Ph.D. Candidate / UT Austin Department of Physics
2013 - Current
I work on problems related to the AdS/CFT correspondence and black hole thermodynamics, especially where these intersect with quantum information theory and quantum complexity theory. So far, this research has resulted in four published papers and two as of yet unpublished pre-prints written with a total of 12 co-authors. See Research.
Teaching Assistant / UT Austin Department of Physics
2013 - Current
As a teaching assistant, I have been responsible for running laboratory courses for engineering physics, leading discussion and problem-solving sessions for electricity and magnetism and quantum mechanics, and grading homework and exams for a variety of undergraduate physics classes as well as graduate quantum mechanics.
- Quantum Physics I: Foundations (3 semesters)
- Forensic Science (2 semesters)
- Graduate Quantum Mechanics (1 semester)
- Quantum Physics III: Particles and Nuclei (2 semesters)
- Classical Electrodynamics I: (1 semester)
- Electricity and Magnetism (2 semesters)
- Lab for Engineering Physics I (4 semesters)
- Lab for Engineering Physics II (1 semester)
Graduate Research Assistant / Center for Particles and Fields, UT Austin
I worked with Prof. Peter Onyisi at the Meyrin, Switzerland campus of the European Center for Nuclear Research (CERN) on projects related to the ATLAS experiment. This work was partially in fulfillment of the experimental physics for my Ph.D. program. For my first project, I worked with the software development team for the ATLAS fast tracker. The fast tracker is a parallel processor for reconstructing particle tracks from raw detector data in real-time. My specific task was to write a python script to retrieve information about the state of the detector (e.g., which components are currently functional) and pass this information on to other software. My second project studied the measurement of the rate at which a Higgs boson is produced along with a top-quark anti-top-quark pair in the ATLAS detector. I ranked the importance of various variables (features) for distinguishing these events from backgrounds with similar detector signatures. I did this using a boosted decision tree from an in house machine learning package.
Undergraduate Researcher / Oklahoma State University
I worked in the lab of Prof. Flera Rizatdinova on particle physics data analysis. The work made use of both 'cut and count' methods, in which one applies filters which signal events are more likely to pass than background events, and more sophisticated 'multivariate' techniques. These were implemented in C/C++, making use of CERN's ROOT package.