About – Martin Sterchi

I am a lecturer at the School of Business at the University of Applied Sciences and Arts Northwestern Switzerland, or short FHNW. Additionally, I hold a part-time Postdoctoral position at the Chair of Psychological Methods, Evaluation and Statistics at the University of Zurich, led by Prof. Dr. Carolin Strobl.

I have a background in economics, having studied at the Universities of Fribourg and Zurich, and hold a PhD in Computer Science from the University of Zurich. My doctoral research, titled Computational Approaches to Epidemic Prevention on Contact Networks, focused on developing methods to mitigate the spread of a disease when at least partial knowledge of the underlying contact network is available.

At FHNW I teach the following modules (more info):

Machine Learning: This course is designed for business students, emphasizing the practical application of ML to real-world problems. We primarily use the tidymodels framework, building on students’ prior knowledge of R.
Applied Data Science: Currently, this week-long intensive course focuses on teaching students data visualization skills in R using ggplot2.
Business Analytics: In this core module, I introduce students to programming in R and guide them through the fundamentals of linear regression.

My research interests are as follows:

Dynamical processes on networks: My primary research interest lies in epidemic processes, which was the focus of my PhD and remains a central theme in my current work.
Monte Carlo simulations: For many problems, analytical solutions do not exist, and as a computer scientist, I am not reluctant to use simulations to find approximate solutions.
Graph Neural Networks: Network science has not escaped the Deep Learning revolution—and naturally, GNNs have caught my attention. Specifically, I am curious if GNNs can help address the challenge of source detection.
Explainability: Recently, I have begun exploring model explainability, primarily through visualizations—another passion of mine. I plan to further investigate this topic, particularly in the context of models on networks.

I enjoy implementing Machine Learning algorithms from scratch to deepen my understanding at the implementation level. Moreover, I can spend hours refining data visualizations—whether in base R, ggplot2, or matplotlib—striving for a balance of aesthetics, clarity, and engagement.

I live in Switzerland together with my partner and our two adorable—yet incredibly demanding—cats.