• Machine Learning

    The research of our group is focused on the development and analysis of cognitively inspired machine learning techniques to automatically analyze digital data sets and to infer useful information from the data. More about our current research

    © Universität Bielefeld

Current Research Projects

Water Futures

André Artelt, Ulrike Kuhl, Inaam Ashraf, Janine Strotherm, Valerie Vaquet, Paul Stahlhofen, Barbara Hammer

Duration: 2021-2027

The Water-Futures project is an interdisciplinary project bringing together researches from machine learning, engineering and economics working on the next generation of urban drinking water systems. Among others, one objective is to develop Explainable Machine Learning models in non-stationary environments for complex structured and networked data to seamlessly support human decision making for smart water systems by data-driven technologies. More information can be found on the official website: https://waterfutures.eu Publications

Funding: European Research Council (ERC)

SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems

Tristan Kenneweg, Thorben Markmann, Michiel Straat, Barbara Hammer

Duration: 2022-2026

Artificial Intelligence is ubiquitous in our society and a key driver of economic growth. This comes with societal and technological challenges, as the increasing proliferation of current AI in society can reduce the autonomy of humans instead of supporting it. In order to address demands for transparency of AI, human agency, safety and resource-efficiency, the research project SAIL focuses on the full life-cycle of AI systems. This highly interdisciplinary research project (Core AI, humanities, social sciences and engineering) paves the way for systems that are sustainable with respect to short-term and long-term objectives and take care of their technical, cognitive and societal impact at reasonable operating costs.

Funding: Ministry of Culture and Science of the Federal State North Rhine-Westphalia

Interpretable machine learning: Explaining Change

Fabian Fumagalli, Barbara Hammer

Duration: 2021-2025

Today, machine learning is commonly used in dynamic environments such as social networks, logistics, transportation, retail, finance, and healthcare, where new data is continuously being generated. In order to respond to possible changes in the underlying processes and to ensure that the models that have been learned continue to function reliably, they must be adapted on a continuous basis. These changes, like the model itself, should be kept transparent by providing clear explanations for users. For this, application-specific needs must be taken into account. The researchers working on Project C03 in the TRR318 are considering how and why different types of models change from a theoretical-mathematical perspective. Their goal is to develop algorithms that efficiently and reliably detect changes in models and provide intuitive explanations to users. Project website: https://trr318.uni-paderborn.de/en/projects/c03 Publications

Funding: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): TRR 318/1 2021 – 438445824

Healthy distrust in explanations and AI

Roel Visser, Barbara Hammer

Duration: 2021-2025

The focus of this project is on crucial overarching properties of decisions and explanations. The aim is to investigate the important question of how a person’s critical attitude towards an AI system can be supported by fostering a healthy distrust in intelligent systems, and whether and how this attitude can be reinforced by means of explainable machine learning methods. This attitude is crucial for persons to act mindfully and be empowered to shape AI. An attitude of healthy distrust towards intelligent systems and their outputs is needed in order to prevent either disuse (e.g. a complete unwillingness to use the system) or overtrusting (e.g. blind trust) of an intelligent system. By fostering an attitude of healthy distrust a person should be able to appropriately rely on intelligent systems in potentially high-risk domains such as medicine. This project benefits from interdisciplinary expertise in experimental studies and formal modeling as well as from being able to use cases from the domain of machine learning. By combining expertise from the fields of Machine Learning and Psychology the researchers will be able to investigate the psychological underpinnings of user trust and how healthy distrust can be fostered by and in the interaction with intelligent systems, ML, and XAI. Project website: https://trr318.uni-paderborn.de/en/projects/c01 Publications

Funding: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): TRR 318/1 2021 – 438445824



Riza Velioglu, Barbara Hammer

Duration: 2021-2025

The project ML4ProM (“Machine Learning for Process Mining”) is part of the Dataninja research training group and it aims to leverage predictive machine learning techniques to improve current efforts in process mining field. The project intends to supplement retrospective analysis with predictive Machine Learning technologies in order to comprehend the processes better. It also aims to provide predictions to enrich the information provided by the process models. Furthermore, the project would also produce alternative solutions by discovering deviations from the process model as early as possible. More information can be found on the official website: https://dataninja.nrw/?page_id=326 Publications

Funding: Ministry of Culture and Science of the Federal State North Rhine-Westphalia



Markus Vieth, Barbara Hammer

Duration: 2021-2025

The project RoSe (“Robust Individualization of Smart Sensor Technology through Transfer Learning Based Feature Selection”) is part of the Dataninja research training group and deals with the optimization of biosensor hardware and software. The hardware should be designed in such a way that the sensor is usable by many people, while it is customized for each user on the software side. Two example scenarios are considered: a novel shoe insole and a myoelectric arm orthosis. More information can be found on the official website: https://dataninja.nrw/?page_id=189 Publications

Funding: Ministry of Culture and Science of the Federal State North Rhine-Westphalia

