Research

Our group is dedicated to both basic and application-oriented research in the expansive field of artificial intelligence and machine learning.

We focus on adaptive learning methods, showcased in projects like Hephaestus, which explores industrial data from drilling machines in a production pipeline. KISWIND concentrates on detecting outliers in multiple sensor data from wind turbines, while OSCAR focuses on textual data streams, including those generated by social media. In addition, we are committed to responsible AI, demonstrated through projects such as MAMMoth, which addresses multidimensional discrimination in complex data, and projects NoBIAS and BIAS that delve into understanding bias sources and designing mitigation strategies in AI systems. Moreover, our research extends to exploring the creative potential of AI to enhance data quality, as seen in projects like STELAR, and to innovatively design new data and solutions, exemplified by projects such as SFB1463.

We apply our methods across various industries, including education, social networks, banking, agriculture, manufacturing, and engineering. Our research receives support from EU and national funding, including the DFG and the Volkswagen Foundation.

Current Research Projects

EU projekt: MAMMOth - Multi-Attribute, Multimodal Bias Mitigation in AI Systems

EU projekt: STELAR - Spatio-TEmporal Linked data tools for the AgRi-food data space

DFG Projekt: SFB 1463 - Integrated Design and Operation Methodology for Offshore Megastructures

DFG Projekt: Hephaestus - Machine learning methods for adaptive process planning of 5-axis milling

EU projekt: ITN NoBIAS - Artificial Intelligence without Bias

Volkswagen Foundation project: BIAS - Bias and Discrimination in Big Data and Algorithmic Processing

LernMINT - Dealing with bias and discrimination in learning analytics models

Completed Research Projects

OSCAR - Opinion Stream Classification with Ensembles and Active leaRners

Funding: Deutsche Forschungsgemeinschaft
Project duration: 2017 – 2019

Many data accumulating in the Web reflect opinions on diverse subjects - products, institutions, events (e.g., elections) or topics (e.g., earth warming). Opinionated documents constitute a continuous stream; polarity learning on them delivers insights on the attitude of people towards each subject. Polarity learning algorithms must cope with classic Big Data characteristics: high volume and velocity of the arriving data, and volatility of the learned concepts, since subjects and attitudes of people toward certain subjects change over time. In OSCAR, we will develop classifiers that operate on an evolving feature space, adapt to changes in both vocabulary and data, and operate with limited class labels.

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Transalpine mobility and knowledge transfer

Funding: DFG FOR 1670
Role: Postdoctoral researcher (Eirini Ntoutsi)

The project aims at the establishment of an isotopic fingerprint for bioarchaeological finds, especially cremations, and its application to archaeological and cultural-historical problems of the Late Bronze Age until Roman Times. From a computer science persective, our focus is on the development of innovative methods that allow complete scientific analysis of project related data despite their complexity. We focus on data management and automated data analysis (similarity search, cluster analysis, outlier recognition) for the establishment of small-scaled isotopic fingerprints.

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GeoPKDD - Geographic Privacy-aware Knowledge Discovery and Delivery

FP6/IST project
Project duration: 2005 – 2009

GeoPKDD aims at developing theory, techniques and systems for knowledge discovery and delivery, based on new automated privacy-preserving methods for extracting user-consumable forms of knowledge from large amounts of raw data referenced in both space and time dimensions.

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Knowledge Discovery and Pattern Management - the PBMS approach

Funding: EPEAEK II / Heracletos Programme
Project duration: 2003 – 2005

The goal of this project is the efficient management of data mining patterns extracted from large databases, with emphasis on the pattern similarity assesment problem.

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PANDA - Patterns for Next Generation Database Systems

Funding: IST project
Project duration: 2001 – 2004

PANDA working group studies current state-of-the-art in pattern management and explores novel theoretical and practical aspects of a Pattern Base Management System (so-called, PBMS). PANDA's goal is the efficient and effective management of patterns; just as raw data are managed by traditional DBMS.

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