and special issues on group recommender systems and human-centered AI. He leads the HICUP lab, fostering interdisciplinary research at the intersection of psychology and computer science.

Anna Feldmanمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Corpus Linguistics
• +۵ مورد دیگر

Anna Feldman is a Professor of Linguistics and Computer Science at Montclair State University, affiliated with the College of Humanities and Social Sciences. She holds a PhD from The Ohio State University and dual Master's and Bachelor's degrees from The Hebrew University of Jerusalem. Her research focuses on computational linguistics, natural language processing (NLP), and corpus linguistics, with specializations in morphological analysis, idiomatic expression detection, and censorship/disinformation detection. She leads the NLP Lab at Montclair and contributes to initiatives like the Computational Linguistics for Literature (CLfL) workshops and the NLP4IF series addressing internet freedom challenges. Education: BA & MA (Hebrew University of Jerusalem), MA & PhD (Ohio State University) Research interests include resource-light approaches to morphology, figurative language processing, and cross-lingual NLP applications. Recent work emphasizes euphemism detection, controversial language analysis, and ethical AI in censorship detection. She has organized major workshops such as CLfL and NLP4IF, and her lab develops tools like positional tagsets and learner corpus annotation frameworks. Her NSF-funded projects focus on low-cost linguistic resource creation and figurative language processing. Her publications span NLP conferences (ACL, NAACL, CICLing) and journals, with contributions to idiom recognition, sentiment analysis, and multilingual NLP. Supervised over 20 students in computational linguistics research across undergraduate and graduate levels. Current projects include euphemism detection datasets and AI ethics in healthcare consultations. Labs/teams: Director of NLP Lab at Montclair State University, core member of CLfL workshop series, and co-founder of NLP4IF initiative.

ZIYA TOKمشاهده پروفایل

دانشیار

• Translation Studies
• French Language Teaching
• Computer-Assisted Translation
• +۳ مورد دیگر

ZIYA TOK is an Associate Professor in the Department of French Translation and Interpreting at Kırıkkale University's Faculty of Humanities and Social Sciences. His academic focus lies in translation studies, particularly French-Turkish translation, and the teaching of French as a foreign language. His educational background includes: Undergraduate and Graduate: French Translation and Interpreting, Kırıkkale University Doctorate: French Language Education, Hacettepe University Professor Tok's research spans translation technology, terminology management, and the pedagogy of foreign language teaching. He investigates challenges in translating specialized texts (e.g., forestry, banking, and poetry) and the role of computer-assisted tools in translation. His work also addresses discourse analysis in translation, particularly reported speech, and the integration of translation in language education curricula. The following list highlights his primary research areas: Translation Studies French Language Teaching Computer-Assisted Translation Machine Translation Terminology Discourse Analysis in Translation Analysis of his recent publications (2020-2025) reveals a strong emphasis on practical translation problems between French and Turkish, with a growing interest in technological solutions. His work bridges theoretical translation studies with applied linguistic challenges in the Turkish context, particularly in the development of terminology resources and pedagogical methods for translation education. A comprehensive review of the available information did not reveal any mentions of scientific awards, fellowships, or major honors received by Professor Tok. While Professor Tok's role as a faculty member likely involves student supervision, the provided text does not specify any current or past students, nor does it detail any research grants he may have secured. There is no indication in the source material of any laboratory affiliations, research centers, or collaborative teams led by Professor Tok.

Sampritha Hassan Manjunathمشاهده پروفایل

پژوهشگر ارشد

• Ontology Learning
• Knowledge Graph Generation
• Relation Extraction
• +۳ مورد دیگر

Sampritha Hassan Manjunath is a Research Fellow at the Insight Centre for Data Analytics at the University of Galway, working under Prof. Paul Buitelaar in the Unit for Natural Language Processing (UNLP). Her primary focus includes ontology learning, knowledge graph generation, relation extraction, named-entity recognition, text analysis, and information extraction. MSc in Artificial Intelligence, University of Galway Her research integrates domain-specific knowledge to advance natural language processing (NLP) tasks, with industry collaborations involving Huawei, the EU, Tr3dent, and the University of Galway on projects like intent classification and automatic SDG mapping. Sampritha has published papers on knowledge graph tools and deep learning models for term recognition, leveraging BERT embeddings and the EEAP framework. She is affiliated with the Insight Centre for Data Analytics and previously gained software engineering experience at Intel Technologies and CGI Group Inc., specializing in Java, Agile, Scrum, and CI/CD practices.

