Assistant Professor of Computer Science, Aalto University
My Top Achievements
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.
A. Jung, A.O. Hero, A. Mara, S. Jahromi, A. Heimowitz, Y.C. Eldar. Semi-supervised Learning in Network-Structured Data via Total Variation Minimization IEEE Trans. Sig. Proc, to appear . Preprint
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.
- 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
first.last at aalto.fi