Alexander Jung

Assistant Professor of Computer Science, Aalto University

Contact information My research group Publications Teaching Short CV

The overarching goal of our research is to develop a theory that allows to tell if (and how) machine learning is possible given the available resources (data, computation, privacy,...). To this end, we try to identify easily measurable quantities (such as temperature in physics) which allow to predict the behaviour of highly complex machine learning systems (e.g. a deep neural network with billions of tuneable weights which is applied to massive unstructured data). We draw inspiration from the accomplishments in physics where a single number (the temperature) can tell a lot about the global behaviour of incredibly complex systems such as a lake which is constituted by billions of water molecules.

Fast Facts

Selected Publications

H. Ambos, N. Tran, A. Jung The Logistic Network Lasso, May 2018. preprint can be found here
A. Jung. On the Complexity of Sparse Label Propagation. Frontiers in Appl. Math. and Stat., Jun. 2018. 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 A Fixed-Point of View on Gradient Methods for Big Data. Frontiers in Appl. Math. and Stat., Sept. 2017. Preprint
N. Tran 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, 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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