Online Algorithms with Predictions

Our focus is on improving optimization algorithms in online decision-making. Using techniques from online algorithms for solving optimization problems, we can provide worst-case guarantees, but normal (averagecase) behavior may not be satisfactory. Using techniques from machine learning, we can often provide good behavior in practice, but guarantees are lacking, and in particular missing for situations not captured by the training data. We aim at combining the best features from these two areas.

Benefit and Bias of Approximate Nearest Neighbor Search for Machine Learning and Data Mining

The search for nearest neighbors is essential but often inefficient in applications like clustering and classification, especially with high-dimensional big data. Traditional methods become impractical due to the curse of dimensionality, making approximate nearest neighbor (ANN) search methods a faster alternative despite their inexact results. ANN methods significantly enhance processing speed, impacting algorithmic decision-making processes by introducing trade-offs in accuracy, bias, and trustworthiness, which must be carefully considered for different use cases.

Low-Code Programming of Spatial Contexts for Logistic Tasks in Mobile Robotics

Together with industrial partners, this project will investigate production scenarios where a machine can be operated by untrained personnel by using low-code development for adaptive and re-configurable robot programming of logistic tasks.

Trust through Software Independence and Program Verification

Greenland’s election law was changed in 2020, which now permits the use of Internet Voting. Together with the authorities in Greenland, this project will investigate the effects of program verification on public trust in election technologies. The project aims to contribute to making internet elections more credible, which can strengthen developing and post-conflict democracies around the world.

Multimodal Data Processing of Earth Observation Data

Based on observations of the Earth, a range of Danish public organizations build and maintain important data foundations that are used for decision-making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark. This project aims to support the digital acceleration of the green transition by strengthening the data foundation for environmental data.

Summer School on Missing Data, Augmentation and Generative Models

14 – 18 August 2023
This summer school will introduce the state-of-the-art for handling too little or missing data in image processing tasks.

Digitalisation can definitely boost the green transition

23 July 2022
Artificial intelligence and algorithms can help calculate how we can best heat our homes, produce efficiently, transport with the least possible energy consumption, and make optimal use of the IT infrastructure as part of the green transition. But it requires that we dare to delegate more tasks to algorithms and invest more in research and development.