kurg.org is the eponymous online home of Marcus Edel—developer, maintainer, computer science student, and open source enthusiast.


Helping people answer questions using data they can't see—AI, stats, data science, computation.

I was born and raised in the heart of Germany and grew up a very outgoing and active child. Always staying proactive. I studied Computer Science and Physics at the Free University of Berlin.

In my research, one important thing I do is develop the C++ machine learning library mlpack, which contains a growing collection of high-quality implementations of cutting-edge machine learning algorithms and is in use by both researchers and industry groups worldwide. I also work on ensmallen—a flexible library for efficient mathematical optimization, which provides a simple set of abstractions for writing an objective function to optimize. It also provides a large set of standard and cutting-edge optimizers that can be used for virtually any mathematical optimization task.

In short, my research interests include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization and (deep/recurrent) neural networks. I have used these techniques in application domains ranging from computer vision and neural decoding to localization and robotics.


Here is a list of publications, listed in reverse order of publication. I also have a Google Scholar profile although the information there may not always be up-to-date.
  • M. Edel, E. Köppe."Deep Neural Networks for Multimodal Wearable Activity Recognition", in preparation.
  • S. Bhardwaj, R. R. Curtin, M. Edel, Y. Mentekidis, C. Sanderson. "ensmallen: a flexible C++ library for efficient function optimization", in NeurIPS 2018 Workshop on Systems for ML, 2018. [bib] [pdf] [code]
  • M. Edel. "mlpack open-source machine learning library and community", in NeurIPS 2018 Workshop on Machine Learning Open Source Software, 2018. [bib] [pdf] [code]
  • R.R. Curtin, M. Edel, M. Lozhnikov, Y. Mentekidis, S. Ghaisas, S. Zhang. "mlpack: a fast, flexible machine learning library", The Journal of Open Source Software, vol. 3, issue 26, pp. [bib] [pdf] [code]
  • R. R. Curtin, S. Bhardwaj, M. Edel, Y. Mentekidis. "A generic and fast C++ optimization framework", arXiv preprint arXiv:1711.06581, 2017. [bib] [pdf]
  • R.R. Curtin, M. Edel. "Designing and building the mlpack open-source machine learning library", submitted to The Fourth International Conference of PUST (ICOPUST), 2017. [pdf] [code]
  • M. Edel, J. Lausch. "Capacity Visual Attention Networks", in Second Global Conference on Artificial Intelligence (GCAI), Proceeedings (Christoph Benzmüller Raul Rojas, Geoff Sutcliffe, eds.), EasyChair, EPiC Series in Computing, 2016. [bib] [pdf]
  • M. Edel, E. Köppe. "Binarized-BLSTM-RNN based Human Activity Recognition", in Proceedings of the 2016 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2016. [bib] [pdf]
  • M. Edel, E. Köppe. "An Advanced Method for Pedestrian Dead Reckoning using BLSTM-RNNs", in Proceedings of the 2015 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015. [bib] [pdf]
  • M. Edel, A. Soni, R.R. Curtin. "An automatic benchmarking system", in NIPS 2014 Workshop on Software Engineering for Machine Learning, 2014. [bib] [pdf] [code]
  • Demos

    In order to make my research as reproducible as possible, most of the algorithms and benchmarks I use are available open-source through the mlpack machine learning library. Here is a list of demos, that entirely run in your browser. No software requirements, no compilers, no installations, no GPUs.
  • Deep Learning Optimizer Visualization [demo] [code]
  • Classify MNIST digits with a Convolutional Neural Network [demo] [code]
  • Classify MNIST digits with the Recurrent Model of Visual Attention [demo] [code]
  • An automatic benchmarking system [interface] [code]