DataScienceGroup @ DSV


The Data Science Research Group at the Department of Computer and Systems Sciences (DSV) of Stockholm University focuses on core data science resarch as well as on application areas where data science can assist in providing useful insights for decision making. The group focuses on the formulation of novel data science problems and the development of algorithmic methods and methodological workflows for achieving scalable solutions in core application areas within social sciences and humanities.

Research Profile

The group has three main focus research themes: sequential and temporal mining, explainable and federated learning, and machine learning applications. For a comprehensive list of our research topics please visit our research page.

Sequential & Temporal Mining
We focus on developing methods for searching and mining rich and complex data sources, with emphasis on sequential and temporal data, as well as text. In particular, we are interested in defining temporal abstractions and extracting high-utility features for clustering or classification of sequential and temporal data sources, such as univariate and multivariate time series, event sequences, and text.
We give particular emphasis on methods and workflows for explainable machine learning. We explore both model agnostic and model specific solutions, as well as counterfactual explanation formulations for sequential data variables, images, and text. Our main goal is to provide scalable and distributed solutions for maintaining good trade-offs between predictive performance and explainability. Moreover, we are interested in solutions that can function in a distributed manner without the need for data exchange.
Explainable & Federated Learning
Machine Learning Applications
Our methods and solutions are motivated by real-world applications and use cases. The group has particular expertise in mining and model understanding from healthcare and medical data sources. In addition, we have established a strong expertise in predictive maintenance and integrated vehicle management. Finally, we are interested in financial data, environmental data, as well as data emerging from immersive technologies, such as virtual reality.


  • April 14, 2023
    [Predoc Seminar of Zed Lee] Our PhD Student Zed Lee will have his predoc seminar on “Z-Series: Mining and learning from complex sequential data” on the 21st of April 2023 at 10:00. The predoc seminar consists of a presentation by the doctoral student and the opposition from professors Elisa Fromont and Hercules Dalianis.
  • October 28, 2022
    [Half-Time Seminar of Alejandro Kuratomi] Our PhD Student Alejandro Kuratomi will have his half-time seminar on “Improving Machine Learning Interpretability - Algorithms and Applications” on the 9th of November 2022 at 10:00. The half-time seminar consists of a presentation by the doctoral student and a question and answers session involving the half-time committee, Eirini Ntoutsi and Rahim Rahmani.
  • October 04, 2022
    [PhD Defense of Jonathan Rebane] Jonathan Rebane will have his PhD thesis defense on the 28th of October 2022 at 13:00. His thesis is entitled “Learning from Complex Medical Data Sources”. The main opponent will be Myra Spiliopoulou, and his examiners will be Arno Knobbe, Stanley Greenstein, Indre Zliobaite, and Hercules Dalianis (suppleant).
  • September 15, 2022
    [Half-Time Seminar of Zhendong Wang] Our PhD Student Zhendong Wang will have his half-time seminar on “Counterfactual Explanations for Complex and Temporally Ordered Data” on the 6th of October 2022 at 13:00. The half-time seminar consists of a presentation by the doctoral student and a question and answers session involving the half-time committee, Allan Tucker, Pedro Pereira Rodrigues, and Rahim Rahmani.

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