Physics // Numerical methods in physics have led to new insights into old problems, and have long since allowed the consideration of previously unaddressed phenomena. In its current state, computation can be viewed as complementary to the traditional routes of experiment and theory. For many physicists, "computer physics" provides an accessible way of doing physics without the need for substantial experimental resources. Literally one only needs a text editor, compiler, and some imagination to be able to start doing physics. Furthermore, computational algorithms provide a way of "discovering" physics in a manner similar to the traditional mode of pure research. Inevitably what follows in this process is the discovery that the same algorithms give the same results. In other words, that physics is phenomenologically unified.
Data Science // My training as a physicist also provides a natural foundation for the role of data scientist, where the roles of explorer, scientist, and analyst are effectively combined. (Experimental physicists are particularly well suited for this role as they are already trained in how to make sense of real world data, and are typically much stronger in statistics.) As demonstrated here, this translates into an individual that has the curiosity and passion for exploring new problems, data sets, and technologies. Implicit in this act of exploration is the tendency to take a clean, novel approach to an old problem. Moreover, the discipline and knowledge of my scientific background means that I am comfortable with testing my code and algorithms in a rigorous and objective manner. Lastly, my training as a scientist also aligns closely with that of an analyst, where answers are often the by-product of details.
Calculates the Sternheimer density effect parameters using the prescription given in The International Journal of Applied Radiation and Isotopes33(11), 1189 (1982). This utility is a companion to the Range-Energy Calculator.