جستجوی استاد راهنمای دانشگاهی

John Banasمشاهده پروفایل

استاد

• Persuasion
• Misinformation
• Conspiracy Theories
• +۵ مورد دیگر

Dr. John Banas is a Professor in the Department of Communication at the University of Oklahoma's Dodge Family College of Arts and Sciences. He holds a Ph.D. from the University of Texas (2005) and directs the university's introductory communication course. His Spring 2024 courses include The Art & Science of US Humor and Conspiracy Theories . Banas researches persuasion strategies to counter misinformation, conspiracy theories, and maladaptive influence, with secondary work on media effects and humor. His theoretical frameworks include inoculation theory, psychological reactance, and mediated intergroup contact. His publications appear in top-tier journals like Human Communication Research , PNAS Nexus , and Communication Monographs . His recent work explores health communication (e.g., anti-vaccination conspiracies), digital literacy (e.g., detecting inauthentic social media accounts), AI-assisted fact-checking, intergroup prejudice reduction, and humor in instructional contexts. Research consistently integrates quantitative social science methods with practical applications for media literacy and public health. Teaching Awards: OU General Education Teaching Award (2013), John H. and Jane M. Patten Teaching Award (2017), Presidential Teaching Fellow (2023-2024) Research Awards: Top Paper Awards from NCA's Health Communication (2015, 2019) and Social Cognition divisions (2022, 2023), ICA's Computers and Technology Division (2022) He currently teaches courses on persuasion, interpersonal relationships, humor, and conspiracy theories, with no mentioned research labs or grants.

Patrick Carterمشاهده پروفایل

دانشیار

• Firearm Injury Prevention
• Violence Prevention
• Substance Use
• +۳ مورد دیگر

Patrick Carter, MD is an Associate Professor in the Department of Health Behavior and Health Equity at the University of Michigan School of Public Health and the Department of Emergency Medicine at the School of Medicine. He directs the CDC-funded University of Michigan Injury Prevention Center and co-leads the NICHD-funded Firearm Safety among Children and Teens (FACTS) Consortium, focusing on evidence-based strategies to reduce firearm violence through healthcare systems. Dr. Carter's research centers on emergency department-based interventions for firearm injury prevention, particularly targeting high-risk urban youth populations. His work examines youth violence, substance use, and the application of mobile health technologies to capture real-time behavioral data. Key interests include contextual factors driving adolescent firearm behaviors, racial disparities in violence exposure, and community-level interventions addressing social determinants of health. His recent publications reveal a cohesive focus on firearm safety practices, violence epidemiology, and predictive modeling for injury risk. Scholarship spans household firearm storage, community violence mediation, and the intersection of public health crises like the opioid epidemic and COVID-19 with firearm-related behaviors. This work consistently emphasizes vulnerable populations and translational approaches from research to clinical practice. Dr. Carter maintains significant research funding from NIDA, NIAAA, CDCP, and NICHD for violence prevention initiatives. He serves as Chair of the ACEP Trauma and Injury Prevention Section, Assistant Editor for the Annals of Emergency Medicine, and contributes to national firearm research agendas through the American College of Emergency Physicians technical advisory group. As Director of the Injury Prevention Center, he oversees a multidisciplinary team implementing community-based violence prevention programs. His leadership in the FACTS Consortium drives collaborative research to develop evidence-based firearm safety protocols for children and teens across diverse settings.

Lea Frermannمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Machine Learning
• Computational Cognitive Modeling
• +۵ مورد دیگر

Lea Frermann is a Senior Lecturer and DECRA 2023 Fellow in the School of Computing and Information Systems at the University of Melbourne. Her research focuses on understanding how humans learn about and represent complex and evolving information in large-scale and noisy environments, with applications to developing fairer and more robust automatic systems. She combines methods from natural language processing, machine learning, and computational cognitive modeling in her work. Her primary research interests include narrative understanding, media framing, word meaning change over centuries, category and feature learning in children, and fairness in natural language processing. She has developed scalable models of category and feature learning from noisy language data and models of historical word meaning change. Her current research focuses on improving automatic understanding of narratives in both fiction and reality, analyzing framing and narrative strategies in biased news stories. Dr. Frermann's recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of NLP, cognitive science, and social science, with particular emphasis on media framing analysis, bias detection, and narrative understanding. Her work often involves collaborations across multiple institutions and disciplines. DECRA Fellow 2023 Outstanding Paper Award at ACL 2024 for 'Media Framing: A Typology and Survey of Computational Approaches Across Disciplines' Best Paper Award at ALTA 2023 She currently supervises multiple PhD students including Chaoyi Xiang, Damian Curran, Matteo Guida, Bryan Chen, Yilin Geng, Gisela Vallejo, Uri Berger, Katie Warburton, and John Xu. Her former students include Sheilla Njoto, Chunhua Liu, Shima Khanezar, and Kemal Kurniawan. She has secured significant research funding, including the DECRA fellowship, supporting her work on narrative understanding and media framing. Dr. Frermann leads a research group focused on narrative understanding and computational social science, with projects spanning media framing analysis, bias detection in language models, narrative structure in fiction, and cross-cultural communication patterns.

