Theoretical Nuclear Physics and Machine Learning
Application of Gaussian Processes to Extend the Range of Many-Body Physics Calculations of Infinite and Finite Systems
- Oral presentation to be given at the APS Global Physics Summit in March 2024 in Anaheim, CA.
- The conference website is located here.
Using Bayesian Machine Learning to Extend the Range of Ab Initio Many-Body Calculations of Infinite Matter Systems
- Invited seminar to be given at the University of Notre Dame in November 2024
- Joint Nuclear Physics & Condensed Matter Seminar
- The seminar website is located here.
Using Bayesian Machine Learning to Extend the Range of Ab Initio Many-Body Calculations of Infinite Matter Systems
- Oral presentation to be given at the APS Eastern Great Lakes Section Meeting in October 2024 in Marietta, OH
- The cofence website is located here.
Using Bayesian Machine Learning to Extend the Range of Ab Initio Many-Body Calculations of Infinite Matter Systems
- Poster presentation at the APS March Meeting in March 2024 in Minneapolis, MN
- The poster is located here and the conference website is located here.
Computing in Physics Education
Data Science for Physicists
- Organized and will present at a workshop entitled Data Science for Physicists which will take place at the 2025 APS Global Physics Summit in Anaheim, CA.
Data Science Tutorial: Data Exploration and Data Visualization
- Presented as a part of the tutorial “Data Science for Physicists I” which took place before the 2024 meeting of the APS March Meeting
- The code is located here and the link to all tutorial materials are located here.
Introduction to Data Science Libraries: Pandas, Seaborn, and Matplotlib
- Presented at the Data Science Education Community of Practice (DSECOP) Workshop in June 2023 in College Park, MD
- The slides are located [here]https://docs.google.com/presentation/d/1YP8tk_cL3gLg4_TeEKuI8Nfb87vGYDm4C2dSsIRnkbQ/edit?usp=sharing) and the conference website is located here.