Hello everyone :)
About
With a PhD in Physics and a specialization in data science, my career has spanned developing likelihood models and machine learning algorithms for data analysis, as well as conducting postdoctoral research in Japan as part of an international collaboration.
My achievements include:
- engineering machine learning models for signal detection, with contributions recognized in top-tier journals,
- designing, implementing, and deploying an open-source data-visualization package in Python,
- leading a team of scientists to evaluate the performance of a particle detector.
I aim to leverage my analytical expertise and collaborative experience to address real-world data challenges.
Links
Talks
- C. Praz and T. Fillinger, Plothist: visualize and compare data in a scalable way and a beautiful style , Python in High Energy Physics, 2024.
- C. Praz (on behalf of the Belle II collaboration), Electroweak penguins and radiative B decays at Belle II , Electroweak session of the 57th Rencontres de Moriond, La Thuile, Italy, 2023.
- C. Praz, Search for B→Kνν decays with a machine learning method at the Belle II experiment , PhD defense, Hamburg, Germany, 2022.
- C. Praz (on behalf of the Belle II collaboration), Search for the rare decay B→Kνν in the early Belle II dataset , Phenomenology Symposium, Pittsburgh, US, 2021.
- C. Praz (on behalf of the Belle II collaboration), Search for B→K(star)νν at Belle II with machine learning techniques , Deutsche Physikalische Gesellschaft Spring Meeting, Dortmund, Germany, 2021.
- C. Praz (on behalf of the Belle II collaboration), Tracking performance and interaction point properties at Belle II , 29th International Workshop on Vertex Detectors, Tsukuba, Japan, 2020.
- C. Praz (on behalf of the Belle II collaboration), B lifetimes at Belle II , 40th International Conference on High Energy Physics, Prague, Czech Republic, 2020.
Certifications
- Stanford Course on Graph Search, Shortest Paths, and Data Structures , Feb 2025.
- Stanford Course on Divide and Conquer, Sorting and Searching, and Randomized Algorithms , Jan 2025.
- Google Introduction to Docker , Oct 2024.
- IBM Introduction to Cloud Computing , Jul 2024.
- IBM Introduction to DevOps , Jul 2024.
- DevOps, Cloud, and Agile Foundations , Jun 2024.
- Developing Applications in Python on AWS , Apr 2024.
- IBM Introduction to Agile Development and Scrum , Apr 2024.
- Machine Learning with Apache Spark , Apr 2024.
- SQL for Data Science , Mar 2024.
- Machine Learning in High-Energy Physics, Jul 2020.