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


BIOGRAPHY

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. I now live in Canada. 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. I also work on speech-focused projects, including WhisperLive (real-time speech recognition and streaming inference) and WhisperSpeech (open-source speech and TTS research).

I also contribute to bandicoot, a GPU linear algebra library for C++ that targets a balance of speed and ease of use.

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.


Publications

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.
Github
Résumé
  • V. Suryan, A. Boxer, M. Edel. "Super-Resolution Via Mixture-of-Experts", 2025 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2025, pp. 1-4. [bib] [pdf]
  • R.R. Curtin, M. Edel, C. Sanderson. "Bandicoot: A Templated C++ Library for GPU Linear Algebra", accepted to The 26th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2025), 2025. [bib] [pdf] [code]
  • M. Edel. "Large Open Source Models on Embedded Hardware - Tricks we learned while training large open source models", accepted to The Embedded Software Engineering Kongress 2025, Embedded Software Engineering Kongress (ESE 2025), 2025. [pdf]
  • R.R. Curtin, M. Edel, O. Shrit, et al. "mlpack 4: a fast, header-only C++ machine learning library", Journal of Open Source Software, vol. 8, issue 82, 2023. [bib] [pdf] [code]
  • Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson."The ensmallen library for flexible numerical optimization", Journal of Machine Learning Research, Vol. 22, No. 166, 2021. [bib] [pdf] [code]
  • 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]
  • Talks

    Selected talks and invited presentations.
  • Tricks Learned from Training Large Open-Source Models — FOSDEM 2025
  • Super-Resolution on Resource-Constrained Devices — Embedded Vision Summit 2001
  • Embedded Deep Learning Super Resolution on GStreamer using ONNX Inference Runtime — Open Source Summit 2021
  • Living on the Edge: Pure Open Source AI Stack with Panfrost, GStreamer and Tensorflow Lite — Open Source Summit North America 2020