Ludovic TANGUY is a Professor at the Department of Language Sciences, University of Toulouse 2, where he conducts research within the Cognition, Languages, Ergonomics (CLLE) research unit. His academic journey includes a Habilitation à diriger des recherches (HDR) from University Toulouse le Mirail - Toulouse II in 2012 and a PhD from University of Rennes 1 in 1997 with a thesis on Natural Language Processing and interpretation. Professor TANGUY's research spans multiple areas within computational linguistics, with particular focus on Natural Language Processing, corpus linguistics, and distributional semantics. His work demonstrates expertise in analyzing online discourse, particularly through Wikipedia talk pages, and applying computational methods to linguistic phenomena. He has developed innovative approaches for studying sociolinguistic variation using Twitter corpora and neural word embeddings, contributing significantly to understanding how language evolves in digital environments. His recent publications reveal a strong trend toward analyzing computer-mediated communication, with numerous studies examining Wikipedia interactions, conflict detection, and discourse patterns. TANGUY has also made substantial contributions to distributional semantics research, exploring how word embeddings can model semantic relationships in specialized domains like family vocabulary. His work bridges theoretical linguistics with practical applications in information retrieval and natural language processing. As an active researcher with publications spanning from 1997 to 2024, Professor TANGUY has established himself as a prominent figure in French computational linguistics. His collaborative work, particularly with researchers like Lydia-Mai Ho-Dac, Cécile Fabre, and Nabil Hathout, demonstrates a strong network within the academic community. His research has been published in reputable journals and presented at major conferences in computational linguistics and corpus linguistics.
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Matteo Poggiمشاهده پروفایل
استادیار
- Stereo matching
- Multi-View Stereo
- Optical flow
- +۷ مورد دیگر
Matteo Poggi is a Tenure-Track Assistant Professor at the University of Bologna, where he teaches courses on Logic Circuits and Computer Architectures. His academic career is deeply rooted in computer vision and 3D perception, with a focus on stereo vision, depth estimation, and SLAM systems. Dr. Poggi earned his MSc and PhD degrees from the University of Bologna in 2014 and 2018 respectively, working on stereo vision under the supervision of Prof. Stefano Mattoccia. His educational background has provided a strong foundation for his current research endeavors in computer vision and 3D scene understanding. Dr. Poggi's research primarily focuses on stereo matching, multi-view stereo, optical flow, single-image depth estimation, online adaptation, federated learning, and neural SLAM. His work bridges the gap between theoretical computer vision and practical applications, with particular emphasis on robustness in real-world scenarios. His recent publications demonstrate a growing interest in 3D Gaussian splatting, neural radiance fields, and environmental monitoring applications like river plastic detection. His scholarly output shows a consistent trajectory of high-impact publications at top computer vision conferences including CVPR, ICCV, and ECCV. The trend in his work reveals an evolution from traditional stereo vision techniques toward more advanced neural representations and applications in environmental monitoring. His research has significant implications for autonomous systems, robotics, and environmental science. Area Chair for CVPR 2026 Associate Editor for IJCV Outstanding Reviewer at CVPR 2025 Dr. Poggi actively collaborates with researchers across the globe, as evidenced by his extensive publication record. His GitHub profile shows active engagement with the research community through open-source implementations of his work. While specific grant information isn't provided in the available texts, his numerous publications in top venues suggest successful funding for his research endeavors. His work appears to be conducted within a vibrant research group at the University of Bologna, with connections to other researchers in the computer vision community as seen through his GitHub followers and collaborators. His research has practical applications in autonomous driving, robotics, and environmental monitoring.
Niranjan Balasubramanian is an Assistant Professor in the Department of Computer Science at Stony Brook University with additional affiliations in the Department of Biomedical Informatics and the Center of Excellence in Wireless & Information Technology (CEWIT). His research focuses on Natural Language Processing and Information Retrieval systems that extract, understand, and reason over textual information. Education: PhD, University of Massachusetts Amherst (Center for Intelligent Information Retrieval) MS, Computer Science, University at Buffalo (2003) His research spans question answering for elementary education, event schema generation from news, machine learning for information retrieval, energy-efficient mobile search, and automatic Wikipedia content generation. Recent work explores causal reasoning in event extraction, multimodal claim verification, authorship fairness, and secure coding with large language models. Analysis of his 15 most recent publications (2024-2025) reveals intensive focus on advancing NLP through causal/temporal reasoning, multimodal verification, and LLM optimization. Key trends include psychological modeling of human language, energy-efficient architectures, and addressing misattribution in authorship analysis. Scientific Awards: No awards mentioned in provided text Advising and grants information was not provided in the source materials. His postdoctoral background at the University of Washington's Turing Center and industry experience at Syracuse University's Center for Natural Language Processing inform his applied research approach. Labs and Teams: Affiliated with Stony Brook's Center of Excellence in Wireless & Information Technology (CEWIT), contributing to interdisciplinary AI initiatives while maintaining primary focus in the Computer Science department.
Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.
