Computational Data Science in Physics: Explore realistic, contemporary examples of how computational methods apply to physics research. Course developed by Phil Harris, Ike Chuang, and Alex Shvonski.
- Free access to all content
- Self-paced, 6-week courses
- Course content available at any time, certificate available (with fee) when course is active
- GitHub repositories
In this first module, you will analyze LIGO data, detect a gravitational wave signal, and fit this signal within a physical model, among other objectives, using Jupyter notebooks.
In this module, topics include hypothesis testing, semi-parameteric methods, and deep learning. In the Final Project, you will analyze LHC data to measure properties of the W boson and Z boson.
Coming early 2024.
MIT Interdisciplinary PhD
IAIFI has partnered with the MIT Physics department and the MIT Statistics and Data Science Center to establish an interdisciplinary PhD program in Physics, Statistics and Data Science, which is designed to provide students with the highest level of competency in 21st century statistics, enabling doctoral students across MIT to better integrate computation and data analysis into their PhD thesis research.
- Open to PhD students in the MIT Physics department
- In addition to satisfying all of the requirements of the Physics PhD, students take one subject each in probability, statistics, computation and statistics, and data analysis, as well as the Doctoral Seminar in Statistics, and they write a dissertation in Physics utilizing statistical methods.
- For access to the selection form or for further information, please contact the IDSS Academic Office at email@example.com.
Many IAIFI faculty are developing courses at the intersection of physics and computer science. If you are an IAIFI faculty member with a course you’d like to share, or if you are a professor at another university working on similar courses and are interested in sharing resources, email IAIFI.
- 8.16/8.316: Data Science in Physics: Prof. Phil Harris
Aims to present modern computational methods by providing realistic, contemporary examples of how these computational methods apply to physics research. Designed around research modules in which each module provides experience with a specific scientific challenge. Modules include: analyzing LIGO open data; measuring electroweak boson to quark decays; understanding the cosmic microwave background; and lattice QCD/Ising model. Experience in Python helpful but not required. Lectures are viewed outside of class; in-class time is dedicated to problem- solving and discussion. Students taking graduate version will complete additional assignments. View course details
- 6.S966 Symmetry and its Application to Machine Learning and Scientific Compuing: Prof. Tess Smidt
Introduces the use of group representation theory to construct symmetry-preserving algorithms for machine learning. Emphases the connection between topics in math and physics and machine learning. Students will implement core mathematical concepts in code to build algorithms that can operate on graphs, geometry, scientific data, and other structured data to preserve the symmetries of these domains. Topics covered include: Euclidean and permutation groups, group representations: regular, reducible, and irreducible, tensor products, statistics and sampling of group representation vector spaces, and symmetry-breaking mechanisms. View course details
- PHYS 7321 Computational Physics: Prof. Jim Halverson
Details to come.