AI-Marketplace: The Digital Platform for AI in Engineering

Sarah Schröder, Philip Kenneweg, Alexander Schulz, Barbara Hammer

Duration: 2019-2023

The AI Marketplace creates a unique ecosystem that brings together AI experts, vendors and users to leverage the full potential of artificial intelligence. The AI Marketplace is a platform that provides a space for secure data exchange and data sovereignty in addition to an intelligent matching of AI service providers and companies. 20 project partners work together in this project, consisting of research institutions and companies and the consortium is headed by the Heinz Nixdorf Institute. Project website: https://ki-marktplatz.com/en/ Publications

Funding: German Federal Ministry of Economic Affairs and Energy (BMWi) within the “Innovationswettbewerb Künstliche Intelligenz“

Out-of-Distribution Detection via Generative Modeling of Deep Latent Representations

Robin Chan

Duration: 2022-2024

State-of-the-art deep neural networks (DNNs) are usually trained to operate on a pre-defined and closed set of categories. Thus, they are ill-equipped when exposed to examples of a novel and unknown category. In real-world applications, to which DNNs are envisioned to be deployed to, the missing capability of handling such so-called “out-of-distribution” (OoD) examples could potentially lead to unwanted consequences. This becomes particularly crucial in high-stake applications. In this project, we tackle the problem of OoD detection in semantic segmentation, which is a key perception component in many existing vision systems based on machine learning. To this end, we are going to employ generative models to evaluate the likelihood of latent representations extracted from internal layers of the encoder of semantic segmentation DNNs. The intuition is that the likelihood measures how well observed features fit to those already known from training. We investigate and develop multiple types of generative models, paying special attention to the challenge of handling very high dimensional input data.

Funding: Ministry of Culture and Science of the Federal State North Rhine-Westphalia within KI-Starter

LeRntVAD - Interpretable Generative Machine Learning for Intelligent Control of Ventricular Assist Devices

Johannes Kummert, Robert Feldhans, Alexander Schulz, Barbara Hammer

Duration: 2021-2023

The central scientific question of the LeRntVAD project addresses the intuitive access to generative machine learning (ML) models. For this purpose, a synthetic simulation model will be created for the cardiovascular system. This model will then be used in the application to design an artificial intelligence (AI)-based controller for left ventricular assist devices (VADs). An AI controller extends the range of existing classical controllers by eliminating the need for invasive measurement data. In this project, we are collaborating with the Institute of Control Engineering at RWTH Aachen University and the University Hospital Aachen.

Funding: German Federal Ministry of Education and Research (BMBF) within the funding initiative “Erzeugung von synthetischen Daten für Künstliche Intelligenz”

Incremental and Collaborative Learning Systems for Multi-Variate Time-Series Analysis

Andrea Castellani, Jonathan Jakob, Sebastian Schmitt, Martina Hasenjäger, Barbara Hammer

Duration: 2021-2023

Intelligent monitoring systems are gaining more and more relevance with many possible applications, from industrial settings to assistive devices for humans. For many possible functions, prediction is one of the basic capabilities to provide the right information at the right time. Machine Learning models should be able to cope with concept drift in streaming non-stationary data. The aim of this project is to extend existing learning methods towards more heterogeneous and challenging learning scenarios. A particular focus of this project is to apply advanced Machine Learning research to real-world problems within the Honda Research Institute EU (HRI-EU) for example smart energy monitoring systems or models for exoskeleton control.

Funding: Honda Research Institute EU (HRI-EU)

Completed Research Projects


André Artelt, Barbara Hammer

Duration: 2019-2023

IMPACT “Implications of conversing with intelligent machines in everyday life for people´s beliefs about algorithms, their communication behavior and their relationship building” is an interdisciplinary project where groups from psychology, ethics, legal science and computer science study the interaction and communication of people with smart assistants. The project focuses on transparency, communication and relationship building. More information can be found on the official website: https://www.impact-projekt.de/ Publications

Funding: VW Foundation

TIM - Transferlernen zur intelligenten Kalibration spektral-optische Messdaten

tim.jpg Valerie Vaquet, Barbara Hammer

Duration: 2020-2023

One core issue when applying machine learning to sensor hardware is a lack in inter-operability. The goal of the TiM-project is the investigation of methods enabling transfer between different types of sensor hardware. The focus lies on hyperspectral and multispectral sensor data. More information can be found on the official website: https://math4innovation.de/index.php?id=58 Publications

Funding: German Federal Ministry of Education and Research (BMBF)

ITS.ML: Maschinelles Lernen für Intelligente Technische Systeme

Ulrike Kuhl, André Artelt, Alexander Schulz, Malte Schilling, Johannes Kummert, Barbara Hammer

Duration: 2019-2022

The aim of ITS.ML is to make machine learning (ML) for intelligent technical systems (ITS) available in the long term. A particular focus is on establishing ML as a service for small and medium-sized enterprises (SMEs).

ITS.ML combines theoretical and practical competencies of proven research institutions for ML. Researchers from the University of Bielefeld, the University of Paderborn, the Technical University of Ostwestfalen-Lippe and the University of Applied Sciences Bielefeld have joined forces to provide ML approaches to practitioners in industry.

See our project’s webpage for more information about scope and results: https://its-ml.de

Our FIT.ML platform provides detailed descriptions of applied examples and hands-on reference implementations of our ML innovations: https://its-ml.de/index.php/fit-ml/ Publications

Funding : German Federal Ministry of Education and Research (BMBF), German Aerospace Center (DLR) *