Efficient Algorithms and Data Structures​

The efficiency of algorithms and data structures is becoming increasingly important in the area of big data, where complicated analysis is performed on very large datasets. Often algorithm efficiency is the deciding factor in analysis quality (of even if it possible at all). Modelling modern computational infrastructure (such as complicated memory-hierarchies, GPUs and modern clientserver architectures), and development of algorithms and data structures for these models/devices, is also increasingly important. 

The main objectives are to extend the basis understanding of efficient algorithms and data structures for fundamental (big data) problems, as well as to further increase the Danish strength and capacity within algorithms and data structures.

Since efficient algorithms and data structures are important – often even essential – in other computer science research areas (as also explicitly indicated e.g. in the descriptions of the artificial intelligence and data management disciplines), as well as in applications, there are significant opportunities for synergies between algorithms researchers and other researcher in the project.

Thus, use of algorithmic advances in interdisciplinary and real-life application settings is another important objective.

Workstream related projects

SciTech project

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

The search for nearest neighbors is an emerging and increasingly vital component in data analysis tasks, for example using vector embedding databases. Typically, the search is the bottleneck in terms of efficiency. Approximate nearest neighbor (ANN) search methods are often employed to speed up the application. However, different methods for ANN search come with different biases that can be positive or negative for the downstream application. In this project, the bias of different ANN methods and its impact on different applications will be studied.

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SciTech project

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.

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SciTech project

Machine Learning Algorithms Generalisation

AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data.

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Workstream manager

Riko Jacob

Associate Professor

IT University of Copenhagen
Computer Science Department
E: rikj@itu.dk

Contributing researchers

Aarhus University
Department of Computer Science

Philip Bille

Professor

Technical University of Denmark
DTU Compute

Mikkel Thorup

Professor

University of Copenhagen
Department of Computer Science

Joan Boyar

Professor

University of Southern Denmark
Department of Mathematics and Computer Science

IT University of Copenhagen
Department of Computer Science

Aalborg University
Department of Computer Science

Jan Damsgaard

Professor

Copenhagen Business School
Department of Digitalization