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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

2025

Anders Larsson, Bertil Wegmann, Toralph Ruge, Joakim Alfredsson, Carl Johan Östgren, Tomas Lindahl (2025) Global Cardiology, Vol. 3 (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)
Martin Andrae, Tomas Landelius, Joel Oskarsson, Fredrik Lindsten (2025) The Thirteenth International Conference on Learning Representations (ICLR 2025) (Conference paper)
Marie-Ange Fleury, Louis Ohl, Lionel Tastet, Mickaël Leclercq, Frédéric Precioso, Pierre-Alexandre Mattei, Romain Capoulade, Kathia Abdoun, Élisabeth Bédard, Marie Arsenault, Jonathan Beaudoin, Mathieu Bernier, Erwan Salaun, Jérémy Bernard, Mylène Shen, Sébastien Hecht, Nancy Côté, Arnaud Droit, Philippe Pibarot (2025) The European Heart Journal - Digital Health, Article ztaf115 (Article in journal)
Jonas Malmborg, Ludvig Joborn, Mattias Beming, Anders Nordgaard, Ivo Alberink (2025) FORENSIC CHEMISTRY, Vol. 46, Article 100699 (Article in journal)
Louis Ohl, Pierre-Alexandre Mattei, Frederic Precioso (2025) ACM Computing Surveys, Vol. 58, Article 90 (Article in journal)
Sourabh Balgi, Marc Braun, Jose M. Peña, Adel Daoud (2025) International Journal of Approximate Reasoning, Vol. 187, Article 109531 (Article in journal)
Arnaud Doucet, Victor Elvira, Fredrik Lindsten, Joaquin Miguez (2025) FOUNDATIONS OF DATA SCIENCE, Vol. 7 (Article in journal)
Annika Tillander, Susanna Lehtinen-Jacks, Nisha Singh, Oskar Halling Ullberg, Ulrika Florin, Katarina Balter (2025) Data in Brief, Vol. 63, Article 112105 (Article in journal)

Teaching - Bachelor and Master's programme

PhD studies

Contact us

Staff at STIMA

About the department