Alexander Jung

Assistant Professor of Computer Science, Aalto University

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My current 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.

This article about ``The A.I. Diet'' features food networks.

Fast Facts

Selected Publications

A. Jung. Learning Networked Exponential Families with Network Lasso, May 2019. preprint can be found here
A. Jung and N. Tran. Localized Linear Regression in Networked Data. IEEE Sig. Proc. Letters , 2019. Preprint
A. Jung and N. Vesselinova. Analysis of Network Lasso for Semi-Supervised Regression. Proceedings of Machine Learning Research (PMLR) 89:380-387, Apr. 2019. click here
A. Jung, N. Tran and A. Mara. When is Network Lasso Accurate?. Frontiers in Appl. Math. and Stat., Jan. 2018. click here
A. Jung, Y. C. Eldar, and N. Goertz. On the Minimax Risk of Dictionary Learning. IEEE Trans. Inf. Theory, vol. 62, no. 3, March 2016. Preprint: arXiv:1507.05498 [stat.ML]
A. Jung. Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach. IEEE Trans. Sig. Proc., vol 63, no. 21, Nov. 2015. Preprint
A. Jung, G. Tauboeck and F. Hlawatsch. Compressive Spectral Estimation for Nonstationary Random Processes. IEEE Trans. Inf. Theory, vol. 59, no. 5, May 2013. Preprint

Recent Talks and Lectures

Clustering within Aalto course CS-E3210 Machine Learning: Basic Principles, Oct. 2018. youtube video
Model Validation, Selection and Regularization within Aalto course CS-E3210 Machine Learning: Basic Principles, Sept. 2018. youtube video
Linear Regression and Gradient Descent within Aalto course CS-E3210 Machine Learning: Basic Principles, Sept. 2018. youtube video
Artificial Intelligence is the New Electricity at Turku-AI meetup, Sept. 2018. youtube video
Compressed Sensing of Big Data Networks at EPFL, Lausanne, Dec. 2017.
Solving the Food Waste Challenge: An Artificial Intelligence perspective at Slush side event ``The Future is BioDigital: The Packaging Valley'', Helsinki, Nov. 2017.

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