Ivan Minevمشاهده پروفایل

استاد

• Bioelectronic Medicine
• Biomedical Engineering
• Neural Interfaces
• +۴ مورد دیگر

Professor Ivan Minev is a Professor of 'Electronic Tissue Technologies' at the Else Kröner Fresenius Center for Digital Health, part of the Medical Faculty at Technische Universität Dresden since 2023. He also heads a research department at the Leibniz Institute for Polymer Research Dresden. Previously, he held a visiting professorship in Intelligent Healthcare Technologies at the Automatic Control and Systems Engineering Department, University of Sheffield (2019–2023). He earned his PhD in 'Soft Neural Interfaces' from the University of Cambridge in 2012. His research focuses on bioelectronic interfaces, combining 3D printing and soft conductive materials to create implantable, wearable, and cell culture devices. His work aims to record biopotentials from neural, muscular, and cellular systems, leveraging machine learning and signal processing to extract diagnostic biomarkers or control signals for neuroprostheses. Collaborations with Sheffield’s School of Electrical and Electronic Engineering (SoEEE) aim to analyze experimental datasets for advanced data interpretation. Research interests include multi-modal neuromodulation, bioelectronic implants, and the development of hydrogels and 3D printing technologies for biomedical applications. He leads the ERC-funded IntegraBrain project, exploring multi-modal sensor-actuator networks for neural communication and treatment of neuropsychiatric disorders. Key honors include the Volkswagen Foundation Freigeist Fellowship (2017–2021) and an ERC Starting Grant (2019–2024). His work bridges materials science, biomedical engineering, and clinical applications, with grants totaling over €2.4 million.

• Natural Language Processing (NLP)
• Artificial Intelligence (AI)
• Translation Technology
• +۳ مورد دیگر

Ayla Rigouts Terryn is an Adjunct Professor at Université de Montréal's Faculty of Arts and Sciences, Department of Linguistics and Translation. She holds a Master's in Translation from the University of Antwerp and a PhD in Automatic Terminology Extraction from Ghent University. Her research focuses on improving language models through linguistically grounded approaches, emphasizing multilingual and specialized contexts. She leads the ACTER dataset project and co-leads research on lexical interactions between humans and machines. Research & Education PhD: Ghent University (D-Termine project) MSc: University of Antwerp (Translation) Mila (Quebec AI Institute) affiliation Research Interests : Automatic terminology extraction Computational linguistics Multilingual AI systems Bias analysis in large language models Specialized translation technologies Grants : FRQNT grant for translation bias research (2025-2028) FRQSC grant for lexical research team (2025-2030) Labs & Teams : LT3 Language and Translation Technology Team (Ghent University), Observatoire de linguistique Sens-Texte (OLST).

Marc Van Hulleمشاهده پروفایل

استاد

• Computational Neuroscience
• Brain-Computer Interfacing
• Artificial Intelligence
• +۴ مورد دیگر

Marc Van Hulle is a full Professor at the Laboratory for Neuro- and Psychophysiology , KU Leuven Faculty of Medicine, Department of Neurosciences. He serves as promotor for multiple long-term projects (2022-2028) at the intersection of biomedical engineering and neuroscience, including Alzheimer's early detection, Parkinson's brainstem-basal ganglia interactions, and advanced brain-computer interfaces (BCIs). His affiliations include the KU Leuven Brain Institute (LBI) and Leuven.AI artificial intelligence institute. Research Focus: Specializing in computational neuroscience, Marc Van Hulle pioneers innovative BCI systems, neurophysiological modeling, and biomedical signal processing. His work spans cognitive modeling, neural network analysis, and translational applications for neurological disorders through advanced signal decoding techniques. Publication Trends: Recent articles emphasize decoding speech and motor activities from brain signals (ECoG/EEG), Alzheimer's detection via graph network analysis, and VR-integrated SSVEP systems. Key methodologies include tensor regression, multiway modeling, and phase synchrony analysis, with consistent application of AI to biomedical challenges. Academic Contributions: He supervises multiple researchers including Aurelie de Borman, Axel Faes, and Qiang Sun. His projects as promotor include European Doctoral Network for Neural Prostheses , Adaptive multistate decoding , and Decoding imagined speech , demonstrating sustained leadership in neurotechnology research. Laboratory Context: The Laboratory for Neuro- and Psychophysiology includes PhD members working on neural computing and BCI systems, with former members having completed significant contributions in similar domains.

