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

From intangible to tangible : The role of big data and machine learning in walkability studies

Jun Yang, Pia Fricker, Alexander Jung 2024 Computers, Environment and Urban Systems


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

A perspective on the enabling technologies of explainable AI-based industrial packetized energy management

Daniel Gutierrez-Rojas, Arun Narayanan, Cássia R. Santos Nunes Almeida, Gustavo M. Almeida, Diana Pfau, Yu Tian, Xu Yang, Alex Jung, Pedro H.J. Nardelli 2023 iScience

Towards Model-Agnostic Federated Learning over Networks

A. Jung, S. Abdurakhmanova, O. Kuznetsova, Y. Sarcheshmehpour 2023 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings

Moreau Envelope ADMM for Decentralized Weakly Convex Optimization

Reza Mirzaeifard, Naveen K.D. Venkategowda, Alexander Jung, Stefan Werner 2023 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 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

Explainable empirical risk minimization

Linli Zhang, Georgios Karakasidis, Arina Odnoblyudova, Leyla Dogruel, Yu Tian, Alex Jung 2023 Neural Computing and Applications

Machine Learning

Alex Jung 2022
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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