How do you create knowledge from data? And how are practical instructions for action derived from this knowledge? This is what the data scientist Dr. Max Zimmerman. Since the 2022 summer semester, he has been a professor of data science in the fields of data engineering, machine learning and MLOps in the department electrical engineering and computer science.
“An exciting area with great future prospects for the students,” says Zimmermann. A data scientist collects, modulates and analyzes data from various sources and derives decisions from it that are used in a productive system. “Today, large amounts of data are recorded in all sectors of the economy,” says Zimmermann. The task of the data scientist is then to read this data in a quality-assured manner, to draw concrete conclusions from it and to derive targeted instructions for action:
This not only increases the company’s profits, but will also be essential in the future with a view to sustainability.
Derive connections from complex data
Zimmermann discovered his passion for researching complex data during his studies in business informatics at the Ostfalia University of Applied Sciences in Braunschweig and his subsequent studies in data science (“Data and Knowledge Engineering”) at the University of Magdeburg. “One puzzles, one looks for connections like a data detective”, he describes his work. “I always like to delve really deep into problems.” From the beginning of his research career, he paid particular attention to the possible applications of the developed algorithms for real scenarios. His doctorate with Prof. Myra Spiliopoulou (“Understanding and Monitoring attitudes of product properties over time”) in the field of applied machine learning was already about analyzing the polarity of product reviews in social media.
Derive smart traffic light circuits from anonymous movement data
During his postdoc period as an Alain Bensoussan fellow at the Swedish Institute of Computer Science in Stockholm, he researched incremental algorithms of machine learning and text mining. “I was concerned with the question of how to use anonymized movement data from the mobile phone to derive predictions about imminent bottlenecks in rush-hour traffic at an early stage – i.e. even before the traffic jam occurs,” says Zimmermann. The application then led to smart traffic light switching so that traffic could continue to flow unhindered.
Data streams provide information about the maintenance time of machines
Back in Germany, his path as a data science consultant took him to various stations in Hamburg. It was clear to him that he wanted to continue doing research with a view to practical application. As a consultant for machine learning and data science, he developed prediction algorithms in edge computing based on audio data for improved maintenance of escalators for Deutsche Bahn, for example. “Before the escalator stops, it makes strange noises. If you record this systematically, you can maintain them proactively and thus significantly improve the service for customers,” says Zimmermann.
Research, teaching and transfer to business
In addition to project and research work, teaching was always a matter of the heart for Max Zimmermann. Even as a student, he took over the supervision of students in practice events, seminars and software projects, and over the course of his career supervised numerous bachelor’s and master’s theses. “I’ve always been very satisfied with getting students excited about data analysis and the development of data-driven applications,” says Zimmermann.
Now he is looking forward to pursuing all three of his passions – research, teaching and transfer to business – in equal measure:
I look forward to pursuing intensive teaching activities in the future and to updating my knowledge every year. At the same time, I am enthusiastic about the focus on applied research in the field of data analysis and big data. I also act as an intermediary between the university and industry. I particularly liked this mix. The meaningfulness of my actions convinces me here.