Lyndon Nixon

Asst.-Prof. Dr. Lyndon Nixon
Assistant Professor
School of Applied Data Science
T: +43-1-3203555-533
Room Number
309
Short Bio
Dr Nixon is an Assistant Professor in the Department 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.
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. 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 coordinates the FFG project EPOCH (extracting and predicting events from online communication; www.epoch-project.eu) and participates in the BMVIT project EcoMove (prediction of urban mobility bottlenecks; www.ecomove.at) and the FFG project GENTIO (prediction of future communication success o.f online publications; www.gentio.eu). Previously he has been active in the EU projects InVID (finding and verifying fake news video on social networks; www.invid-project.eu) and LinkedTV (identifying topics of TV programming and linking them to related Web content on a second screen; www.linkedtv.eu). He also teaches (BBA/BSc, MSc/MBA), supervises theses and works on acquiring new research projects. Dr Nixon welcomes thesis and research project proposals around any of the above topics!
Research interests
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)
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)
Further Information
Visit his work blog
Courses @ MU
MBA specialisation module "Media Asset Management and Utilisation"
PhD course "Knowledge Extraction and Verification from News and Social Media"
Bachelors' Courses (BBA/BSc)
New Media and E-Business Applications
Web Information Systems
Project Management
Masters' Course (MSc in International Management)
Interactive Marketing