Stefan Kluge, Chair of Quantitative Marketing & Consumer Analytics: Neural Networks (May 2022)

Stefan Kluge has been a PhD student at the Chair of Quantitative Marketing at the University of Mannheim since 2019 and works as a freelance data scientist on various projects. Previously, he studied computer science for his diploma and master's degree.

What is your current research topic?

I'm involved in two major projects right now: With José Parra-Moyano of Copenhagen Business School we are looking at the design of Parachains auctions for Blockchain projects. Leonie Gehrmann, Florian Stahl and me are analysing 100 years of advertisements of the Economist magazine at the Chair of Quantitative Marketing in Mannheim – a very big dataset with high potential.

For those who have not yet delved deeply into the topic of Data Science: How would you explain to a child what you are working on?

Actually, I just recently told me daughter: I'm working with very smart people on giant mountains of data, in countries all over the world. And what we are trying to do, for instance, is that you will get better videos recommended on YouTube.

Everyone talks about Data Science – how would you describe the importance of the topic for yourself in three words?

Creativity, versatility and lots of command line.

What points of contact with Data Science does your work have? Which methods do you already use, and which would be interesting for you in the future?

I'm fully concentrated on the data itself in all research projects that I'm involved in, since my background is in computer science and I love this stuff. Python is always my first go to, the methods used greatly depend on the types of data and the questions asked. In computer vision and natural language processing we are mostly using neural networks. For early stage data exploration it can make sense to use faster and simpler methods like dictionary based Topic Modelings or image analysis methods from our colleagues in signal processing.

It would be interesting to have access to the big generalized neural networks from Google AI or Meta AI in the future. They are creating very powerful tools that could help many smaller research projects.

How high is the value of Data Science for your work? Would your research even be possible without Data Science?

It's essential. There would be no way to handle big data and get those kinds of insights without modern Data Science for researchers like us.

What development opportunities do you see for the topic of Data Science in relation to your field?

The legal framework in Europe could be more research friendly. Data privacy laws are very strict and hard to follow for many researchers, especially if you are competing with state of the art research projects from overseas. In terms of working on data science methods and infrastructure we are on a good path.