Approaching Data Science as a Physicist

As this is my first article here, I will just explain what I do and how it relates to my newly-found interest in Data Science.

To provide my background, I am a computational and statistical physicist who works at the University of Luxembourg. I am working on basic physics problems which has applications in futuristic organic solar cells (OSC), organic light emitting diode (OLED) etc. For example, new iPhone 8 by Apple will use curved OLED panels. These screens are organic in nature (unlike conventional inorganic films). They are biodegradable, more efficient, possess more functionalities. Quite Fascinating.

(Photo : News Update 24/Youtube) Apple iPhone 8 Curved OLED Screen

Now to study these materials, I use some stochastic methods (Monte Carlo, Brownian Dynamics) to simulate these systems. They provide me nice images, movies and lots of data. Now when there is data, there is statistics. Although making images of material formation is nice but writing mathematical formulation of the material formation is even more fun. To have a theoretical formulation of a process which provides quantitative insight is something almost every theoretical physicist yearn for.

A simulation image of film growth to synthesize these devices on nano scale. Rod shaped paricles are nanorods which, interestingly, features in our Liquid Crystal Displays (LCD) such as the screen you are looking at right now.

Interestingly, all these are methods are being applied in machine learning and data science too. Obtaining data, organizing it, understanding it, visualizing it, modeling it, making predictions using the model and then communicate the learnings from it. This is something I do in my daily life as a physicist. As there is a lot of talk around data science nowadays, I am curious about how it will pan out in next 10 years. As the nature of the field is quite interdisciplinary, it would receive contributions from Computer Science, Theoretical Physics, Statistics, Bioinformatics, Applied Mathematics and what not. I expect lots of academic research and industrial input in this field. I am staying put.