As senior researcher, data scientist, and part of both the Data Science and Future & Trends Research Groups of the Nuremberg Institute for Market Decisions, René Schallner researches methods in the areas of artificial intelligence, machine learning, computer vision, speech synthesis, and natural language processing. The aim of this research is to apply it to predict, explain, and support making market decisions.
In previous projects, he worked on gaining marketing intelligence from social media pictures, together with Dr. Carolin Kaiser. He later extended the scope of this work to its application on mobile devices. In 2016 the research project “Social Media Pictures” won the innovation prize of the German BVM (Bundesverband Deutscher Markt- und Sozialforscher).
His primary focus now lies in the application and development of modern technologies and methods, such as Deep Learning and Reinforcement Learning to better understand, predict, and augment human decisions.
René Schallner applies his electronics and software engineering skills as manager of the Technology Lab, where he develops technology and infrastructure to enable state-of-the-art research projects across research groups - such as the electronic synchronization of sensor and experiment software data streams or a backend platform for extending the capabilities of third-party online survey software.
Within 15 years of international work as a self-employed software engineer, he transitioned from the telecom industry via the medical-engineering sector into the field of market research. There he spent the first three years working on analysis and encryption of smartphone data at GfK SE, before joining the then GfK Verein (now NIM) in 2016.
René Schallner graduated from the “Higher department of electronics and technical informatics” of the Austrian "Höhere Technische Bundeslehr- und Versuchsanstalt" in Mödling near Vienna.
Harzig, P., Zecha, D., Lienhart, R., Kaiser, C., & Schallner, R. (2019). Image captioning with clause-focused metrics in a multi-modal setting for marketing. 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA, 419-424.
Harzig, P., Brehm, S., Lienhart, R., Kaiser, C., and Schallner, R. (2018). Multimodal Image Captioning for Marketing Analysis. In Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval, FL, USA.
Paolanti, M., Kaiser, C., Schallner, R., Frontoni, E., and Zingaretti, P. (2017). Visual and Textual Sentiment Analysis of Brand-Related Social Media Pictures Using Deep Convolutional Neural Networks. In Battiato, S., Gallo, G., Schettini, R., and Stanco, F., editors, Lecture Notes in Computer Science, vol 10484, Springer, 402-413.