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

Research

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

Well-Rounded Lattices

Mohamed Taoufiq Damir, Alex Karrila, Laia Amoros, Oliver Gnilke, David Karpuk, Camilla Hollanti 2021 IEEE Transactions on Information Theory

Target Tracking on Sensing Surface with Electrical Impedance Tomography

Timo Huuhtanen, Antti Lankinen, Alex Jung 2021 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings

Local Graph Clustering with Network Lasso

Alexander Jung, Yasmin Sarcheshmehpour 2021 IEEE Signal Processing Letters

Predicting power outages caused by extratropical storms

Roope Tervo, Ilona Láng, Alexander Jung, Antti Mäkelä 2021 NATURAL HAZARDS AND EARTH SYSTEM SCIENCES

Images of Galois representations in mod p Hecke algebras

Laia Amorós 2020 INTERNATIONAL JOURNAL OF NUMBER THEORY

Learning Explainable Decision Rules via Maximum Satisfiability

Henrik Cao, Riku Sarlin, Alex Jung 2020 IEEE Access

Anomaly Location Detection with Electrical Impedance Tomography Using Multilayer Perceptrons

Timo Huuhtanen, Alex Jung 2020 Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020

An Information-Theoretic Approach to Personalized Explainable Machine Learning

Alex Jung, Pedro H. J. Nardelli 2020 IEEE Signal Processing Letters

Clustering in Partially Labeled Stochastic Block Models via Total Variation Minimization

Alexander Jung 2020 Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
More information on our research in the Research database.
Research database

People

 Alex Jung

Alex Jung

Assistant Professor, Machine Learning, Big Data

Henrik Ambos

 Laia Amorós Carafí

Laia Amorós Carafí

PhD
 Timo Huuhtanen

Timo Huuhtanen

Doctoral Candidate

Arttu Mäkinen

Roope Tervo

Nguyen Tran

Doctoral Candidate
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