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 large area starting with the automation of feature detection and selection for given datasets as well as model search and optimization, continuing with their automated evaluation, and ending with the adaptive adjustment of models through training data and system feedback.


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

Learning from Experience 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.

© Fraunhofer IIS

Data-centric AI (DCAI) offers a new perspective on AI modeling that shifts the focus from model building to the curation of high-quality annotated training datasets, because in many AI projects, that is where the leverage for model performance lies. DCAI offers methods such as model-based annotation error detection, design of consistent multi-rater annotation systems for efficient data annotation, use of weak and semi-supervised learning methods to exploit unannotated data, and human-in-the-loop approaches to improve models and data.

To use artificial intelligence safely and appropriately in practice, machine learning (ML) must be comprehensible to the user. In many areas, such as medical decision making or quality control in manufacturing, it is important to understand the basis on which the model makes decisions and predictions in order to create transparency and trust. Methods for comprehensible and trustworthy AI are researched at the ADA Lovelace Center in the competence pillar of Comprehensible & Trustworthy AI contributing to human centered AI for users in in business, academia and society.

© Fraunhofer IIS

Process-aware Learning is the link between process mining, the data-based analysis and modeling of processes, and machine learning. The focus is on predicting process flows, process metrics, and process anomalies. This is made possible by extracting process knowledge from event logs and transferring it into explainable prediction models. In this way, influencing factors can be identified and predictive process improvement options can be defined.

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.

Tiny Machine Learning (TinyML) brings AI even to microcontrollers. It enables low-latency inference on edge devices that typically have only a few milliwatts of power consumption. To achieve this, Fraunhofer IIS is conducting research on multi-objective optimization for efficient design space exploration and advanced compression techniques. Furthermore, hierarchical and informed machine learning, efficient model architectures and genetic AI pipeline composition are explored in our research. We enable the intelligent products of our partners.

© Fraunhofer IIS

Hardware-aware Machine Learning (HW-aware ML) focuses on algorithms, methods and tools to design, train and deploy HW-specific ML models. This includes a wide range of techniques to increase energy efficiency and robustness against HW faults, e.g. robust training for quantized DNN models using Quantization- and Fault-aware Training, and optimized mapping and deployment to specialized (e.g. neuromorphic) hardware. At Fraunhofer IIS, we complement this with extensive research in the field of Spiking Neural Network training, optimization, and deployment.

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.