Data Science and Intelligent Decision Making

Data Science addresses the acquisition, storage, and analysis of observed complex data, towards detecting patterns and making predictions. Intelligent Decision Making addresses the modelling of constrained decision-making problems, towards running a model for input data on an off-the-shelf solver. In this programme, we develop e-science methods to support the whole pipeline: from data to decisions.

From Databases to Data Science and Intelligent Decision Making

The eSSENCE programme at the Computing Science Division, Department of Information Technology, started under the leadership of Prof. Tore Risch as an eSSENCE Cornerstone Technology unit. It initially focused on scalable processing of high-level queries over high-volume data streams. This research, conducted at the Uppsala Database Laboratory, led to the development of technology now used by spin-off company StreamAnalyze.

In 2017, we expanded our activities into the broader area of Data Science, while continuing our foundational research on database systems. The newly established Uppsala Information Laboratory joined the programme, and in 2022, we proposed our vision to strengthen and expand activities on e-Science methods for the social sciences, one of the eSSENCE key application areas. Examples of data sources for which we are developing e-Science methods include social-media platforms (used for example to study how information and disinformation spread online) and statistical population registers (used to monitor segregation and other nation-wide social structures).

During this period, eSSENCE researchers from our programme also led the establishment of a new International Master's Programme in Data Science at Uppsala University. This initiative has been essential for advancing our research and education in e-Science methods, and it is already bearing fruit, with some early graduates having joined our department and programme as PhD students.

In 2024, we further expanded our scope with the objective of supporting the whole pipeline: from data collection and processing to analysis and decision-making. The Optimisation Group, also based at the Computing Science Division, joined the eSSENCE programme, contributing through the development of optimisation methods and supporting new application areas, including vehicle routing and data-driven drug development.

Recent events

Computational Social Science Spring Seminar Series, March–May 2025, Uppsala.

CPAIOR 2024, the 21st International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research. 28–31 May 2024, Uppsala.

First National Conference on Computational Social Science, 23–25 April 2024, Uppsala.

Research staff

Current

Professors

Pierre Flener
Matteo Magnani
Di Yuan

Associate professors

Georgios Fakas
Justin Pearson

Assistant professors

Ece Calikus
María Andreína Francisco Rodríguez
Davide Vega

Postdoctoral researchers

Ramiz Gindullin

PhD students

Diletta Goglia
Georgios Panayiotou
Matias Piqueras
Xin Shen
Inga Wohlert
Frej Knutar Lewander

Past

Tore Risch (Professor)
Kjell Orsborn (Associate Professor)
Silvia Stefanova (Postdoctoral researcher)
Georgios Kalamatianos (PhD, defended: 2022)
Amin Kaveh (PhD, defended: 2022)
Khalid Mahmood (PhD, defended: 2021)
Andrej Andrejev (PhD, defended: 2016)
Minpeng Zhu (PhD, defended: 2016)
Thanh Truong (PhD, defended: 2016)
Cheng Xu (PhD, defended: 2016)

Recent eSSENCE publications

Xin Shen, Matteo Magnani, Christian Rohner, Fiona Skerman. On the accurate computation of expected modularity in probabilistic networks. Scientific Reports, forthcoming.

Katrin Uba, Alexandra Segerberg, Matteo Magnani. Climate Clash: A Multimodal Analysis of Movement - Countermovement Interactions on Digital Sphere. American Behavioral Scientist, forthcoming.

Georgios Panayiotou, Matteo Magnani, Ece Calikus. Towards intersectional fairness in community detection. In: DETOX workshop @ ICWSM. 2025. Forthcoming.

Valeria Policastro, Matteo Magnani, Claudia Angelini, Annamaria Carissimo. INet for Network Integration. Computational Statistics, Vol. 40. 2025. https://doi.org/10.1007/s00180-024-01536-8

Alexandra Segerberg, Matteo Magnani. Visual digital intermediaries and global climate communication: is climate change still a distant problem on YouTube? Plos ONE, Vol. 20(4). 2025. https://doi.org/10.1371/journal.pone.0318338

Nora Al-Naami, Nicolas Médoc, Matteo Magnani, Mohammad Ghoniem. Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts. IEEE Transactions on Visualization and Computer Graphics, Vol. 31. 2024. https://doi.ieeecomputersociety.org/10.1109/TVCG.2024.3456167

Diletta Goglia, Davide Vega. Structure and dynamics of growing networks of Reddit threads. Applied Network Science, 9:48, 1–23. 2024. https://doi.org/10.1007/s41109-024-00654-y

Luca Rossi, Alexandra Segerberg, Luigi Arminio, Matteo Magnani. Do You See What I See? Emotional Reaction to Visual Content in the Online Debate About Climate Change. Environmental Communication, 1–19. 2024. https://doi.org/10.1080/17524032.2024.2420787

Georgios Panayiotou, Matteo Magnani, Bruno Pinaud. Current challenges in multilayer network engineering. Applied Network Science, 9:1, 1–23. 2024. https://doi.org/10.1007/s41109-024-00686-4

Georgios Panayiotou and Matteo Magnani. Fair-mod: Fair Modular Community Detection. International Conference on Complex Networks. 2024. https://doi.org/10.1007/978-3-031-82435-7_8

Viktoria Yantseva, Davide Vega, Matteo Magnani. Immigrant-critical alternative media in online conversations. Plos one, 18(11), e0294636. 2023. https://doi.org/10.1371/journal.pone.0294636

Georgios John Fakas, Georgios Kalamatianos. Proportionality on Spatial Data with Context. ACM Trans. Database Syst., 48(2), 4:1–4:37. 2023. https://doi.org/10.1145/3588434

Patrik Seiron, Axel Lindegren, Matteo Magnani, Christian Rohner, Tsuyoshi Murata, Petter Holme. Modularity-based selection of the number of slices in temporal network clustering. In: Temporal Network Theory, 435-447. 2023. https://doi.org/10.1007/978-3-031-30399-9_21

Davide Vega, Matteo Magnani. Metrics for temporal text networks. In: Temporal Network Theory, 149-164. 2023. https://doi.org/10.1007/978-3-031-30399-9_8