Alexander Jung

alt text 
  • Dipl.-Ing. Dr. techn. ("sub auspiciis"), Assistant Professor,

  • Associate Editor for IEEE Signal Processing Letters (website)

  • Chapter Chair IEEE Finland Jt. Chapter SP-CAS (website)

  • Member of EURASIP-TAC ‘‘Signal and Data Analytics for Machine Learning’’ (website)

  • Board Member Finnish Union of University Researchers and Teachers (FUURT) (website)

  • Reviewer for top-tier Journals and Conferences in Machine Learning and Signal Processing

    Follow me on Linkedin, Twitter, YouTube or GitHub
    E-post: alex.jung [@] aalto [DOT] fi

About me

I have received a Dipl.-Ing. (MSc) and Dr.techn. (Phd) degree in electrical engineering and signal processing from TU Vienna in 2008 and 2012, respectively. Since 2015, I am an Assistant Professor for Machine Learning at the Department of Computer Science of Aalto University. I am leading the research group Machine Learning for Big Data which is researching and teaching the mathematical foundations of machine learning.

Research Highlight: Federated Multitask Learning from Big Data over Networks

Many important application domains generate collections of local datasets that are related by an intrinsic network structure (“big data over networks”). A timely application domain that generates such big data over networks is the management of pandemics. Individuals generate local datasets via their smartphones and wearables that measure biophysical parameters. The statistical properties local datasets are related via different network structures that reflect physical (“contact networks”), social or biological proximity. While the entire collection of local datasets does not conform to an i.i.d. assumption, we might still be able to approximate the local datasets in each cluster as i.i.d..

We have recently proposed networked exponential families as a novel probabilistic model for big data over networks. Networked exponential families allow to jointly capitalize on the information in local datasets and their network structure, Networked exponential families are appealing statistically and computationally. They allow to adaptively pool local datasets with similar statistical properties as training sets to learn personalized predictions tailored to each local dataset. We can compute these personalized predictions using highly scalable distributed convex optimization methods. These methods are robust against various types of failures and do not require the exchange of potentially sensitive raw data.

Relevant Publications:

  • Y. Sarcheshmehpour, M Leinonen and A. Jung, “Federated Learning From Big Data Over Networks”, accepted for presentation at IEEE ICASSP, 2021. preprint: https:arxiv.orgabs2010.14159

  • A. Jung, “Networked Exponential Families for Big Data Over Networks,” in IEEE Access, vol. 8, pp. 202897-202909, 2020, doi: 10.1109/ACCESS.2020.3033817.

  • A. Jung and N. Tran, “Localized Linear Regression in Networked Data,” in IEEE Signal Processing Letters, vol. 26, no. 7, pp. 1090-1094, July 2019, doi: 10.1109/LSP.2019.2918933.

  • N. Tran, O. Abramenko and A. Jung, “On the Sample Complexity of Graphical Model Selection From Non-Stationary Samples,” in IEEE Transactions on Signal Processing, vol. 68, pp. 17-32, 2020, doi: 10.1109/TSP.2019.2956687.

Research Highlight: Personalized Explainable Machine Learning

A key challenge for the widespread use of machine learning methods is the explainability of their predictions. We have recently developed a novel approach to constructing personalized explanations for the predictions delivered by machine learning method. We measure the effect of an explanation by the reduction in the conditional entropy of the prediction given the summary that a particular user associates with data points. The user summary is used to characterise the user's background knowledge and, in turn, to compute tailored personalized explanations.

Relevant Publications:

  • A. Jung, “Explainable Empirical Risk Minimization”, arXiv eprint, 2020. weblink

  • A. Jung and P. H. J. Nardelli, “An Information-Theoretic Approach to Personalized Explainable Machine Learning,” in IEEE Signal Processing Letters, vol. 27, pp. 825-829, 2020, doi: 10.1109/LSP.2020.2993176.

Teaching Highlight: Student Feedback-Driven Course Development

Right from my start at Aalto in 2015, I took care of the main machine learning courses at Aalto University. Within three years I have re-designed the spearhead course Machine Learning: Basic Principles (MLBP). This re-design was based on a careful analysis of feedback received from several thousands of students. I have also started to prepare response letters to student feedback, as it is customary in the review process of scientific journals. My final edition of MLBP in 2018 has achieved the best student rating since the course was established at Aalto. The efforts have also been acknowledged by the Teacher of the Year award, which I have received from the Department of Computer Science in 2018.

Teaching Highlight: A Three-Component Picture of Machine Learning

Machine learning methods have been and are currently popularized in virtually any field of science and technology. As a result, machine learning courses attract students from different study programs. Thus, a key challenge in teaching basic machine learning courses is the heterogeneity of student backgrounds. To cope with this challenge, I have developed a new teaching concept for machine learning. This teaching concept revolves around three main components of machine learning: data, models and loss functions. By decomposing every machine learning methods into specific design choices for data representation, model and loss function, students learn to navigate the vast landscape of machine learning methods and applications. The three-component picture of machine learning is the main subject of my textbook “Machine Learning: The Basics”.