Welcome!
The Data Science Research Group at the Department of Computer and Systems Sciences (DSV) at Stockholm University focuses on core data science research 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.
News
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December 16, 2024
[Opening: Associate Senior Lecturer in data science] The Data Science group is looking for up to three associate senior lecturers in the area of data science. This is a four-year tenure track position with the intention to start no later than August 2025. Deadline: 16 December 2024.
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December 04, 2024
[PhD Defense of Zhendong Wang] Zhendong Wang will have his PhD thesis defense on the 4th of December 2024 at 09:00. His thesis is entitled “Constrained Counterfactual Explanations for Temporal Data”. The main opponent will be Mykola Pechenizkiy, and his examiners will be Aron Henriksson, Pedro Rodrigues, Tatiana Kouznetsova, and Peter Wahlgren (suppleant). Link to thesis
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November 14, 2024
[PhD Defense of Alejandro Kuratomi Hernández] Alejandro Kuratomi Hernández will have his PhD thesis defense on the 14th of November 2024 at 09:00. His thesis is entitled “Orange Juice - Enhancing Machine Learning Interpretability”. The main opponent will be Toon Calders, and his examiners will be Eirini Ntoutsi, Ulf Johansson, Fredrik Eng Larsson, and Rahim Rahmani (suppleant). Link to thesis
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September 06, 2024
[PhD Defense of Maria Bampa] Maria Bampa will have her PhD thesis defense on the 6th of Semptember 2024 at 09:00. Her thesis is entitled “Data-Driven AI for Patient and Public Health”. The main opponent will be Myra Spiliopoulou, and her examiners will be Vana Kalogeraki, Allan Tucker, Stephan Hau, and Rahim Rahmani (suppleant). Link to thesis
Collaborators
