جست‌وجوی استاد و پژوهشگر

Yllias Chaliمشاهده پروفایل

استاد

• Natural Language Processing
• Text Summarization
• Text Generation
• +۴ مورد دیگر

Yllias Chali is a Professor at the Department of Mathematics and Computer Science, University of Lethbridge, Canada. His research focuses on Natural Language Processing , Artificial Intelligence , and Human Language Technologies , with a focus on text summarization, question answering, and knowledge representation. He has supervised numerous graduate students and secured funding from NSERC for projects in these areas. Teaching : Courses include Fundamentals of Programming I , Artificial Intelligence , and Statistical Methods and Machine Learning . Research Trends : Recent publications emphasize abstractive text summarization using BART, graph attention networks , multi-hop question generation , and faithfulness in NLP models. His work integrates machine learning , syntactic analysis , and semantic modeling . Advising : Collaborates with graduate students on NLP projects, including Narjes Delpisheh and Elozino Egonmwan. Funded by NSERC. Labs & Teams : Leads NLP research initiatives at the University of Lethbridge, contributing to computational linguistics and machine learning advancements.

• Natural Language Processing
• Artificial Intelligence
• Information Extraction
• +۴ مورد دیگر

Assoc. Prof. Pavel Pecina, Ph.D., is a faculty member at the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. His primary academic role as an Associate Professor focuses on Natural Language Processing , Artificial Intelligence , and related areas. Research Interests: Information extraction, information retrieval, machine translation, multimodal data interpretation, optical music recognition Teaching: Courses since 2012-2022 including Natural Language Processing , Information Retrieval , and Statistical Methods in NLP Key Research Projects include GI-Insight (AI for healthcare), RES-Q+ (global stroke care registry), and MEMORISE (heritage digitization). His recent publications (2023-2025) demonstrate expertise across clinical NLP , historical document analysis , and music technology domains. Advising: Supervised 10+ graduate students in areas ranging from semantic search to optical music recognition. Collaborates with international institutions on multilingual translation and medical informatics challenges through initiatives like IWSLT and CLEF eHealth.

Johanna Geißمشاهده پروفایل

پژوهشگر

• Event Detection
• Information Extraction
• Information Retrieval
• +۵ مورد دیگر

Johanna Geiß is a postdoctoral researcher at the Institute of Computer Science , Heidelberg University, specializing in Natural Language Processing , Information Extraction , and Event Detection . She contributes to projects like SCIDATOS (sepsis diagnosis) and EventAE (event-based Linked Data exploration). Education: PhD from University of Cambridge (2011), Magister Artium in Computational Linguistics (Heidelberg, 2006) Her research integrates Geoparsing , Social Network Analysis , and Graph Theory for tasks such as toponym disambiguation and entity resolution. Recent work includes frameworks like HeidelPlace and tools for semantic word clouds. Key awards include the 2010 Lundgren Research Award and multiple grants from the EPSRC and Cambridge European Trust. She has taught courses on Information Networks and Data Mining at Heidelberg University. As a postdoc , she advises on scientific computing applications in healthcare and contributes to open-source tools like NECKAr and EventAE .

Carolina Scartonمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Machine Translation
• Online Misinformation Detection
• +۶ مورد دیگر

