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

Towards Model-Agnostic Federated Learning over Networks

A. Jung, S. Abdurakhmanova, O. Kuznetsova, Y. SarcheshmehPour 2023

Clustered Federated Learning via Generalized Total Variation Minimization

Yasmin SarcheshmehPour, Yu Tian, Linli Zhang, Alexander Jung 2023 IEEE Transactions on Signal Processing

Wind to start the dishwasher? High-Resolution Wind Atlas for Finland

Yu Tian, Xu Yang, Irene Schicker, Alexander Jung 2023

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

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Jinping Sui, Zhen Liu, Li Liu, Alex Jung, Xiang Li 2022 IEEE Transactions on Cybernetics

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)
More information on our research in the Aalto research portal.
Research portal


 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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