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Keng-Chi ChangView profile

Assistant Professor

• Computational Social Science
• Political Methodology
• Digital Politics
• +3 more

Keng-Chi Chang is an Assistant Professor in the Department of Quantitative Social Science at Dartmouth College. His research employs computational tools including AI and Large Language Models to investigate how technology and media shape political processes. Specific interests include censorship circumvention, online image-sharing behavior, and algorithmic recommendation systems for short-form video content. Education: • Ph.D. Political Science (Computational Social Science), University of California San Diego • M.S. Computer Science, University of California San Diego • B.A. Economics, National Taiwan University Research Focus: Chang explores the intersection of digital technology and political behavior, with emphasis on visual media analysis, censorship dynamics, and social media algorithms. His work examines how crises like COVID-19 trigger information-seeking behaviors and how platform features like live-stream chats influence political perceptions. Publications: Recent works demonstrate a strong focus on computational methods applied to political communication, including neural network interpretability, meme propagation patterns, and image-based credibility assessment. Publications span PNAS, Journal of Communication, and premier CS conferences. Funding & Awards: • NSF/APSA Dissertation Grant • Rapoport Family Foundation Research Grant

• Video Quality Assessment
• Telecommunications
• Quality of Service (QoS)
• +5 more

Professor Tadeus Uhl is a faculty member at the University of Applied Sciences Flensburg in the School of Information and Communication. His research focuses on telecommunications, video quality assessment, and Quality of Service (QoS) technologies. He maintains an active research profile with numerous publications spanning from 2017 to 2024. Professor Uhl's research interests center around video quality evaluation, telecommunications technologies, and network performance analysis. His work examines cutting-edge topics including 8-bit and 10-bit MP4 coding, machine learning applications for video quality assessment, and user experience with different video resolutions (1K, 2K, and 4K). He has also conducted significant research on 5G technology, VoIP systems, and Quality of Service in IoT environments. His recent publications show a clear trend toward video quality assessment using modern machine learning techniques, with a focus on real-world streaming services like Netflix. His work bridges theoretical network performance concepts with practical applications in video streaming, cellular networks, and offshore communication systems. Professor Uhl has made substantial contributions to understanding Quality of Service parameters across various communication technologies, from traditional VoIP systems to modern IoT networks and offshore wind farm communications infrastructure. His research has been published in notable venues including IEEE conferences, the Journal of Telecommunications and Information Technology, and various scientific journals focused on telecommunications and multimedia technology.