Dr. Carolina Scarton is a Senior Lecturer in Natural Language Processing at the Department of Computer Science, University of Sheffield, UK, and serves as Research Innovation Grant Support Lead. She is an active member of the Natural Language Processing research group and part of the GATE team, with significant contributions to European projects including WeVerify and SIMPATICO. Her educational background includes a PhD in Computer Science from the University of Sheffield (2017), supervised by Professor Lucia Specia and funded by the EXPERT Marie Curie ITN network, along with MSc and BSc degrees from the University of São Paulo, Brazil (awarded 2013), where she was a member of the Interinstitutional Center for Computational Linguistics (NILC). Dr. Scarton's research focuses on Natural Language Processing with particular emphasis on text adaptation, machine translation, online misinformation detection and verification, evaluation of NLP task outputs, NLP applied to healthcare and robotics, and dialog systems. Her work bridges theoretical NLP advances with practical applications in disinformation combat and accessibility. Analysis of her recent publications (2023-2025) reveals strong trends in multilingual misinformation detection, biomedical text simplification, idiomaticity representation, and leveraging large language models for zero-shot tasks. Her research demonstrates consistent focus on real-world applications while advancing core NLP methodologies across multiple domains. Secretary for the European Association for Machine Translation (EAMT) since 2018 (re-elected 2020) Secretary for the International Association for Machine Translation (IAMT) since 2019 Dr. Scarton currently leads as Principal Investigator for the ExU project (€399,926, 2023-2025) and VIGILANT project (Horizon Europe, £476,955, 2022-2025), while serving as Co-PI for vera.ai (Horizon Europe, £776,703, 2022-2025) and Co-I for multiple UKRI and EPSRC grants including XAIvsDisinfo (£288,337) and Modeling Idiomaticity (£446,163). Her research group actively collaborates with the GATE team on NLP tool development and applications.

• Natural Language Processing
• Deep Learning
• Text Summarization
• +۳ مورد دیگر

Valentin Nyzam is a Researcher at the University of Tours , affiliated with the Polytechnic School and Department of Networks and Telecommunications (RT) . He works at the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT, UR 6300) and is associated with the University Institute of Technology of Blois (IUT Blois) . Research Focus: Specializes in Natural Language Processing with emphasis on automatic summarization and comparative summarization Develops modular open-source tools (MOTS) for multi-document summarization Active in deep learning applications for text analysis and word embeddings Conducts cross-lingual research and short text language identification Focus on French language processing and implementation of NLP tools Contact: valentin.nyzam@univ-tours.fr

Catherine Blakeمشاهده پروفایل

استاد

• Biomedical Informatics
• Natural Language Processing
• Evidence-Based Discovery
• +۹ مورد دیگر

Catherine Blake is a Professor at the School of Information Sciences and a Health Innovation Professor at the Carle Illinois College of Medicine , University of Illinois Urbana-Champaign. She holds courtesy appointments in the Department of Computer Science and affiliations with the National Center for Supercomputing Applications (NCSA) , Personalized Nutrition Initiative , and Center for Health Informatics . Education : PhD in Information and Computer Science from University of California, Irvine Master's and Bachelor's in Computer Science from University of Wollongong Research Interests : Dr. Blake specializes in biomedical informatics , natural language processing , and evidence-based discovery , focusing on synthesizing evidence from text using the Claim Framework to extract findings from empirical studies. Her work extends to text mining in humanities , socio-technical systems , and FATE principles in data science . Scientific Awards : California Breast Cancer Research Program Dissertation Award (2002-2004) Computing Research Association MRO-W Program (2007-2008) RENCI Faculty Fellows Program (2007-2008) UNC Junior Faculty Award (2007) Teaching & Advising : She teaches database design (IS455) and text mining (IS567) , and has developed the Socio-technical Data Analytics (SODA) specialization . Her advisees include doctoral students Ryan Wang, George Vazquez, Don Keefer, and Jaemin Yang, and former students Jooho Lee (University of Chicago), Henry Gabb (Intel), and Ana Lucic (UIUC Applied Research Institute). Labs & Collaborations : Dr. Blake leads the National Science Foundation's Midwest Big Data Innovation Hub (MBDH) and collaborates with UIUC Extension , CISCO , and John Deere on health data literacy and privacy analysis projects.