Anca Udristoiuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Medical Imaging
• Deep Learning
• +۴ مورد دیگر

Dr. Anca Udristoiu is an Associate Professor at the Department of Computers and Information Technology, University of Craiova's Faculty of Automatic Control, Computers and Electronics. She graduated from the same faculty in 2003 as a Software Engineering specialist and has been a PhD candidate in Computer Science since 2009 under Prof. Mircea Petrescu. Research Focus: Her work centers on AI-driven medical diagnostics, with emphasis on: Real-time analysis of endoscopic ultrasound (EUS) for pancreatic/liver pathologies Deep learning for cancer detection in biopsy samples Robotic navigation systems for minimally invasive procedures Computer-aided diagnosis frameworks for thyroid/liver disorders Teaching: Leads courses in Databases, Machine Learning, and Web Systems Engineering. Research Trends: Publications (2022-2025) demonstrate: Dominant focus on AI applications in endoscopic imaging (76% of recent works) Progression from theoretical models to clinical validation studies Integration of convolutional/long short-term memory neural networks Robotics development for endovascular/pulmonary interventions

Hong Cuiمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Semantic Annotation
• +۳ مورد دیگر

Hong Cui is a Professor at the University of Arizona's College of Information Science, where she leads the biosemantics research group. Her work focuses on developing machine learning and natural language processing algorithms to convert biodiversity literature into machine-readable Semantic Web formats, enabling computers to interpret scientific information previously accessible only to humans. Her research spans critical areas in computational biology: Machine Learning for Semantic Annotation of Taxonomic Descriptions Natural Language Processing in Biodiversity Informatics Ontology Engineering for Biological Domains Unsupervised Learning Applications in Phenomics XML Markup Systems for Scientific Literature Analysis of her 2012-2015 publications reveals consistent innovation in transforming taxonomic literature through tools like OTO (Ontology Term Organizer) and MARTT (Automatic Markup of Taxonomic Descriptions), with applications spanning phenotypic diversity description, botanical knowledge portals, and cross-lingual semantic annotation. Her work bridges computer science and biological sciences through semantic technologies. Dr. Cui serves as principal investigator on multiple National Science Foundation-funded projects, with methodologies adopted internationally by research groups in the US and abroad. She mentors students through her biosemantics research group, advancing computational approaches to biodiversity data. The biosemantics research group operates at the intersection of information science and biology, developing infrastructure for next-generation phenomics and semantic annotation systems that process both born-digital and digitized scientific literature.

Ayla Rigouts Terrynمشاهده پروفایل

استادیار

• Automatic Terminology Extraction
• Machine Learning
• Language and Translation Technology
• +۳ مورد دیگر

Ayla Rigouts Terryn is an Assistant Professor at KU Leuven, with a focus on language and translation technology. She previously served as a postdoctoral researcher at Ghent University's LT3 research group. Her research centers on computational terminology, particularly supervised machine learning approaches for monolingual and multilingual automatic term extraction. She developed the open-source ACTER dataset and organized the TermEval shared task, advancing methodologies in data-driven terminology research. Recent publications highlight her work on sequential labelling approaches (2022), hybrid term extraction frameworks (2019), and cross-lingual embeddings (2022). The ACTER dataset (2020) covers corruption, dressage, heart failure, and wind energy domains across English, French, and Dutch. FWO scholarship recipient for her EXTRACT project (2017) She co-lectures on Advanced Website Management at Ghent University, covering CMS development, web design principles, and team dynamics. Her tools, like D-Terminer, demonstrate practical applications of her research.