Carrie Demmans Eppمشاهده پروفایل
استادیار
Carrie Demmans Epp is an Assistant Professor in the Department of Computing Science at the University of Alberta, leading the EdTeKLA research group. Her career spans postdoctoral work at the University of Pittsburgh (Learning Research & Development Center and University Center for Teaching and Learning, 2015-2017), assistant director roles at the University of Toronto's TAGlab (2013-2015), and visiting researcher positions at Kwansei Gakuin University and the University of Birmingham (2014). Education: PhD in Computer Science (2016, University of Toronto), MSc in Computer Science (2010, University of Saskatchewan), BSc in Computer Science and Russian (2007, University of Saskatchewan) Research Focus: Educational Technology, Human-Computer Interaction, Artificial Intelligence, and Language Learning (CALL/MALL), with emphasis on learner modeling, open learner models, learning dashboards, and mobile-assisted language learning (MALL) Her research explores adaptive systems like VocabNomad for language learners, PsychOut for nursing education, and PeppeR for collaborative learning environments. She has contributed to understanding student behaviors in MOOCs, discourse health in online courses, and uncertainty visualization in educational data. Scientific Awards include the Walter C. Sumner Memorial Fellowship (2013-2014), Ontario Graduate Scholarship (2012, 2014), W. Garfield Weston Fellowship (2013), and Google Canada Anita Borg Scholarship (2008). 2019: Why and How Think-Alouds with Older Adults Fail (usability testing, aging populations) 2018: PsychOut! - A Mobile App (medical education tools) 2017: Talk with Me (discourse analysis in online courses) Teaching & Projects : She developed iHelp Discussions for linguistic analysis, Recollect for lecture capture, and ProTutor for Russian pronunciation support. Her work bridges formal and informal learning environments, addressing barriers in migrant education (2017) and synchronized swimming coaching (non-academic engagement).
Daniel Cores Costa is an Associate Professor of Computer Science and Artificial Intelligence at the University of Santiago de Compostela (USC) and a researcher at the Research Center on Intelligent Technologies (CiTIUS). He completed his PhD in Research in Technologies in November 2022 with a dissertation on 'Spatio-temporal convolutional neural networks for video object detection' under the supervision of Victor Manuel Brea Sanchez. Dr. Cores Costa's research focuses on computer vision with specialized expertise in several interconnected areas: Video object detection leveraging spatio-temporal information Small object detection challenges Advancing few-shot learning techniques for object detection with limited training data Developing open-vocabulary and category-free detection approaches Practical applications in medical imaging (particularly COVID-19 diagnosis) and UAV technology His recent publications (2023-2025) demonstrate a strong research trajectory with multiple first-author papers in top venues including ICCV, BMVC, and CAIP, as well as high-impact journals. Dr. Cores Costa's work consistently addresses fundamental challenges in object detection while developing practical solutions for real-world applications across various domains. Key contributions include: Development of the Downsampling GAN for small object data augmentation Innovative approaches to few-shot object detection through pseudo-label mining (xPAND) Pioneering work on category-free detection using open-vocabulary models Creation of TVBench, a new temporal benchmark for video-language models Advancements in spatiotemporal tubelet feature aggregation for small object detection Dr. Cores Costa actively collaborates with researchers both within USC and internationally, as evidenced by his diverse co-authorship patterns. His research bridges theoretical computer vision with practical implementations, making significant contributions to both academic knowledge and real-world applications in healthcare, autonomous systems, and visual recognition technologies.
Maria Blanco-Hermidaمشاهده پروفایل
مدرس ارشد
Maria Blanco-Hermida is a Senior Lecturer in Spanish in the School of Humanities at the University of Westminster. She teaches Spanish Language in Polylang, the University's Open Language Programme, and the BA Spanish programme. Additionally, she works as a language learning strategies coach and language teacher trainer at her own coaching business, Maria Blanco Coaching. Dr. Blanco-Hermida has held various academic roles over her career, including Open Language Spanish Coordinator, DELE Exams Officer, Spanish Language and Culture consultant, HE external examiner, GCSE examiner, and Spanish Development Editor. She has worked extensively in designing, delivering, and assessing Spanish as a Foreign Language courses for more than twenty-five years. Her educational background includes a BA (Hons) in Education from the Universidad Pontificia de Salamanca (Spain) and an MA in Modern Languages in Education from the University College of London Institute of Education. She is also a Neurolanguage Coach® certified by Efficient Language Coaching and accredited by the International Coaching Federation, as well as a certified EUROLTA Teacher Trainer, International House Spanish Language Teacher and Online Tutor, DELE Examiner, and Fellow of the Higher Education Academy. Dr. Blanco-Hermida's research focuses on exploring frameworks for effective language learning strategy training and coaching, as well as learner perceptions of strategy use. She has designed, implemented, and researched strategy training activities aimed at promoting effective and enjoyable autonomous language learning. Her recent work shows a strong emphasis on practical applications of language learning strategies, with numerous workshops and projects focused on vocabulary acquisition, speaking skills development, and teacher training. Her publications and presentations consistently address how to empower students to become independent and effective language learners. Dr. Blanco-Hermida has authored two significant books: 'How to Learn Spanish: A guide to powerful principles and strategies for successful learning and self-empowerment' (2021) and 'How to Teach Language Learning Strategies: 4 simple steps for successful independent language learning' (2021), which have been well-received in the language education community. She has been actively involved in staff development and teacher training, conducting workshops for institutions including the University of Westminster, Morley College, and the Instituto Cervantes in London and Berlin. Her work bridges theoretical research with practical classroom applications, making her a respected figure in language education.