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 on mining and model understanding from healthcare and medical data sources. In addition, we have established strong expertise on predictive maintanance and integrated vehicle management. Finally, we are interested in financial data, enironmental data, as well as data emerging from immersve technologies, such as virtual reality.


  • 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.
  • August 18, 2022
    [Medical Artificial Intelligence PhD Forum 2022 @ Porto: Sep 5-6 2022] The Data Science group is participating in the Medical AI PhD Forum in Porto, where our PhD students will present their ongoing research and discuss potential cross-group collaboration and research visits. The PhD Forum is a joint initiative between Stockholm University, the University of Magdeburg, Brunel University, and the University of Porto.
  • March 07, 2022
    [CBMS Special Track - Federated Learning for Medical Data] Members of the Data Science group are organizing the CBMS Special Track on Federated Learning for Medical Data that will take place on 21-23 July 2022 in Shenzhen, China, and online! Paper submission deadline: 25 April 2022. More information is available here.
  • November 01, 2021
    [DSV and Spotify in scientific collaboration] Three industrial doctoral students from Spotify study the future of machine learning together with researchers at the Department for Computer and Systems Sciences at Stockholm University. More information is available here.

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