Anthony Riosمشاهده پروفایل

استادیار

• Biomedical informatics
• Computational social science
• Natural language processing
• +۱ مورد دیگر

Dr. Anthony Rios is an Assistant Professor in the Department of Information Systems and Cyber Security at The University of Texas at San Antonio (UTSA), affiliated with the Alvarez College of Business. He holds a Ph.D. in Computer Science from the University of Kentucky (2018) and a B.S. in Computer Science from Georgetown College (2011). His research focuses on biomedical informatics, computational social science, natural language processing (NLP), and machine learning, with applications in healthcare and social media analysis. Dr. Rios has published extensively in top venues such as EMNLP, NAACL, AMIA, and JAMIA, and has received awards including a distinguished poster finalist award at AMIA 2014 and a best paper finalist at ICHI 2015. His work includes developing robust NLP systems for electronic medical records (EMR) coding, combating bias in AI systems (e.g., dialect biases in privacy policies), and enhancing cybersecurity through SQL-based threat detection frameworks. He leads the UTSA-NLP team, contributing to projects like ArchEHR-QA and Chemotimelines. Current research explores large language model (LLM) validation, ethical AI, and scalable evaluation of data visualization systems. Education: Ph.D. in Computer Science, University of Kentucky, 2018 B.S. in Computer Science, Georgetown College, 2011 Awards: 2014: Distinguished Poster Finalist Award, AMIA 2015: Best Technical Paper Finalist, ICHI Labs/Teams: UTSA-NLP Lab, focused on NLP applications in healthcare and cybersecurity

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +۴ مورد دیگر

Dr. Qianqian Zhang is an Associate Professor at China Agricultural University's College of Information and Electrical Engineering, specializing in computer vision, medical imaging, and IoT security. With over 140 publications spanning from 2007 to projected 2026, her research demonstrates consistent productivity across multiple domains of computer science and engineering. Her research interests focus on practical applications of machine learning in medical diagnostics, autonomous systems, and information security. She has developed innovative approaches in object detection (YOLO-FCE), medical image analysis for renal carcinoma diagnosis, and lightweight neural networks for image steganalysis. Her work bridges theoretical computer science with real-world healthcare and security applications. Analysis of her recent publications (2023-2026) reveals a strong trend toward interdisciplinary research combining computer vision with medical diagnostics, particularly in renal tumor analysis using CT imaging. She has also maintained significant contributions to IoT security through federated learning approaches and developed novel methods for motion sickness analysis using EEG and virtual reality. Featured in IEEE Access with 15 publications (2020) Published in Pattern Recognition, a premier computer vision journal Contributions to BMC Medical Imaging for clinical applications Multiple publications in IEEE Internet of Things Journal Dr. Zhang actively supervises graduate students including Peng Liu, Tiancheng Zhao, and Jiajia Liao, with whom she has co-authored numerous papers on vision-language models and multimodal learning. Her research group appears to focus on practical implementations of deep learning for real-world problems in healthcare, transportation, and security.

• Natural Language Processing
• Information Retrieval
• Computational Linguistics
• +۳ مورد دیگر

Dr Emma Barker is a Research Fellow and Data Science & AI Community Manager at the University of Sheffield's School of Computer Science. Her work focuses on Natural Language Processing (NLP), computational linguistics, and data science applications. She has contributed to projects involving term extraction from web texts, analysis of online news reader comments, and development of spoken language corpora. Her research bridges theoretical NLP advancements with practical applications in information retrieval, social media analysis, and cross-lingual systems. Her publications emphasize techniques like graph-based clustering for news comment analysis, automated label generation for topic categorization, and corpus development for dialogue systems. She collaborates on interdisciplinary projects such as the SENSEI corpus and the Sheffield Numbers Spoken Language Corpus (SNuC). Barker also plays an active role in community-building through her Data Science & AI Community Manager role, fostering academic-industry partnerships and advancing AI adoption in real-world contexts. Key research themes include argumentation mining in multi-party conversations, cross-domain document classification, and bootstrapping methods for multilingual term extraction. Her work spans technical innovations in NLP tools integration, evaluation metrics for summarization systems, and graph-theoretic approaches to content analysis. Despite no listed awards, her contributions to open-source corpora and collaborative frameworks reflect her commitment to advancing computational linguistics and data science practices.

