Summer School

CIQM Undergraduate Summer School in Research and Education

Undergraduates at Bunker Hill Community College, Gallaudet University, Howard University, Mount Holyoke College, Olin College, Prince George’s Community College, and Wellesley College are welcome to apply for our CIQM Summer School program.

Students work with Center researchers and educators at Harvard, Howard, MIT, the Museum of Science, Boston, as well as College Network partners. Research projects are matched to our 4 research areas, and we also offer educational and outreach projects based on CIQM research.

Summer 2021 Program Details:
10 Weeks: June 7 - August 13, 2021
Students receive a $5,000 stipend

CIQM is also providing sponsorship for eligible students to attend BNL C2QA

To receive CIQM sponsorship for this BNL Workshop:
1) Complete the SEAS application (link above) and write in "C2QA" for research interest under other.
2) Be sure to list CIQM as Sponsor and list Dr Brower Thomas as contact on the BNL application.

Eligibility requirements are as follows:

Must be at least 18 years of age by the start date of the program.
Must be a U.S. Citizen or Lawful Permanent Resident (green card holder) of the U.S.
Applicant must be enrolled in an accredited institution of higher learning, or recently graduated (within one year of this application).
Will attend the full duration of the program.

Application: https://www.bnl.gov/education/programs/program.php?q=248
For questions or if you need assistance with this application, please contact:
Office of Educational Programs (OEP)
oep@bnl.gov

An Introduction to Linear Algebra Using Python
Dr. Steven L. Richardson
Howard University and Harvard University
Tuesdays 2:00 p.m. – 3:35 p.m. (Zoom Lecture)
Tuesdays 3:35 p.m. – 4:30 p.m. (Zoom Recitation Session)
Zoom Registration Required: https://harvard.zoom.us/meeting/register/tJwpc-GuqTIiGtV-S8y8iGQzQn-DoJQ8_Zbc
After registering, you will receive a confirmation email containing information about joining the meeting.

Linear algebra is an invaluable tool which serves as the gateway to a number of important subjects such as scientific computing, machine learning, quantum computing, and computer graphics. In this course we will cover the following topics: how to simultaneously solve systems of linear equations using determinants, Cramer’s rule, the method of cofactors and minors, Gauss-Jordan elimination, properties of matrices, the inverse of a matrix, using Gauss-Jordan elimination to find the inverse of a matrix, the rank of a matrix, review of vector analysis, expressing vectors as matrices, the linear dependence and independence of vectors, the basis of a vector, orthogonal and orthonormal vectors, the inner product of vectors, complex vectors and their inner products, linear transformations, special types of matrices (e.g. orthogonal, unitary, and Hermitian), the eigenvalue problem, matrix diagonalization, and the least squares approximation in linear algebra. NumPy (a numerical library of Python) is a very convenient package which is used to code both vectors and matrices and we will use some simple applications of it throughout this course to reinforce the important concepts and examples of linear algebra. We will assume throughout this course that the student is neither fluent in linear algebra nor Python.

This course is supported by the National Science Foundation Science and Technology Center for Integrated Quantum Materials, NSF Grant No. DMR-1231319

Pictures from our 2018 Summer Internship Program