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Division of Theoretical Computer Science

The Division of Theoretical Computer Science (TCS) works on the foundations of efficient and correct algorithms and software, and it deals with the tractability of computational problems. Applications areas include software engineering, computer security and privacy, cryptography, verification and sat solving, natural language tools, and computer science education 

Research areas

Algorithms and Complexity research

Algorithms and Complexity

Solving a computational problem requires resources and the fundamental question studied in this research area is to determine, as closely as possible, the computational difficulty of basic problems.

computer security research

Computer security

We study the impact of network communication delays and failures on the behavior of networked software, where error handling is difficult to test with conventional means.

Computer Science education research

Computer science education

Computer science education research is an interdisciplinary area aiming at improving the understanding of how students learn computer science and how the teaching and assessment of computer science could be improved.

Foundations of data science research

Foundations of Data Science

Data science has emerged as a key discipline to enable transforming the available data into knowledge products that bring insights into the corresponding domains, improve decision making, and accelerate scientific discovery.

Software construction and analysis research

Software construction and analysis

A major current technological and societal challenge is to be able to produce software systems that behave in a reliable and predictable manner. We perform research into different ways of mastering software complexity.

Meet the division

Recent publications

[1]
E. Ábrahám et al., "Why do women pursue a Ph.D. in Computer Science?," Journal of Systems and Software, vol. 231, 2026.
[2]
S. Díaz-Aranda et al., "Error Bounds for the Network Scale-Up Method," in KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025, pp. 498-509.
[3]
R. Liu et al., "Dirty-Waters: Detecting Software Supply Chain Smells," in FSE Companion 2025 - Companion Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering, 2025, pp. 1045-1049.
Full list in the KTH publications portal

News

Benoit Baudry (Université de Montréal) and Martin Monperrus (KTH Royal Institute of Technology)

Taking Humour Seriously in Graduate Training

For nearly two decades, Benoit Baudry (Université de Montréal) and Martin Monperrus (KTH Royal Institute of Technology) have supervised master’s and PhD students. Along the way, they’ve drawn a perhap...

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Martin Monperrus

AI fixes security flaws – KTH wins prestigious award

Can AI fix security flaws before hackers strike? KTH researchers say yes—and their breakthrough won the Best Paper Award 2023 from IEEE Transactions on Software Engineering. By using AI to automate se...

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Samuel Lavebrink and Madeleine Lindström are studying the Master's programme Machine Learning.

How to stop cyber-attacks with honeypots

In the ever-evolving landscape of cyber warfare, defending against human-controlled cyberattacks requires innovative strategies. A recent study conducted by students at KTH delves into the realm of cy...

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