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WASP Machine learning

Machine learning
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The Division of Statistics and Machine Learning develops the machine learning methods that autonomous systems use when learning from data. Our research and education focuses on probabilistic modeling and efficient algorithms for learning, prediction and decision making under uncertainty.

The researchers at The Division of Statistics and Machine Learning, STIMA, are active in both basic machine learning methodology as well as in applied industrial research in areas such as transport systems, telecommunication, robotics, autonomous vehicles, medical technology, climate modeling, and software development.

The research at the division has resulted in efficient simulation-based inference algorithms for large scale data, new graphical models for describing structure and dependencies in complex data, methods for analyzing causal relationships, and other contributions to the machine learning area.

Contact

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Fredrik Lindsten

Senior Associate Professor, Head of Division, Head of Unit

Research

WASP machine learning

Telecommunications

Machine learning for 5G System Control and Automation

There is a plethora of new applications envisioned for 5G, ranging from traditional efficient broadband communication links to more strictly reliable communication links or massive communication that is typically associated with Internet of Things.
Transportation

Bayesian Learning for Spatiotemporal Processes in Transportation

The rapid deployment of streaming sensors have made spatiotemporal data increasingly common. In this project we develop probabilistic models for spatiotemporal data with applications in the field of transportation.
Probabilistic models

Probabilistic models and deep learning – bridging the gap

Probabilistic models and deep learning are two successful branches of machine learning. In this project, we will develop theory and methods related to the interplay between these technologies, enabling us to take advantage of the strengths of both.

Related research

WASP at LiU

WASP - Wallenberg AI, Autonomous Systems and Software Program

The fourth industrial revolution is upon us, as automation becomes autonomy. LiU conducts outstanding research in several of the fields that are central to the Wallenberg AI Autonomous Systems and Software Program, WASP.
A pair of hands holding a dice.

The Division of Statistics and Machine Learning (STIMA)

The STIMA division is part of the Department of Computer and Information Science. The research and teaching activities are focused on modern data analysis and the division hosts the international master's programme in Statistics and Machine Learning.

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