Department of Computer Science

Machine Learning for Big Data

Our research revolves around machine learning models and methods for big data over networks.
Department of Computer Science research, infrared lights hanging on the roof for machine learning in plant growing project


Our research revolves around machine learning models and methods for big data over networks. The data arising in many important big data applications, ranging from social networks to network medicine, consist of high-dimensional data points related by an intrinsic (complex) network structure. In order to jointly leverage the information conveyed in the network structure as well as the statistical power contained in high-dimensional data points, we study networked exponential families. For the accurate learning of such networked exponential families, we borrow statistical strength, via the intrinsic network structure, across the dataset. A powerful algorithmic toolbox for designing learning algorithms is provided by convex optimization methods. Modern convex optimization methods are appealing for big data applications as they can be implemented as highly scalable message passing protocols.

Latest publications


Moloud Abdar, Mohammad Amin Fahami, Leonardo Rundo, Petia Radeva, Alejandro Frangi, U. Rajendra Acharya, Abbas Khosravi, H. K. Lam, Alexander Jung, Saeid Nahavandi 2023 IEEE Transactions on Industrial Informatics

Wind to start the washing machine? High-Resolution Wind Atlas for Finland

X. Yang, Y. Tian, I. Schicker, A. Jung 2023

Machine Learning

Alex Jung 2022

From Intuition to Reasoning

Jun Yang, Pia Fricker, Alexander Jung 2022 Journal of Digital Landscape Architecture

Images of Galois representations in mod p Hecke algebras


Learning optimal lattice codes for MIMO communications

Laia Amorós, Mikko Pitkänen 2021 ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Hash-Based Preprocessing and Inprocessing Techniques in SAT Solvers

Henrik Cao 2021 Theory and Applications of Satisfiability Testing – SAT 2021 - 24th International Conference, 2021, Proceedings

Containing Future Epidemics With Trustworthy Federated Systems for Ubiquitous Warning and Response

Dick Carrillo, Duc Lam Nguyen, Pedro Henrique Juliano Nardelli, Evangelos Pournaras, Plinio Morita, Demóstenes Z. Rodriguez, Merim Dzaferagic, Harun Siljak, Alex Jung, Laurent Hébert-Dufresne, Irene Macaluso, Mehar Ullah, Gustavo Fraidenraich, Petar Popovski 2021 Frontiers in Communications and Networks

Well-Rounded Lattices

Mohamed Taoufiq Damir, Alex Karrila, Laia Amoros, Oliver Gnilke, David Karpuk, Camilla Hollanti 2021 IEEE Transactions on Information Theory
More information on our research in the Research database.
Research database


 Alex Jung

Alex Jung

Assistant Professor, Machine Learning, Big Data

Henrik Ambos

 Laia Amorós Carafí

Laia Amorós Carafí

 Timo Huuhtanen

Timo Huuhtanen

Doctoral Candidate

Arttu Mäkinen

Roope Tervo

Nguyen Tran

Doctoral Candidate
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