TUM – Technical University of Munich Menü

Prof. Dr. Jakob Macke

Academic Career and Research Areas

Professor Jakob Macke's (b. 1982) research is focused on understanding the computations that neural networks use to process sensory information and to control intelligent behaviour. He aims to contribute both to a better understanding of the principles of neural signal processing in the brain, as well as to the development of high-performance artificial neural networks. He develops machine learning methods for analyzing high-dimensional data and collaborates closely with experimental groups in the neural and behavioral sciences.

After completing his studies of mathematics at the University of Oxford, Professor Macke worked as a doctoral student at the Max Planck Institute for Biological Cybernetics, Tübingen, as a postdoctoral researcher at the Gatsby Computational Neuroscience Unit, University College London, and as a Bernstein Fellow at the Max Planck Institute in Tübingen. From 2015 onwards he was a Max Planck Group Leader at the Center of Advanced European Study and Research (CAESAR), Bonn, and in October 2017 he took up a professorship at the Centre for Cognitive Science at TU Darmstadt. Professor Macke has been Assistant Professor for Computational Neuroengineering at TUM since May 2018.

    Awards

    • FENS-Kavli Network of Excellence Scholar (2018)
    • Member of the Junge Akademie at the Berlin-Brandenburg Academy of Sciences and Humanities and the German National Academy of Sciences Leopoldina (2013)
    • Otto Hahn Medal, Max Planck Society (2012)
    • Gibbs Prize (proxime accessit), University of Oxford (2005)

    Key Publications

    Lueckmann JM, Goncalves P, Bassetto G, Oecal K, Nonnenmacher M, Macke JH: "Flexible statistical inference for mechanistic models of neural dynamics". Advances in Neural Information Processing Systems 30: 31st Conference on Neural Information Processing Systems. Montreal, Canada. 03.12.2017.

    Abstract

    Speiser A, Ye J, Archer E, Turaga S, Macke JH: "Fast amortized inference of neural activity from calcium imaging data with variational autoencoders". Advances in Neural Information Processing Systems 30: 31st Conference on Neural Information Processing Systems. Montreal, Canada. 03.12.2017.

    Abstract

    Haefner R, Gerwinn S, Macke JH, Bethge M: "Inferring decoding strategies from choice probabilities in the presence of correlated variability". Nature Neuroscience. 2013; 16(2): 235–242.

    Abstract

    Macke JH, Büsing L, Cunningham JP, Yu BM, Shenoy KV, Sahani M: "Empirical models of spiking in neural populations". Advances in Neural Information Processing Systems 24 (NIPS). Red Hook, NY, USA. 2011: 1350-1358.

    Abstract

    Macke JH, Opper M, Bethge M: "Common input explains higher-order correlations and entropy in a simple model of neural population activity". Physical Review Letters. 2011; 106(20): 208102.

    Abstract