Scientific Support

Machine learning is widely used across many scientific fields. For example:

• Unsupervised methods are useful for data exploration and visualization (dimensionality reduction and clustering).
• Supervised methods are used for prediction tasks, where an output is inferred from input data (classification or regression).

At DCSR, we support researchers in several key areas of machine learning:

Training

We help you understand how specific machine learning methods work and how to apply them in your research. We also offers short introductory courses on machine learning; see ML courses.

Methodology

We assist you in selecting and applying appropriate machine learning methods for your research.

This may include:

• A pilot phase, where we collaboratively develop and test code on your laptop or UNIL clusters.
• A production phase, where we help scale and refine your workflow.

More specifically, we can:

• Identify existing tools suited to your analysis
• Help install and run them on your laptop or UNIL clusters
• Explain key parameters and settings
• Develop custom algorithms and code if no suitable tools exist

Infrastructure

We can help you to implement efficiently your machine learning pipeline on the UNIL clusters. More specifically, we can help you to install your codes on the UNIL clusters and to profile them in order to find the optimal setting (in terms of RAM, number of nodes and CPU/GPU).

Foster UNIL collaborations

We can help you to find appropriate experts at the UNIL to discuss with you about specific machine learning related problems. 

A few examples:

1. An experimental scientist would like to learn how to analyse his/her data on his/her laptop or on the UNIL clusters by using machine learning methods. We can help this researcher to find appropriate machine learning tools, to understand how they work and to use them on his/her laptop or on the UNIL clusters.

2. A data scientist would like to implement a machine learning pipeline (on his/her laptop or on the UNIL clusters) but he/she is not sure how to do it properly. We can help this researcher to find and apply appropriate machine learning methods.

3. A data scientist has implemented a machine learning pipeline (on his/her laptop or on the UNIL clusters) and would like to discuss about the methodology he/she used. We can help this researcher to check whether his/her approach is correct and we can also suggest alternative approaches.

4. A data scientist has implemented a machine learning pipeline on his/her laptop and would like to run it on the UNIL clusters (going from laptop to cluster). We can help this researcher to implement efficiently his/her pipeline on the UNIL clusters. This may involve the installation of softwares and the profiling of codes.