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The Division of Statistics and Machine Learning (STIMA)

The Division of Statistics and Machine Learning is part of the Department of Computer and Information Science. The research and teaching activities at the division are focused on modern data analysis. 

Research

STIMA is a division of Statistics and Machine Learning that belongs to a department of computer science. This fact makes us unique in Sweden, and we like to view ourselves as Sweden's most modern division of statistics with a clear focus on state-of-the-art data analysis, prediction and decision making in complex systems.

We are engaged in basic methodological research, motivated by a wide range of problems in areas that span from journalism and psychology to genetics and robotics.

Teaching

The division hosts the unique bachelor's programme Statistics and Data Analysis and the international master's programme Statistics and Machine Learning.

We are responsible for the course in machine learning taught at the engineering programmes at Linköping University, as well as the PhD study programme in Statistics.


Seminar series at STIMA

News at STIMA

News and major articles

Innovative idea for more effective cancer treatments rewarded

Lisa Menacher has been awarded the 2024 Christer Gilén Scholarship in statistics and machine learning for her master’s thesis. She utilised machine learning in an effort to make the selection of cancer treatments more effective.

Tomas Landelius and Carolina Natel de Moura.

The focus period resulted in new collaborations for the climate

In the fall of 2024, researchers from around the world once again gathered at ¸£Àû¼§ for ELLIIT's five-week focus period. This time, the goal was to initiate and deepen collaborations in climate research using machine learning.

Participants are listening to a lecture.

Symposium aiming to improve the climate

In the fall of 2024, ¸£Àû¼§ once again hosted ELLIIT's five-week-long focus period. This guest researcher program aimed for greater breadth in interdisciplinarity this year, with the theme of machine learning for climate science.

Research at STIMA

Latest publications

2026

Bayu Brahmantio, Krzysztof Bartoszek, Etka Yapar (2026) BMC Bioinformatics, Vol. 27, Article 77 (Article in journal)
Lisa Maria Menacher, Liam Ward, Fredrik Heintz, Henrik Green, Oleg Sysoev (2026) Analytical Chemistry, Vol. 98, p. 6589-6597 (Article in journal)
Zheng Zhao (2026) COMMUNICATIONS IN INFORMATION AND SYSTEMS, Vol. 26, p. 151-167 (Article in journal)
Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Daniel Giles, Matt J. Kusner, Stanislas Pamela, Marc Peter Deisenroth (2026) Machine Learning: Science and Technology, Vol. 7, Article 015025 (Article in journal)
Jonas Bjermo, Ellinor Fackle Fornius, Frank Miller (2026) Applied psychological measurement (Article in journal)
Tatjana Pavlenko, Annika Tillander, Fredrik Boulund, Gabriella Edfeldt (2026) EURASIP Journal on Advances in Signal Processing, Vol. 2026, Article 11 (Article in journal)

2025

Jose M Pena (2025) CAUSAL LEARNING AND REASONING, p. 693-703 (Conference paper)
Anders Larsson, Bertil Wegmann, Toralph Ruge, Joakim Alfredsson, Carl Johan Östgren, Tomas Lindahl (2025) Global Cardiology, Vol. 3, p. 25­-32 (Article in journal)
Sehrish Qummar, August Ernstsson, Christoph Kessler, Oleg Sysoev (2025) 2025 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS, IPDPSW, p. 423-432 (Conference paper)
Linda Wänström, O'Keefe Patrick, Muniz-Terrera Graciela, Voll Stacey, D. Mann Frank, Clouston Sean, Hofer Scott, L. Rodgers Joseph (2025) Intelligence, Vol. 113, Article 101966 (Article in journal)

Teaching - Bachelor and Master's programme

PhD studies

Contact us

Staff at STIMA

About the department