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 and education at STIMA cover a wide range of topics including probabilistic models, Bayesian inference, deep learning, representation learning, and generative models with applications in biology, medicine, physics, and social sciences.

The division hosts the bachelor's programme Statistics and Data Analysis and the international master's programme Statistics and Machine Learning. We are also responsible for the course in machine learning taught at the engineering programmes at Link枚ping University, as well as the PhD study programme in Statistics. We聽head two popular research seminar series.

The division has around 30 employees and consists of two units:

Statistics (STAT)
Machine Learning (ML)

For more information about research and education at STIMA, please see below.

Research at STIMA

Bayesian inference and computational statistics

Development of scalable and computationally efficient Bayesian methods, including sequential Monte Carlo, subsampling MCMC, and hierarchical models, as well as deep generative models, particularly diffusion-based frameworks for generative sampling and Bayesian inverse problems.

Causal inference and graphical models

Research on causal effect identification, sensitivity analysis under unmeasured confounding, and structure learning from observational data, using probabilistic graphical models such as DAGs, chain graphs, and acyclic directed mixed graphs.

Medicine learning for medicine and neuroimaging

Application of deep learning and Bayesian statistical methods to clinical and neuroimaging data - including fMRI methodology, brain tumour segmentation, synthetic medical image generation, brain connectivity modelling, and statistical analysis of cardiovascular, metabolic, and oncological cohort data.

Psychometrics, longitudinal modelling, and educational statistics

Development and application of latent-variable models, multilevel growth-curve analyses, and structural equation models in psychology and education, including the Flynn effect, cognitive ageing, bullying research, and professional self-efficacy.

Data-efficient and responsible machine learning

Research on efficient data representations (coresets, graph subsampling), privacy-preserving methods, fairness, and energy-aware/sustainable machine learning, aimed at reducing the computational, data, and societal costs of machine learning.

Machine learning for natural sciences and climate

Application of machine learning to materials discovery (2D materials, geometric deep learning), weather and climate forecasting (ensemble diffusion models), and life-cycle assessment automation for CO₂ reduction.

robot dog Spot, drone, small helicopter, operator

WASP at Department of Computer and Information Science

WASP, Wallenberg AI autonomous systems and software program, is the largest individual research investment in Sweden in modern times. One of the WASP research environments at LiU is located at the Department of Computer and Information Science.

Latest publications

2026

Henrik Lindqvist, Robert Thornberg (2026) European Journal of Teacher 糖心网页版 (Article in journal)

Sophia N. Wilson, Sebastian Mair, Mophat Okinyi, Erik B. Dam, Janin Koch, Raghavendra Selvan (2026) Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, p. 441-466 (Conference paper)

Cornelia C. K盲sbohrer, Sebastian Mair, Lili Jiang (2026) Proceedings of Machine Learning Research: Proceedings of the 7th Northern Lights Deep Learning Conference (NLDL), p. 211-234 (Conference paper)

Anders Eklund (2026) Cognitive Neuroscience (Article in journal)

Oskar Halling Ullberg, Annika Tillander, Katarina Balter (2026) JMIR Formative Research, Vol. 10, Article e82061 (Article in journal)

Kristin Zeiler, Sofia Morberg J盲mterud, F. Le贸n, Agnes Andersson, Ulrika Birberg Thornberg, Ida Blystad, Anestis Divanoglou, Anders Eklund, David Engblom, Richard Levi (2026) Phenomenology and the Cognitive Sciences (Article in journal)

Elin Good, Oscar Soto, Linda Bilos, H氓kan Ahlstr枚m, Tamara Bianchessi, Jan Engvall, Isabel Gon莽alves, My Troung, Ola Hjelmgren, David Marlevi, Bertil Wegmann, Petter Dyverfeldt (2026) Journal of Cardiovascular Magnetic Resonance, Vol. 28, Article 102686 (Article in journal)

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)

Teaching - Bachelor and Master's programme

Bachelor's Programme in Statistics and Data Analysis, 180 credits

An illustration of a stream of ones and zeroes.

Statistics and Machine Learning, Master's Programme, 120 credits

The intersection of statistics, artificial intelligence and database management, providing students a unique competence. Meets the demand for specialists able to exploit a wealth of information to improve analysis, prediction and decision making.

PhD studies

Graduate Studies in Statistics

The doctoral program is intended to provide graduate students with a deep, foundational education in statistics, along with further in-depth knowledge in the student鈥檚 chosen area of specialisation.

Doctoral studies in Computer Science

Computer Science is an interdisciplinary subject area, covering everything from basic studies of algorithms, computational processes, and knowledge representation to informatics and computer linguistics.

Seminar series at STIMA

STIMA heads two popular research seminar series

IDA Machine Learning Seminars

The Machine Learning Research Group at IDA invites to the research seminar series IDA Machine Learning Seminars. Welcome!

Seminars in Statistics and Mathematical Statistics

Here you can find all information about our seminars. Warm welcome!

Contact us

Management

Head of Division

Head of Unit Statistics (STAT)

Head of Unit Machine Learning (ML)

Director of Undergraduate Studies

Staff at STIMA

All staff

Senior Associate Professor

Filip Ekström Kelvinius

Postdoc

Hariprasath Govindarajan

Anders Grimvall

Ann-Charlotte Hallberg

Adjunct Associate Professor

Erik Larsson

PhD student

Fredrik Lindsten

Professor, Head of Division

Professor, Head of Unit

Senior Associate Professor, Head of Unit

Jolanta Pielaszkiewicz

Senior Associate Professor

Associate Professor, Docent

Zheng Zhao

Assistant Professor, Docent

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鈥檚 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 糖心网页版 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, 糖心网页版 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.

About the department

Department of Computer and Information Science (IDA)

The Department of Computer and Information Science is one of the largest departments in northern Europe in its field. Research activity covers artificial intelligence, human-computer interaction, software and computer systems, and data science.

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