# Instructor & Credits

## Claire David

```{image} ../images/Claire_David_2023_circle.png
:alt: Claire David
:width: 30%
:align: center
```
   
   


I am an experimental particle physicist, and as such I study the Universe through its tiniest, most fundamental constituents: the elementary particles. 

I was initially trained as an engineer at the [Institut National des Sciences Appliquées (INSA)](https://www.insa-toulouse.fr/en/index.html) in Toulouse, France.

I switched to fundamental science with a PhD in Particle Physics at the [University of Victoria](https://www.uvic.ca/science/physics/index.php) in Canada. I was based at [TRIUMF](https://www.triumf.ca/) in Vancouver and was stationned from 2012 until mid-2013 at CERN between Switzerland and France.

After a postdoctoral fellowship at [DESY](https://www.desy.de/) in Hamburg, Germany, I started as Assistant Professor at [York University](https://www.yorku.ca/science/physics/), north of Toronto, Canada. There I co-founded the Canadian effort on the [Deep Underground Neutrino Experiment (DUNE)](https://www.dunescience.org/), which is a future detector to be hosted by the US laboratory [Fermilab](https://www.fnal.gov/) near Chicago, Illinois.

My main area of research is in collider physics. I have 10+ years' experience working for the [ATLAS Experiment](https://atlas.cern/), which is one of the detectors of the [Large Hadron Collider (LHC)](https://home.cern/science/accelerators/large-hadron-collider) at [CERN](https://home.web.cern.ch/), on both data analysis and detector upgrade.

During the academic year 2022-2023, I took a leave from York University to build a course on machine learning designed for the [African Institute for Mathematical Sciences](https://nexteinstein.org/) (AIMS) format. I taught this course at [AIMS Senegal](https://aims-senegal.org/), [AIMS South Africa](https://aims.ac.za/) and [AIMS Ghana](https://aims.edu.gh/). During my stay at AIMS South Africa, I was offered the opportunity to become a Resident Researcher in Machine Learning and the Academic Director of a new Master's course [AI for Science](https://ai.aims.ac.za/), founded by Google DeepMind. 

My interests range from advanced machine learning implementations for experimental particle physics to ethics in AI, education and sustainability on this fragile planet.


## Credits

I have received the precious help of colleagues and friends. A big thank you to Harrison Prosper for being a great mentor and sharing so many valuable resources.

Thanks to Mike Giles for catching all the typos and having this admirable attention to details.

I would also like to thank Alex Held, Benjamin Nachman, Bruno Barton-Singer, Clara Nellist, Dag Gillberg, Matthew Feickert, Sascha Caron and Will Leight.

For this course I got inspired by Coursera's Introduction to Machine Learning, by Andrew Ng. I found wonderful tutorials in Machine Learning Mastery, by Dr. Jason Brownlee. Aurélien Géron's book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ([Second Edition](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)) is fabulous. I am grateful to all 'StackOverflowers' helping me debug some parts of code to shape the tutorials and assignments into, I hope, an awesome learning experience for the students.

Extra credits: in 2025, this course has been renovated with the help of AIMS former students and tutors. I am grateful to Ahmed Turkman, Ayman Saeed, David Krame, Isaac Houngue, Jeffrey Katabo, Koomi Amoussouvi, Maria Stickel, Omer Ebead, Ophelia Dankyi, Timothy Hitge and Yassir Aladdin.