Paul Resnickمشاهده پروفایل

استاد

• Computational Social Science
• Recommender Systems
• Human-Computer Interaction
• +۵ مورد دیگر

Paul Resnick is the Michael D. Cohen Collegiate Professor of Information and Associate Dean for Research and Faculty Affairs at the University of Michigan School of Information. A pioneer in recommender systems and computational social science, he co-developed the GroupLens system which earned the 2010 ACM Software Systems Award. His research spans social computing, online communities, and human-computer interaction with a focus on addressing societal challenges like misinformation and political polarization. Resnick's research interests include computational social science, recommender systems, human-computer interaction, and data visualization. His work focuses on designing socio-technical systems that enhance diversity in information consumption, improve community health, and combat misinformation. He has developed influential metrics like the Iffy Quotient for assessing platform health and the Community Notes Monitor for tracking X's community moderation system. His research on recommender systems, online communities, and social computing has been funded by NSF, Google, and other organizations. His recent scholarly output demonstrates a strong focus on misinformation detection, content moderation, and recommender system design. The research shows increasing emphasis on practical interventions to reduce political polarization and improve digital wellbeing. His work bridges technical innovation with social science insights, particularly in how algorithmic systems affect information ecosystems and user behavior in online communities. 2010 ACM Software Systems Award (for GroupLens system) As an educator, Resnick has mentored numerous PhD students including Sean Munson, Xiaodan Daniel Zhou, and Sam Carton. His work on the book 'Building Successful Online Communities: Evidence-Based Social Design' (2012, MIT Press) with Robert Kraut has become a foundational text in the field. His research projects like BALANCE, RumorLens, Commit To Steps, and MTogether demonstrate his commitment to designing online systems that promote healthy information ecosystems and social interactions.

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +۳ مورد دیگر

Hayden Dean Gemeinhardt is a Research Assistant at the Department of Computer Science (DIKU), University of Copenhagen, specializing in Machine Learning. He is affiliated with the SCIENCE AI Centre and works within a research section that focuses on statistical models, natural language processing, and multi-modal language learning applications such as automatic fact-checking and machine translation. His research spans core and emerging topics in machine learning, including explainable AI, transfer learning, and image captioning. The department emphasizes both theoretical and applied work, with collaborations across academia and industry. Fields of Interest: Machine Learning, Natural Language Processing, Artificial Intelligence, Statistical Models, Multi-modal Machine Learning, Computational Linguistics

• Interactive Communication
• Photography and Digital Image Processing
• Audiovisual Media
• +۳ مورد دیگر

Pere Freixa Font is an Associate Professor in the Communication Department at University of Barcelona. He holds academic credentials including a BA in Fine Arts, Postgraduate Degree in Interactive and Multimedia Systems, MS in Art and Image Technology, and PhD in Fine Arts. His research focuses on interactive communication, digital photography, and audiovisual media. Education: BA in Fine Arts - University of Barcelona Postgraduate in Interactive/Multimedia Systems - UPC MS in Art & Image Technology - University of Barcelona PhD in Fine Arts - University of Barcelona Research projects include INDOCS (Interactive storytelling in documentaries) and MIRONINS (Museum transmedia experiences). Recent publications address misinformation verification, audience engagement, political photography analysis, and temporal dynamics in digital journalism. He employs methodologies combining structured journalism with interactive media frameworks. Professional affiliations include DigiDoc Group and involvement in EU-funded research initiatives. His work bridges digital communication with educational technologies, evidenced by his studies on Wikipedia's academic applications.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Axel Prütz Kanne is an Instructor at the Department of Computer Science, University of Copenhagen, affiliated with the Natural Language Processing (NLP) section. His work aligns with the section’s focus on methods for automatic text processing, understanding, and generation using statistical models and machine learning, with applications in fact-checking, machine translation, and multimodal language processing. Department: Computer Science Location: Universitetsparken 1, DK-2100 Copenhagen Ø The NLP section explores core tasks like cross-lingual processing, parsing, and transfer learning, as well as multi-modal machine learning intersecting with computer vision. Teaching activities include courses in Computer Science, Machine Learning, and the Language Technologies track for MSc programs.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Ernst á Heygum Kass is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. Core research areas include natural language understanding, misinformation detection, explainable AI, and multi-modal machine learning intersecting with computer vision. Applications of his affiliated section's work span automatic fact-checking, machine translation, question answering, and visually-grounded language learning.

