Home

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.

Machine 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.
Global networks combined with light.

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.

Visit ELLIIT.
En ai-genererad bild av en robot likt typen boston dynamic samt en drönare.

AI4X Center of Excellence (LiU-CoE)

As a Center of Excellence, AI4X is a long-term investment designed to strengthen quality, development and collaboration in the field of AI research.
Car, taxi and bus traffic on road intersection at night in Hong Kong downtown, drone aerial view. Street commuters, Asian city life or public transport concept.

The Swedish Excellence Centre for Computational Social Science

This is a research hub and training centre at the forefront of Computational Social Science, a rapidly growing field that leverages computational tools to advance our understanding of human behavior and the complex social dynamics it can bring about.

Professional Self-efficacy, Emotional challenges and Moral stress in Teacher and Medical Education

Schools and healthcare are complex that place high cognitive and emotional demands on professionals. The project investigates how students develop professional self-efficacy during their education to become teachers and doctors.

Latest publications

2026

B. k. Masinde, A. Ozarowska, G. Zaniewicz, W. Meissner, Krzysztof Bartoszek (2026) FlyingR: An R package to analyze flight range of avian migrants SoftwareX, Vol. 36, Article 103066 (Article in journal) https://dx.doi.org/10.1016/j.softx.2026.103066
Karl Sigfrid, Ellinor Fackle-Fornius, Frank Miller (2026) Estimating Abilities With an Elo-Informed Growth Model Journal of educational and behavioral statistics (Article in journal) https://dx.doi.org/10.3102/10769986261489723
Ahcène Boubekki, Samuel G. Fadel, Sebastian Mair (2026) Multi-depth Concept Extraction for Post-Hoc Vision Encoder Explanation Machine Learning and Knowledge Discovery in Databases. Research Track.: ECML PKDD 2026, p. 449-467 (Conference paper) https://dx.doi.org/10.1007/978-3-032-37664-0_26
Sophia N. Wilson, Andrew Millard, Guðrún Fjóla Guðmundsdóttir, Raghavendra Selvan, Sebastian Mair (2026) Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI Proceedings of the 43rd International Conference on Machine Learning, p. 172585-172604 (Conference paper)
Hugo Morvan, Jonas Agholme, Bjorn Eliasson, Katarina Olofsson, Ludger Grote, Fredrik Iredahl, Oleg Sysoev (2026) Scalable multiple imputation for national-scale healthcare registries and EHR International Journal of Medical Informatics, Vol. 222, Article 106717 (Article in journal) https://dx.doi.org/10.1016/j.ijmedinf.2026.106717
Johan Alenlöv (2026) Stacked Feynman-Kac: A Generalised Method for Within-Timestep Sampling of Intermediate Distributions for Diffusion Models Transactions on Machine Learning Research (Article in journal)
Carl Soderberg, Oleg Sysoev, Rasmus Magnusson, Fredrik Kugelberg, Henrik Green (2026) Anomaly detection as a tool for identifying cases of interest in postmortem toxicology Scientific Reports, Vol. 16, Article 25686 (Article in journal) https://dx.doi.org/10.1038/s41598-026-65664-5
Monika Mioduchowska, Pushpalata Kayastha, Magdalena M. Bartylak, Edyta Konecka, Bayu Beta Brahmantio, Julita Mackiewicz, Wojciech Przybyszewski, Aleksandra M. Naczk, Marcin Gorniak, Jason Pienaar, Edyta Fialkowska, Lukasz Kaczmarek (2026) "Let's Dry up and Survive Together": Is Anhydrobiosis in Two Paramacrobiotus Species (Tardigrada) Associated with a Specific Microbiome Community? International Journal of Molecular Sciences, Vol. 27, Article 5256 (Article in journal) https://dx.doi.org/10.3390/ijms27125256
Marc Braun, Jose M. Peña, Adel Daoud (2026) Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables Proceedings of the Fifth Conference on Causal Learning and Reasoning, p. 861-886 (Conference paper)
Martin Andrae, Erik Wikingsson, So Takao, Tomas Landelius, Fredrik Lindsten (2026) DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants (Conference paper)

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

students seated at a round table

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.
Spot, the robot dog

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

Photo of Fredrik Lindsten

Fredrik Lindsten

Professor, Head of Division

Head of Unit Statistics (STAT)

Photo of Frank Miller

Frank Miller

Professor, Head of Unit

Head of Unit Machine Learning (ML)

Photo of Jose M Pena

Jose M Pena

Senior Associate Pro, Head of Unit

Director of Undergraduate Studies

Photo of Jolanta Pielaszkiewicz

Jolanta Pielaszkiewicz

Associate Professor

Staff at STIMA

All staff

Photo of Johan Alenlöv
Johan Alenlöv
Lecturer
Photo of Martin Andrae
Martin Andrae
PhD student
Photo of Krzysztof Bartoszek
Krzysztof Bartoszek
Senior Associate Pro
Photo of Bayu Brahmantio
Bayu Brahmantio
PhD student
Photo of Marc Braun
Marc Braun
PhD student
Photo of Gabriel Ducrocq
Gabriel Ducrocq
Photo of Hariprasath Govindarajan
Hariprasath Govindarajan
Placeholder for missing image of Anders Grimvall
Anders Grimvall
Photo of Ann-Charlotte Hallberg
Ann-Charlotte Hallberg
Lecturer
Photo of Isak Hietala
Isak Hietala
Lecturer
Photo of Vinit Shambhu Horakeri
Vinit Shambhu Horakeri
PhD student
Photo of Leo Jarhede
Leo Jarhede
PhD student
Photo of Adhithyan Kalaivanan
Adhithyan Kalaivanan
PhD student
Photo of Anushka Kulkarni
Anushka Kulkarni
PhD student
Placeholder for missing image of Tomas Landelius
Tomas Landelius
Adjunct Associate Pr
Photo of Fredrik Lindsten
Fredrik Lindsten
Professor, Head of Division
Photo of Sebastian Mair
Sebastian Mair
Assistant Professor
Photo of Lisa Menacher
Lisa Menacher
PhD student
Photo of Frank Miller
Frank Miller
Professor, Head of Unit
Photo of Shashi Nagarajan
Shashi Nagarajan
Photo of Jose M Pena
Jose M Pena
Senior Associate Pro, Head of Unit
Photo of Jolanta Pielaszkiewicz
Jolanta Pielaszkiewicz
Associate Professor
Photo of Erik Raab
Erik Raab
PhD student
Placeholder for missing image of Behnaz Rivandi
Behnaz Rivandi
PhD student
Photo of Karl Sigfrid
Karl Sigfrid
Postdoc
Photo of Oleg Sysoev
Oleg Sysoev
Senior Associate Pro
Photo of Annika Tillander
Annika Tillander
Associate Professor
Photo of Bertil Wegmann
Bertil Wegmann
Associate Professor
Photo of Erik Wikingsson
Erik Wikingsson
PhD student
Photo of Josef Wilzén
Josef Wilzén
Lecturer
Photo of Linda Wänström
Linda Wänström
Associate Professor, Docent
Photo of Zheng Zhao
Zheng Zhao
Assistant Professor, Docent

News at STIMA

News and major articles

Panel discussion on AI and sustainability.

Researchers call for greater responsibility and sustainability in AI development

AI development must be matched by responsibility, improved governance, and sustainability. That is the message from scientists at Linköping University, who met across disciplines to discuss values, security, environmental impacts and AI politics.

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.

About the department

Image of Building E, Campus Valla.

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.