Lyndon Nixon

Short Bio

Dr Nixon is an Assistant Professor in the School of Applied Data Science. His research interests cover the visual classification of photography to automatically extract the touristic destination image from social networks such as Instagram; the use of deep learning for predictive analytics in open domains such as predicting the next trending topic in online channels; the extraction and modelling of knowledge, e.g. about events, in graph structures and their combination with neural networks to improve computational understanding of the world.

Projects

He also leads research projects at the Research Centre for New Media Technology in his role as CTO of MODUL Technology GmbH, the research spin-off of MODUL University. Currently he is project co-ordinator in AI-CENTIVE, which is incentivizing sustainable mobility behaviour in Austria, as well as contributing to SDG-HUB, building a comprehensive repository of SDG related communication in Austria, and TRANSMIXR, using Web and social media intelligence to identify topics and suggest digital content for immersive experiences (XR). He was project coordinator of the EU Horizon 2020 project ReTV (www.retv-project.eu), which developed tools to help media organisations to optimally select and repurpose their media assets for digital marketing. He led research in predicting future trending topics among online audiences and recommending relevant future events to target in online marketing. He also coordinated the FFG project EPOCH (extracting and predicting events from online communication; www.epoch-project.eu) and participated in the BMVIT project EcoMove (prediction of urban mobility bottlenecks; www.ecomove.at) and the FFG project GENTIO (prediction of future communication success of online publications; www.gentio.eu).

Teaching

He also teaches (BBA/BSc, MSc/MBA) and supervises theses. His courses cover: New Media Technologies and E-Business, Social Media Marketing, Marketing Intelligence, Foundations of Computer Programming, Media Asset Management, Search Engine Marketing and Optimisation (SEM/SEO) as well as Social Media Intelligence.

Research

His research domain is the analysis and description of online media and the use of this description in the computational understanding and use of media assets, supported by knowledge models in graph-based structures ("knowledge graphs") to provide additional explicit understanding of the domain. He works in the domains of e-tourism ("Visual Destination Image" - extracting tourist destination image from online photography), news and journalism (video verification based on visual content and knowledge models), and media organisations and broadcasters ("TV Intelligence" - extracting additional information from and around audiovisual streams to enhance their re-use).