• Natural Language Processing
• Machine Learning
• Statistical Models
• +۳ مورد دیگر

Casper Rømer Halling Larsen is an Lecturer at the Department of Computer Science (DIKU), University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for automatic text processing, understanding, and generation using statistical models and machine learning. Applications of the section's research include automatic fact-checking, machine translation, question answering, and visually-grounded language learning. Fields of Interest : Natural Language Processing, Machine Learning, Statistical Models, Language Technologies, Text Processing, Computational Linguistics Casper Rømer Halling Larsen contributes to teaching within the university's Computer Science and Language Technologies programs, though specific details about his courses or publications are not provided in the available information.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Philip Rajani Lassen is a Lecturer at the Department of Computer Science , University of Copenhagen, affiliated with the Natural Language Processing (NLP) section . His work focuses on methods for automatic text processing, understanding, and generation using statistical models and machine learning. University: University of Copenhagen Department: Department of Computer Science Email: prl@di.ku.dk Research Interests: Natural Language Processing Machine Learning Computational Linguistics Artificial Intelligence Text Processing Multimodal Learning The NLP section at DIKU explores core and emerging topics in natural language processing, including meaning representations, cross-lingual NLP, parsing, and applications like automatic fact-checking and machine translation. They are part of the SCIENCE AI Centre at the University of Copenhagen.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Xiangyu Lu is a Lecturer in the Department of Computer Science at the University of Copenhagen, affiliated with the Natural Language Processing (NLP) section. Their research focuses on statistical models and machine learning methods for text processing, with applications in automatic fact-checking, machine translation, and multimodal language processing. University: University of Copenhagen Department: Department of Computer Science Email: xilu@di.ku.dk

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Peter Trip Malmroes is an Instructor at the Department of Computer Science, University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. His research interests align with NLP section priorities, including applications in automatic fact-checking, machine translation, question answering, and multi-modal language processing. The section also contributes to the SCIENCE AI Centre at the university. Contact details: Email: pema@di.ku.dk Address: Universitetsparken 1, 2100 Copenhagen Ø

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

Daniel Nicholas Mølhave is an Instructor at the Department of Computer Science (DIKU) , University of Copenhagen, located at Universitetsparken 1, 2100 København Ø. His work is associated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. University affiliation: University of Copenhagen Department: Department of Computer Science (DIKU) Email: damo@di.ku.dk The NLP section at DIKU engages in cutting-edge research spanning core areas such as: Natural language understanding Multi-modal machine learning Explainable AI Visually-grounded language learning Cross-lingual NLP Language technologies Research applications include machine translation, misinformation detection, image captioning, and multilingual multimodal representation learning. The section contributes to the SCIENCE AI centre and offers courses in the Bachelor's and Master's programs in Computer Science, Machine Learning, and Data Science.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Mathias Theodor Jul Overby serves as a Lecturer at the Department of Computer Science (DIKU) and the Department of Mathematical Sciences at the University of Copenhagen (Faculty of Science). His academic work intersects with the Natural Language Processing (NLP) section, which focuses on statistical models, machine learning, and applications like automatic fact-checking, machine translation, and multi-modal language processing. Instructor roles in NLP and mathematical sciences Part of the SCIENCE AI centre at UCPH Teaching involvement in Computer Science, Machine Learning, and Data Science programs His research interests include natural language understanding, computational linguistics, and machine learning techniques for text analysis. The NLP section's work spans core tasks like parsing, transfer learning, and multimodal translation. Contact: maov@di.ku.dk , mtjo@math.ku.dk

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Benjamin Daniel Nyberg Shultz is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. His work aligns with the Natural Language Processing (NLP) section , focusing on methods for automated text processing, understanding, and generation using statistical models and machine learning. Research Focus : Core NLP tasks like parsing, cross-lingual processing, and transfer learning; applications in automatic fact-checking, machine translation, and visually grounded language learning. Teaching : Contributes to courses in Computer Science, Machine Learning, and Data Science, including advanced topics in NLP and deep learning. Collaboration : Part of the SCIENCE AI Centre, exploring intersections between NLP and computer vision in multi-modal learning.