ADA Lovelace Center for Analytics, Data and Applications

New competence center for data analytics and AI in industry

Data are the raw material of the digital world. Therefore, the ability to master, analyze and evaluate data is crucial for companies to remain competitive. As data volumes grow, however, so does the importance of handling them efficiently. Methods from the field of artificial intelligence (AI) such as machine learning (ML) and mathematical optimization can help – but they require a special kind of expertise that is not readily available in many companies.

With the goal of bringing together research and industry, the Fraunhofer Institute for Integrated Circuits IIS with the Center for Applied Research on Supply Chain Services created the ADA Lovelace Center for Analytics, Data and Application, a unique research body in Bavaria, in collaboration with Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Ludwig-Maximilian-Universität München (LMU), with the additional involvement of the Fraunhofer Institute for Embedded Systems and Communication Technologies ESK and the Fraunhofer Institute for Integrated Systems and Device Technology IISB.


A cooperation platform for research and industry

At the ADA Lovelace Center, companies team up with leading national and international AI researchers to collaborate on specific projects. This approach leads to the rapid emergence of new data analytics methods and algorithms within concrete applications – delivering added value for industry, services, and research.

Infrastructure at the ADA Lovelace Center

The ADA Lovelace Center relies on innovative forms of cooperation and infrastructure.


Fraunhofer IIS Showroom with AI Focus

The showroom at the new location in Augustinerhof invites visitors to experience the topic of AI in an informative and interactive way. Try the video game based on projects from the »Driver Assistance Systems in Rail Traffic« application, which shows how mathematical optimization can be used to save energy.

Joint Labs for agile, time-limited project development


Company employees and ADA Lovelace Center researchers collaborate in small interdisciplinary development teams known as Joint Labs to work on specific AI-related issues.
Therefore, Fraunhofer IIS designed the Coworking Space CoWiS at its Nuremberg site to create an innovative working environment.

ADA Young Talents Hub



With a view to establishing a solid foundation of AI expertise in companies, the Center supports qualified junior researchers aand students seeking a career in industry with measures including supervision of degree papers and dissertations, or assistance with networking.

Our focus areas within AI research

Our work at the ADA Lovelace Center is aimed at developing the following methods and procedures in nine domains of artificial intelligence from an applied perspective.

Automatisches Lernen
© Fraunhofer IIS

Automated learning covers a vast field that ranges from automated feature recognition and selection for datasets, model search and optimization, or automated evaluation of these processes through to adaptive model adjustment using training data and system feedback. It plays a key role in areas such as assistance systems for data-driven decision support.

Sequenzbasiertes Lernen
© Fraunhofer IIS

Sequence-based learning concerns itself with the temporal and causal relationships found in data in applications such as language processing, event processing, biosequence analysis, or multimedia files. Observed events are used to determine the system’s current status, and to predict future conditions. This is possible both in cases where only the sequence in which the events occurred is known, and when they are labelled with exact time stamps.

Erfahrungsbasiertes Lernen
© Fraunhofer IIS

Experience-based learning refers to methods whereby a system is able to optimize itself by interacting with its environment and evaluating the feedback it receives, or dynamically adjusting to changing environmental conditions. Examples include automatic generation of models for evaluation and optimization of business processes, transport flows, or control systems for robots in industrial production.

Few Labels Learning
© Fraunhofer IIS

Major breakthroughs in AI involving tasks such as language recognition, object recognition or machine translation can be attributed in part to the availability of vast annotated datasets. Yet in many real-life scenarios, particularly in industry, such datasets are much more limited. We therefore conduct research on learning using small annotated datasets in the context of techniques for unsupervised, semi-supervised and transfer learning.

For several years, we have seen unbridled growth in the volume of digital data in existence, giving rise to the field of big data. When this data is used to generate knowledge, there is a need to explain the ensuing results and forecasts to users in a plausible and transparent manner. At the ADA Center, this issue is explored under the heading of explainable learning, with the goal of boosting acceptance for artificial intelligence among users in industry, research and society at large.

Mathematical optimization plays a crucial role in model-based decision support, providing planning solutions in areas as diverse as logistics, energy systems, mobility, finance, and building infrastructure, to name but a few examples. The Center is expanding its already extensive expertise in a number of promising areas, in particular real-time planning and control.

© Fraunhofer IIS

The task of semantics is to describe data and data structures in a formally defined, standardized, consistent and unambiguous manner. For the purposes of Industry 4.0, numerous entities (such as sensors, products, machines, or transport systems) must be able to interpret the properties, capabilities or conditions of other entities in the value chain.

Few Data Learning
© Fraunhofer IIS

We use few data learning to address key research issues involved in processing and augmenting data, or generating sufficient datasets, for instance in AI applications using material master data in industry. This includes processing flawed datasets and using simulation techniques to generate missing data.

Project and partners

Logo Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie

Project duration: 2018-2023

The ADA Lovelace Center’s national and international employees from the following institutions conduct research in nine key areas of artificial intelligence:

The Center also has the participation of the Fraunhofer Institute for Embedded Systems and Communication Technologies ESK, and the Fraunhofer Institute for Integrated Systems and Device Technology IISB, besides maintaining research partnerships with the Center for Machine Learning of the Georgia Institute of Technology in Atlanta (USA) and the Riken Institute for Advanced Intelligence in Tokyo (Japan).

Other topics of interest


Ada Lovelace

Ada Lovelace, after whom the ADA Lovelace Center is named, is regarded as the developer of the first ever computer program. Her early speculations on the possibility of machine AI date back to the 19th century.

Interview with Professor Alexander Martin

»Data Analytics – what is it and how does it benefit business?«


»Data analytics itself is nothing new,« says Professor Alexander Martin of Fraunhofer SCS. »What is new is that we’re working with enormous quantities of data and that the methods available are getting better all the time. This means we can gather and analyze the data in a meaningful way.«

In this interview, the industrial mathematician explains exactly what this looks like and how it can lead to successful business decisions.