Teaching

Teaching Summary

Over my career, I have been fortunate to teach a wide range of Mathematics and Computer Science courses to undergraduate and doctoral students. I was also able to participate in multiple science popularization initiatives, where I taught professionals and the general public outside of academia (see below). These experiences have equipped me with a range of teaching approaches that I continue to refine through ongoing training, such as coursework at the university didactic centers of both AMU and UniFR, and the Certificate in Graduate Teaching and Education Technology I recently passed. My teaching philosophy is articulated around two axes: A) each class is unique and requires a tailored approach, and B) I hold high expectations for both my students and myself as a professor.

More details on my vision of teaching can be found in my [teaching statement] (/files/teaching_statement.pdf).

Some recently taught classes

1. Formal Methods (Bachelor)
2. Introduction to Machine Learning (Co-Instructor, Bachelor)
3. Social Media Analytics (Co-Instructor, Master)
4. Quantitative Research Methods and Statistics (Co-instructor, Bachelor)

Current PhD supervision

1. Mondal, Manuel (2023 — Now) Lab: Exascale Infolab, Fribourg University Fribourg, Switzerland PhD TOPICS : NLP and LLMs

Science populatization initiatives

I’ve always been very fond of science popularization, which I think is a separate but very crucial form of teaching. I follow multiple science communicator across several platforms on the web, from blogs to video to newsletters, and I also read many journals that aim at presenting scientific facts and results to the general public on a very wide range of themes, far broader than my own research topics. I’ve also always enjoyed sharing this passion about learning and the incredible nature of the world, and have been venturing into science popularization myself. What is the value of knowledge if it is not shared ? Of course, it is not always possible to go into the fine details of the results, as it may require advanced knowledge and skills to be fully grasped, but I believe that at least two third of every idea, result or theory can be shared in an easy, approachable and even enticing way with the public. In line with this thinking, throughout the years, I was a guest-writer in a science journal (the DAP), I co-organized ted-talk like events, and I also frequently gave presentations or classes outside the university to the general public. For instance, I have recently given several workshops around Large Language Models and ChatGPT. These Artificial Intelligence models have taken the world by storm recently, and while the details of their inner working is complicated, I believe that the general idea behind them is quite accessible – and that it can be taught in an entertaining manner– and that this idea is enough to understand their advantages and limitations. Teaching the public about how they work is key as there is a lot of misinformation on the web about them, and they are bound to play a significant role in the society of Tomorrow. More details about the difference initiatives can be found in my cv.