By Yuan-Ru Lin
In software development, there are often situations where it's difficult to balance execution efficiency and development efficiency. When physicists analyze a large amount of detector data, they often develop prototypes in Python during the early stages and rewrite them in C++ for high-performance versions in the later stages.
I will take the research I conducted during my master's program in the Department of Physics at National Taiwan University as a case study, comparing the original implementation using Python with C++, and a version replicated in Julia, and comment on the Julia packages that correspond to each function in ROOT, the original framework.
I have been a graduate student at the Experimental High-Energy Physics Laboratory at National Taiwan University for nearly six years. In addition to applying machine learning techniques in the context of physics analysis, I also enjoy thinking about engineering problems that may arise in the context of experimental high-energy physics.
This September, I will be attending the Physics Department's doctoral program at the University of Washington in Seattle, USA.