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 Linköping University, as well as the PhD study programme in Statistics. We 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.

ELLIIT - a network for Information and Communication Technology

ELLIIT is a network organization for Information and Communication Technology (ICT) research at Linköping, Lund, Halmstad and Blekinge. The objective is scientific excellence in combination with industrial relevance and impact.

Latest publications

2026

Sophia N. Wilson, Sebastian Mair, Mophat Okinyi, Erik B. Dam, Janin Koch, Raghavendra Selvan (2026)

How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, p. 441-466 (Conference paper)

https://dx.doi.org/10.1145/3805689.3812393

Cornelia C. Käsbohrer, Sebastian Mair, Lili Jiang (2026)

Assessing the Fragility of SHAP-Based Model Explanations Using Counterfactuals Proceedings of Machine Learning Research: Proceedings of the 7th Northern Lights Deep Learning Conference (NLDL), p. 211-234 (Conference paper)

Anders Eklund (2026)

Increasing statistical power in functional MRI through permutation and multivariate statistics Cognitive Neuroscience (Article in journal)

https://dx.doi.org/10.1080/17588928.2026.2682170

Oskar Halling Ullberg, Annika Tillander, Katarina Balter (2026)

A Sustainable Lifestyle Intervention Among Office Workers: Cluster Randomized Pilot and Feasibility Study JMIR Formative Research, Vol. 10, Article e82061 (Article in journal)

https://dx.doi.org/10.2196/82061

Kristin Zeiler, Sofia Morberg Jämterud, F. León, Agnes Andersson, Ulrika Birberg Thornberg, Ida Blystad, Anestis Divanoglou, Anders Eklund, David Engblom, Richard Levi (2026)

Affective dimensions of fatigue in post COVID-19 condition: An interdisciplinary investigation across phenomenology and biomedicine Phenomenology and the Cognitive Sciences (Article in journal)

https://dx.doi.org/10.1007/s11097-026-10151-5

Elin Good, Oscar Soto, Linda Bilos, Håkan Ahlström, Tamara Bianchessi, Jan Engvall, Isabel Gonçalves, My Troung, Ola Hjelmgren, David Marlevi, Bertil Wegmann, Petter Dyverfeldt (2026)

Carotid Plaque Characteristics and Their Association with Cardiovascular Risk Factors and Coronary Atherosclerosis in a Middle-Aged Population Journal of Cardiovascular Magnetic Resonance, Vol. 28, Article 102686 (Article in journal)

https://dx.doi.org/10.1016/j.jocmr.2026.102686

Bayu Brahmantio, Krzysztof Bartoszek, Etka Yapar (2026)

Bayesian inference of mixed Gaussian phylogenetic models BMC Bioinformatics, Vol. 27, Article 77 (Article in journal)

https://dx.doi.org/10.1186/s12859-026-06399-y

Lisa Maria Menacher, Liam Ward, Fredrik Heintz, Henrik Green, Oleg Sysoev (2026)

LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening Analytical Chemistry, Vol. 98, p. 6589-6597 (Article in journal)

https://dx.doi.org/10.1021/acs.analchem.5c05404

Zheng Zhao (2026)

Generative diffusion posterior sampling for informative likelihoods COMMUNICATIONS IN INFORMATION AND SYSTEMS, Vol. 26, p. 151-167 (Article in journal)

https://dx.doi.org/10.4310/cis.260128110636

Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Daniel Giles, Matt J. Kusner, Stanislas Pamela, Marc Peter Deisenroth (2026)

Uncertainty quantification of surrogate models using conformal prediction Machine Learning: Science and Technology, Vol. 7, Article 015025 (Article in journal)

https://dx.doi.org/10.1088/2632-2153/ae2e7b

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’s 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

Professor Emeritus

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’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 Linköping University 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, Linköping University 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.

The Division of Statistics and Machine Learning (STIMA)

Research at STIMA

Bayesian inference and computational statistics

Causal inference and graphical models

Medicine learning for medicine and neuroimaging

Psychometrics, longitudinal modelling, and educational statistics

Data-efficient and responsible machine learning

Machine learning for natural sciences and climate

WASP at Department of Computer and Information Science

ELLIIT - a network for Information and Communication Technology

Latest publications

2026

Teaching - Bachelor and Master's programme

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

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

PhD studies

Graduate Studies in Statistics

Doctoral studies in Computer Science

Seminar series at STIMA

STIMA heads two popular research seminar series

IDA Machine Learning Seminars

Seminars in Statistics and Mathematical Statistics

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

News at STIMA

News and major articles

Innovative idea for more effective cancer treatments rewarded

The focus period resulted in new collaborations for the climate

Symposium aiming to improve the climate

Machine learning can give the climate a chance

SEK 13 million for research into solving murders using AI

Master's student awarded for research on traffic flow patterns

Minister for Energy and Digital Development visits IDA

Measuring muscle mass with neural network

Among the world’s 36 best in machine learning

LiU alumnus, Christer Gilén want to give something back

About the department

Department of Computer and Information Science (IDA)