Alexander Jung

Assistant Professor of Computer Science, Aalto University

Contact information My research group Publications Teaching Short CV

"There is Nothing More Practical Than A Good Theory" (Kurt Lewin)

Compressed Sensing over Complex Networks for (Semi-Supervised) Learning from Big Data (CSLeBiD)

My current research revolves around applying the theory and algorithmics of compressed sensing to massive datasets with intrinsic network structure, i.e., to big data over networks. In particular, I am interested in the relations between concepts of the theory of complex networks (e.g., clustering, percolation) and the theory of sparse graph signals defined over such complex networks. The combination of compressed sensing with modern clustering algorithms is expected to be game changing for (semi-supervised) learning from big data over networks in a similar manner as compressed sensing was for digital signal processing.

Complex Networks and Systems
I am co-organizing the complex systems and networks seminar of the Department of Computer Science at Aalto University.

News

Selected Publications

A. Jung, A. O. Hero III, A. Mara and S. Jahromi Semi-Supervised Learning via Sparse Label Propagation. submitted to a journal, May 2017. Preprint
A. Jung. The Network Nullspace Property for Compressed Sensing of Big Data Networks. submitted to a journal , May 2017. Preprint
A. Jung. When is Network Lasso Accurate?. submitted to a journal , April 2017. Preprint
M. Hinkka, T. Lehto, K. Heljanko and A. Jung. Structural Feature Selection from Event Logs. submitted , Mar. 2017.
S. Jahromi and A. Jung. Random Walk Sampling for Big Data over Networks. accepted at Sampta 2017 , Jun. 2017. Preprint
N. Tran Quang and A. Jung. On the Sample Complexity of Graphical Model Selection for Non-Stationary Processes. submitted , Jan. 2017. Preprint
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, S. Schmutzhard, F. Hlawatsch, Z. Ben-Haim and Y. C. Eldar. Minimum Variance Estimation of a Sparse Vector Within the Linear Gaussian Model: An RKHS Approach. IEEE Trans. Inf. Theory, vol. 60, no.10, Oct. 2014. Preprint
A. Jung, S. Schmutzhard and F. Hlawatsch. The RKHS Approach to Minimum Variance Estimation Revisited: Variance Bounds, Sufficient Statistics, and Exponential Families. IEEE Trans. Inf. Theory, vol. 60, no. 7, Jul. 2014. 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
A. Jung, Z. Ben-Haim, F. Hlawatsch and Y. C. Eldar. Unbiased Estimation of a Sparse Vector in White Gaussian Noise. IEEE Trans. Inf. Theory, vol. 57, no. 12, Dec. 2011. Preprint

Selected Talks

Backpropagation at the Artificial Intelligence Laboratory, Vrije Universiteit Brussel, June 2017.
Machine Learning for Big Data over Networks: When is Network Lasso Accurate? in complex systems and networks seminar at Dept. of Computer Science, Aalto University, April 2017.

When is Network Lasso Accurate?

Signal Processing and Speech Communication Laboratory (SPSC Lab)

When is Network Lasso Accurate? in group seminar of Prof. Thomas Pock at TU Graz, April 2017.
Compressed Sensing for Learning from Big Data over Networks at International Institute for Applied Systems Analysis (IIASA) Austria, March 2017.
Compressed Sensing for Learning from Big Data over Networks at Johannes Kepler University (JKU) Linz, Feb. 2017.
Compressed Sensing for Learning from Big Data over Networks . within the Large Structures Seminar of Aalto University, Feb. 2017.
Compressed Sensing for Semi-Supervised Learning from Big Data over Networks. within the Machine Learning Coffee Seminars of Aalto University and the University of Helsinki, Feb. 2017.
Compressed Sensing for Big Data over Networks. in group seminar of Prof. Sjoerd Dirksen and Prof. Holger Rauhut at RWTH Aachen, Jan. 2017.
Graphical Model Selection for Big Data over Networks. within the Stochastic Sauna at Aalto University, Dec. 2016.
Graph Signal Recovery using Convex Optimization. within the Helsinki Algorithms Seminar of Aalto University and the University of Helsinki, May 2016.
An Information-Theoretic Approach to Dictionary Learning. TU Berlin, June 2015.
On the Sample-Complexity of Dictionary Learning. within the Mathematics Colloquium of the University of Innsbruck, June 2015.
Performance Limits of Dictionary Learning for Sparse Coding: An Information-Theoretic Approach. University of Michigan, June 2014.
Compressive Nonparametric Graphical Model Selection for Time Series: A Multitask Learning Approach. The University of Edinburgh, Oct. 2013.

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