Research areas

Areas of action and expertise within the efl

Core Competences of the efl - the Data Science Institute

The efl- the Data Science Institute is commited to conducting high-quality research at the cutting-edge of novel technologies and their accompanying phenomena. Research focuses on recent developments from the area of digital markets, applied artificial intelligence, the business value of IT, the adoption and diffusion of new technologies and the impact of data science on various industries. The aim of our research is to solve practical problems with the tools of data science and methodology of scientific research. For this, quantitative, analytical, simulative, experimental and design oriented methods are used.

With all activities in research and joint projects with our partners in practice, we intend to contribute to the evolution of traditional industries towards digitized and high-performance industries. To be able to accordingly adress topics of interest, our institute is divided in several areas of competence in relation to industry areas under which the application of our data science competences fall.

A graph showing high-frequency data.

Data Science for Financial Services

In the last decade, the financial services industry has been subject to technological innovations and regulatory changes. Furthermore, we observe that more and more data is generated through digital and online processes. Thus, data science plays an increasingly important role in the financial industry and in finance-related research.

We aim to provide insights on the interplay of financial innovation and the structure of financial markets. Furthermore, we apply advanced statistical methods to analyze how market participants process quantitative information (e.g., financial figures) and qualitative information (e.g., unstructured data).

Data Science for Sales & Marketing

In this competence area, we focus on SalesTech and MarTech. The idea is to use data-driven-sales and marketing activities to support the sales persons to best sell to customers. So, we investigate how to best use (more and more often online) marketing to generate qualified leads for sales. The seamless integration of marketing and sales requires to model the entire process from attracting and engaging prospects, nurturing these prospects and finally converting them into customers. Cross- and up-selling activities aim at retaining these customers and ideally these customers become ambassadors in word-of-mouth or referral marketing activities.

insights from data science
A doctor explaining a health analytics app to a patient

Data Science for Health

Novel machine learning approaches are permeating the health industry: CNN-based radiology systems are superceding the diagnostical power of several physicians at once, user applications that integrate ML models enable end-users to self-diagnose and quantified self and wearables empower medtechs to disrupt the health industry.

In the area of data science for health, we deal with exactly these topics, among others with the focus on: human-machine interaction and collaboration, interactive machine learning and quantified self.

Data Science for Law

With increasing influence of technology in everyday life, the law industry has also been struck by the wave of digitalization. The automation of case classification with techniques from Natural Language Processing and Deep Learning, as well as automated intelligent law assistants, which may be first points of contact for simple questions are only some of the interesting research topics, which are covered by this research area.

Library with marble busts
A girl holding hands with an artificially intelligent robot.

General, cross-sectional research

The general section comprises all research endeavours that are either cross-sectional or cross-disciplinary. Research topics in this area encompass cutting-edge technological topics in the are of Artificial Intelligence, Human-Computer Interaction and Digital Transformation. Exemplary research topics are: Conversational Agents, Assistance Systems for the Individual, Decision Support Systems, AI and Digital Transformation, Algorithmic Discrimination, Smart Living, Social Media, and Internet of Things.


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