Machine learning (ML) and artificial inteligence (AI) models and applications. Modeling systems of stochastic microscopic and macroscopic interactants at different temporal and spatial scales. Examples include traffic flow models, micromagnetics, climate systems under non-Gaussian noise assumptions. Forecasting and predictions with Tranformers and attention methods. Description of noise and Anomaly Detection methods are used to analyze signals but also in generating and learning data with methods such as Stable Difussion and Dinoising Diffusion Probabilistic Models (DDPM). Classical work has also been performed into analysis and generation of synthetic data for systems via machine learning methods such as generative GANs and VAEs or Natural Language Processing (NLP) and Large Language Model (LLM) based methods. Graph Neural Networks are also explored within applications ranging from traffic networks to agriculture and harvest forecasting.

Currently hold three patents, two of them with Eriksson on 5G and 6G mobility devices based on handling beam managment measurements and one patent with Ximantis on traffic network forecasting.