Martin Gauch

Master's student, University of Waterloo, Ontario, Canada


Hi, I’m Martin. I’m a Master’s student in computer science at the University of Waterloo. Before studying in Waterloo, I was an undergraduate and graduate computer science student at Karlsruhe Institute of Technology in Germany.

My research is at the intersection of machine learning and hydrology. We focus on developing data-driven techniques for streamflow prediction, both neural and non-neural models. We work in collaboration with hydrologists as part of the Canadian Global Water Futures program. My supervisor is Jimmy Lin.



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
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.


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

Data Systems Lab, University of Waterloo

Graduate Research Assistant

January 2019 – present

Besides my main research focus during my graduate studies, I work as a graduate research assistant. Together with domain specialists from the environmental sciences, we’re developing the Cuizinart, a cloud-based interactive platform that allows researchers to “slice and dice” large environmental datasets. Also, we work on solutions for metadata management in the Global Water Futures program.

SAP SuccessFactors

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.


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.


University of Waterloo

Computer Science
(Master's student)

September 2018 – present

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

Expected graduation: May 2020

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)