Mr Peter Reutemann
Senior Research Programmer (CS)
Qualifications: MSc CS Freiburg
Personal Website: http://www.cms.waikato.ac.nz/~fracpete/
About Peter
Open-source advocate, lead developer of the ADAMS workflow engine, contributor to a wide range of open-source projects and Docker fan. Works mostly on commercial projects, developing and integrating open-source solutions in business processed in various commercial environments. Applies traditional machine learning techniques and ones based on deep learning frameworks (TensorFlow and PyTorch) to solve real-world problems (e.g., processing spectral data and images).
Organizes the Linux and Python meetups in Hamilton.
Research Interests
Some of the research projects Peter has been involved in are:
- ADAMS (Advanced Data mining and Machine learning System)
- MOA (Massive Online Analysis)
- MEKA (multi-label Extension to Weka)
- Weka (workbench for machine learning)
- python-weka-wrapper: using Weka from within Python
- weka-virtualenv: virtual environments for Weka
- UFDL: User-friendly Deep Learning
- TAIAO: Time-Evolving Data Science / AI for Advanced Open Environmental Science
- Overview of Github projects
Recent Publications
Read, J., Reutemann, P., Pfahringer, B., & Holmes, G. (2016). MEKA: A multi-label/multi-target extension to WEKA. Journal of Machine Learning Research, 17(21), 1-5. Open Access version: https://hdl.handle.net/10289/10136
Reutemann, P., & Holmes, G. (2015). Big data with ADAMS. In 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications Vol. 41 (pp. 5-8). Conference held Sydney, Australia: JMLR Workshop and Conference Proceedings. Retrieved from http://jmlr.org/proceedings/papers/v41/reutemann15.html
Hill, M. G., Connolly, P. G., Reutemann, P., & Fletcher, D. (2014). The use of data mining to assist crop protection decisions on kiwifruit in New Zealand. Computers and Electronics in Agriculture, 108, 250-257. doi:10.1016/j.compag.2014.08.011
Reutemann, P., & Vanschoren, J. (2012). Scientific Workflow Management with ADAMS. In P. Flach, T. De Bie, & N. Cristianini (Eds.), Proceedings of the Machine Learning and Knowledge Discovery in Databases (ECML-PKDD) (pp. 833-837). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33486-3_58
Find more research publications by Peter Reutemann
Contact Details
Email: [email protected]Room: G.2.19
Phone: +64 7 858 5174