Martin Gauch

PhD Student, Johannes Kepler University, Linz, Austria

Email: gauch@ml.jku.at

Hi, I’m Martin. I’m a PhD student in computer science at the Institute for Machine Learning at Johannes Kepler University, Linz, Austria, in the team of Sepp Hochreiter. Previously, I was a research associate and a graduate computer science student at the University of Waterloo, Canada and a graduate and undergraduate student at Karlsruhe Institute of Technology, Germany.

My research is at the intersection of machine learning and hydrology. We focus on developing data-driven deep learning techniques for streamflow prediction. Visit our research blog at neuralhydrology.github.io for more information on current and recent projects.

Publications

2021

Multi-Timescale LSTM for Rainfall–Runoff Forecasting
Martin Gauch, Frederik Kratzert, Grey Nearing, Jimmy Lin, Sepp Hochreiter, Johannes Brandstetter, and Daniel Klotz.
EGU General Assembly, April 2021, virtual event.

Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network
Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, and Sepp Hochreiter.
Hydrology and Earth System Sciences, April 2021 (preprint published October 2020).

The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction
(open-access version published on arXiv, November 2019)
Martin Gauch, Juliane Mai, and Jimmy Lin.
Environmental Modelling & Software, Volume 135, 2021, 104926, ISSN 1364-8152.

2020

A Machine Learner’s Guide to Streamflow Prediction (revised version of June 2020 arXiv paper)
Martin Gauch, Daniel Klotz, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Jimmy Lin.
NeurIPS 2020 Workshop on AI for Earth Sciences, December 2020.

A Data Scientist’s Guide to Streamflow Prediction
Martin Gauch and Jimmy Lin.
arXiv:2006.12975, June 2020.

An Open-Source Interface to the Canadian Surface Prediction Archive
Martin Gauch, James Bai, Juliane Mai, and Jimmy Lin.
Proceedings of the 20th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL 2020), August 2020, virtual event.

Machine Learning for Streamflow Prediction
Martin Gauch.
Master’s Thesis, April 2020, Waterloo, Ontario, Canada.

2019

Streamflow Prediction with Limited Spatially-Distributed Input Data
Martin Gauch, Shervan Gharari, Juliane Mai, and Jimmy Lin.
Proceedings of the NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning, December 2019, Vancouver, British Columbia, Canada.

The Runoff Model-Intercomparison Project Over Lake Erie and the Great Lakes
Juliane Mai, Bryan Tolson, Hongren Shen, Etienne Gaborit, Vincent Fortin, Milena Dimitrijevic, Nicolas Gasset, Dorothy Durnford, Young Lan Shin, Tricia Anne Stadnyk, Oyémonbadé Hervé Rodrigue Awoye, Lauren M. Fry, Emily A. Bradley, Tim Hunter, Andrew Gronewold, Joeseph Smith, Lacey Mason, Laura Read, Katelyn FitzGerald, Kevin Michael Sampson, Alan F. Hamlet, Frank Seglenieks, André Guy Tranquille Temgoua, Shervan Gharari, Saman Razavi, Amin Haghnegahdar, Mohamed Elshamy, Daniel G. Princz, Alain Pietroniro, Xiaojing Ni, Yongping Yuan, Mohammad Reza Najafi, Melika Rahimimovaghar, Martin Gauch, Jimmy Lin, and Raphael Tang.
Abstracts of the 2019 AGU Fall Meeting, December 2019, San Francisco, California.

Machine Learning for Streamflow Prediction: Current Status and Future Prospects
Martin Gauch, Raphael Tang, Juliane Mai, Bryan Tolson, Shervan Gharari, and Jimmy Lin.
Abstracts of the 2019 AGU Fall Meeting, December 2019, San Francisco, California.

The Cuizinart: Slice and Dice Your Environmental Datasets
Jimmy Lin, Martin Gauch, Yixin Wang, Alexander Weatherhead, Kaisong Huang, Bhaleka D. Persaud, and Juliane Mai.
Abstracts of the 2019 AGU Fall Meeting, December 2019, San Francisco, California.

The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction
(now published in Environmental Modelling & Software, Volume 135, 2021)
Martin Gauch, Juliane Mai, and Jimmy Lin.
arXiv:1911.07249, November 2019.

Data-Driven vs. Physically-Based Streamflow Prediction Models
Martin Gauch, Juliane Mai, Shervan Gharari, and Jimmy Lin.
Proceedings of the 9th International Workshop on Climate Informatics, October 2019, Paris, France.

2018

Towards Simulation-Data Science—A Case Study on Material Failures
Holger Trittenbach, Martin Gauch, Klemens Böhm, and Katrin Schulz.
2018 IEEE 5th International Conference on Data Science and Advanced Analytics, October 2018, Turin, Italy.

Work Experience

University of Waterloo

https://uwaterloo.ca

Research Associate

May 2020 – December 2020

I developed neural and non-neural machine learning algorithms to predict streamflow from spatially and temporally distributed input data. A large part of this work was in collaboration with environmental scientists. Besides, we worked with domain specialists from the environmental sciences on developing the Cuizinart, a cloud-based interactive platform that allows researchers to “slice and dice” large environmental datasets.

Data Systems Lab, University of Waterloo

https://cs.uwaterloo.ca

Graduate Research Assistant

January 2019 – April 2020

Besides my main research focus on machine learning during my graduate studies, I worked as a graduate research assistant. In collaboration with domain specialists from the environmental sciences, we developed the cloud-based Cuizinart platform that provides access to large environmental datasets. Also, we worked on solutions for metadata management in the Global Water Futures program.

SAP SuccessFactors

https://successfactors.com

Working student, software engineering

August 2014 – July 2018

In parallel to my studies in Karlsruhe, I worked part-time as a working student in the cloud HR software development at SAP SuccessFactors. As part of the Rules Engine scrum team, I worked on designing and developing a rules framework that enables users to customize automatic business processes in their HR system. My work involved Java programming as well as data analytics tasks, using Python, Jupyter notebooks, SQL, and Splunk.

SAP SE

https://sap.com

Working student, software engineering

May 2014 – July 2014

Between high school and university, I did a full-time internship in software development. I designed, developed, and tested on-premise HR software for the SAP ERP solution. My work mostly involved programming in ABAP. Also, I learned about SAP’s ERP software and its customizing.

Education

University of Waterloo

Computer Science
(Master's student)

September 2018 – April 2020

Supervised by Jimmy Lin, my research was on machine learning in hydrology.
I developed neural and non-neural machine learning algorithms to predict streamflow from spatially and temporally distributed input data. A large part of this work was in collaboration with environmental scientists.

Karlsruhe Institute of Technology

Computer Science
(Master's student)

September 2017 – August 2018

After two exchange terms at the University of Waterloo, I decided to continue pursuing my Master’s there. Switching to Waterloo allowed me to do a lot more research throughout my degree, as opposed to just taking courses and writing a thesis at the end.

Average grade: 1.1 (in the German grading scheme)

Karlsruhe Institute of Technology

Computer Science
(Bachelor's student)

October 2014 – September 2017

Bachelor’s thesis: Data-Driven Approaches to Predict Material Failure and Analyze Material Models
In my thesis, I developed data-driven models to predict failure events of an observed material and compared traditional machine learning approaches with recurrent neural network architectures.

Final grade: 1.3 (in the German grading scheme)