Kwanghee Wonمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Convolutional Neural Networks
• Deep Reinforcement Learning
• +۴ مورد دیگر

Kwanghee Won is an Associate Professor at the McComish Department of Electrical Engineering and Computer Science, South Dakota State University. His research focuses on computer vision, deep learning, and their applications in autonomous systems, precision agriculture, and structural health monitoring. He holds a Ph.D. from Kyungpook National University, Korea. Education: B.S., M.S., and Ph.D. in Computer Engineering from Kyungpook National University, Daegu, Korea Research Interests: Deep convolutional neural networks for anomaly detection and classification Autonomous vehicle systems and augmented reality applications Computer vision in smart agriculture and infrastructure assessment Model compression techniques for efficient neural networks Professional Contributions: Upsilon Pi Epsilon honor society faculty advisor (2021–present) ACM member and SIG Applied Computing participant Labs and Teams: Director of the Intelligent Vision Systems Laboratory , focusing on advanced vision-based solutions for real-world challenges.

Tamara Polajnarمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Computational Linguistics
• Distributional Semantics
• +۲ مورد دیگر

Dr. Tamara Polajnar is a Visiting Researcher at the Department of Computer Science and Technology, University of Cambridge. Her research focuses on computational linguistics and natural language processing, with particular emphasis on distributional semantics and semantic compositionality. Her research interests span multiple areas including computational semantics, distributional models, and semantic composition. She develops tensor-based methods for representing meaning and investigates techniques for semantic analysis of text at different levels from phrases to documents. Her work combines theoretical linguistics with practical machine learning approaches. Her publications primarily focus on compositional distributional semantics, exploring how to effectively combine word vectors to represent larger linguistic units. Recent work examines discourse-level representations and evaluation methods for semantic models. She has contributed datasets and frameworks that advance the understanding of how computational models can capture linguistic phenomena. She actively contributes to the research community through the development of specialized datasets and evaluation resources. Her work on RELPRON provides valuable resources for evaluating compositional distributional models, while her research on short scientific summaries addresses challenges in processing academic text.

Rinat Khusainovمشاهده پروفایل

استاد

• Applied Artificial Intelligence
• Health Informatics
• Machine Learning
• +۵ مورد دیگر

Rinat Khusainov is a Professor at the University of Portsmouth's Faculty of Technology, affiliated with the School of Computing and the Department of Computing. He holds roles as Associate Head (Research and Innovation), Innovation and Impact Development Manager, and PhD Supervisor. His research focuses on Applied Artificial Intelligence, particularly in health technologies, sensor systems, and addressing societal challenges through AI. He leads the University of Portsmouth's Cisco Networking Academy, contributing to undergraduate/postgraduate curriculum and professional development programs like Cisco CCNA. Khusainov has a PhD in Computer Science from University College Dublin (2004) and an MSc from the same institution (1998). His research interests span Machine Learning, Computer Vision, IoT, Cybersecurity, and healthcare analytics. He has collaborated with industry on projects involving assisted living technologies, smart sensors, and embedded systems. Key affiliations include the Centre for Healthcare Modelling and Informatics, Computational Intelligence Research Group, and Future of Law, Innovation and Technology Research Centre. Recent articles highlight innovations in AI-driven mental health solutions, edge-based violence detection systems, and steganographic methods. His work integrates interdisciplinary approaches, bridging AI with healthcare, cybersecurity, and renewable energy systems. He actively contributes to knowledge transfer initiatives, including EU-funded projects and industrial consultancy in sensor characterization and data management. Khusainov’s teaching portfolio includes computer networks, distributed systems, and machine learning. His academic leadership extends to managing research impact initiatives and fostering innovation in higher education through tech-enabled pedagogies. He remains engaged in advancing ethical AI applications and sustainable energy technologies through collaborative research.

