Data Science for Social Issues

Dr. Helena Mihaljevic was appointed Professor of Data Science and Analytics at HTW Berlin (University of Applied Sciences) on July 1 2018. The mathematician brings a wealth of practical experience to the university from several years working as a data scientist in the private sector. “I am particularly interested in applied research in the areas of statistical data analysis, data mining, machine learning and natural language processing, as well as in the transparency of algorithmic methods – an increasingly important area. The use of data analysis for social issues is an area I find especially appealing,” says Mihaljevic. 

Born in Sarajevo, she came to Germany in the mid-1990s. After studying mathematics in Göttingen, she completed a PhD in dynamical systems in Liverpool and went on to work as a research assistant at Kiel University. She decided against a classic academic career in pure mathematics and moved to the Berlin base of the Leibniz Institute for Information Infrastructure (FIZ Karlsruhe). “At FIZ I was involved in a wide variety of data projects, which led to an increasing focus on data science – in my view, it’s the perfect combination of data, math, algorithms and new technologies.”  

“Fortunately, HTW Berlin is a university with a strong emphasis on applied research, so that there is a good basis for cooperation with the Einstein Center and with private firms,” says Helena Mihaljevic. Since one and a half years she is involved in the interdisciplinary project 'Gender Gap in Science‘, which is supported by the International Science Council (ISC). “The project is about investigating, measuring and visualizing the gender gap in various fields of science and research and across different regions. Our findings should help the unions in the various fields to take appropriate measures to reduce the gender gap.” In this project Helena Mihaljevic is leading research activities on the data-driven analysis of publication patterns. 

“Despite the increasing number of female students in the STEM subjects, women are still severely underrepresented in research and professorships. The key role played by research publications in academic careers means it is enormously important to analyze such data in order to understand the gender gap.” Specially developed algorithms enable her and her team to extract answers from the publication data to questions such as how the “drop out” rate of women from research and academia has changed over the years, the extent to which women can access prestigious journals and what kinds of collaborations they enter into. “In order to carry out these statistical analyses in the first place, various data sources have to be pooled and processed using techniques from areas such as machine learning.” 

Mihaljevic, who is an amateur boxer and a former Berlin champion, is very much looking forward to working with other colleagues at the Einstein Center Digital Future (ECDF). “Data plays an important role in most digital fields, and algorithmic methods are becoming increasingly important. This opens up interesting opportunities for cooperation within the ECDF.”  

Mihaljevic will work closely over the next few years with the Berlin public transport provider, BVG, which is co-financing her computer science professorship.