Wajdi Aljedaaniمشاهده پروفایل

استادیار بالینی

• Accessibility
• Software Engineering
• Human-Computer Interaction
• +۲ مورد دیگر

Wajdi Aljedaani serves as a Clinical Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT), with his office located in Discovery Park, room E235D. His research and academic work center on advancing accessibility solutions and software engineering practices through interdisciplinary approaches. His primary research domains include: Accessibility for users with visual, auditory, and neurological disabilities Integration of machine learning in accessibility compliance and software development Analysis of educational barriers in online learning for disabled students Software engineering challenges in refactoring, testing, and accessibility implementation Recent publications (2024-2025) demonstrate a concentrated focus on identifying accessibility barriers in social media, e-government platforms, and mobile applications through systematic literature reviews and empirical studies. His work uniquely bridges clinical perspectives with technical solutions, particularly examining seizure triggers in apps and accessibility sprints in software engineering education. A significant portion of his research leverages large language models to enhance accessibility features and address developer challenges with WCAG guidelines. His advising and research initiatives emphasize practical applications in educational settings, particularly through student software engineering projects that incorporate accessibility requirements. While specific grant information isn't detailed in available sources, his publication pattern indicates active engagement with real-world accessibility testing and implementation challenges across multiple platforms.

• Artificial Intelligence
• Computational Linguistics
• Natural Language Processing
• +۷ مورد دیگر

Sandra Carberry is a Professor in the Department of Computer and Information Sciences at the University of Delaware with a joint appointment in Linguistics. Her research integrates artificial intelligence, natural language processing, and user modeling to develop intelligent systems for digital libraries, medical decision support, and educational applications. She leads the NLP/AI laboratory and teaches core AI and data mining courses. Her primary research focuses include: Intelligent Interfaces : Developing systems that recognize user goals and generate tailored responses through advanced user modeling Digital Libraries : Pioneering methods to extract meaning from information graphics using perceptual effort analysis and Bayesian reasoning Medical Informatics : Creating TraumaCASE for generating clinical training scenarios and improving trauma care decision support Educational Systems : Building adaptive medical tutoring systems that dynamically adjust case complexity Dialogue Systems : Advancing transformation-based learning for dialogue act recognition and cooperative response generation Her publication trend (2002-2007) reveals a strategic shift toward information graphics understanding, where she established that 61% of graphics convey unique information not present in text. This work combines cognitive psychology with machine learning to recognize graphic intentions through perceptual effort metrics and communicative signals, significantly advancing digital library capabilities. Key recognitions include: James Chen 2006 Award for Best Paper (UMUAI Journal) Best Paper Award at Diagrams Conference (2006) Springer Best Paper Award at User Modeling Conference (2007) Funded by NSF grant IIS-0534948, her digital libraries project demonstrates strong grant acquisition capability. Her extensive co-authorship with junior researchers indicates active mentorship, though formal student lists aren't published. University service includes departmental committees and professional activities documented in her vita. She directs the NLP/AI laboratory, which maintains active research in natural language processing, artificial intelligence applications, and multimodal information systems, with current work extending graphic understanding to complex visual representations.

Falk Scholerمشاهده پروفایل

استاد

• Information Access and Retrieval
• Fairness and Ethics of AI Systems
• Search Engine Evaluation
• +۵ مورد دیگر

Professor Falk Scholer is a Professor of Information Access and Retrieval Technologies in the School of Computing Technologies at RMIT University, Melbourne, Australia. He leads the RMIT University Centre for Human-AI Information Environments (CHAI). His research focuses on understanding how search engines and recommender systems assist users in resolving information needs and measuring their effectiveness. His work emphasizes fairness, accountability, and human-centered computing. Research interests include interactive information access, evaluation methodologies, misinformation, and the ethical implications of AI systems. His projects span topics like bias mitigation in conversational search, physiological data analysis for user behavior, and generative AI adaptation for diverse scenarios. He collaborates on industry projects, such as enhancing job search systems with SEEK, improving query performance prediction, and developing fact-checking tools. His teaching interests include Data Science, Web Programming, and Research Methods. He actively supervises PhD and Master’s students in areas like fairness-aware systems and complex answer retrieval. Professor Scholer serves on committees including RMIT’s Academic Board and Human Research Ethics Committee. His research has been recognized through high-impact publications and partnerships addressing real-world challenges in search and information systems.