This blog is a brief summary of my work and results in GSOC 2022 under ML4SCI. During my time in GSOC I worked on the Fast Accurate Empirical Representation of Histograms project. The goal of this project is to use seq2seq models map histogram data to empirical functions. In particular we planned on adapting a transformer for this purpose. In order to train our model we will need a sufficiently diverse set of expressions and their corresponding histograms.

Unlike most other organizations participating at the Google summer of code, I feel that ML4SCI is unique in both its methods and objectives. While most organizations look for developers to build up their code repositories, resolve bugs and update new features, the primary objective of ML4SCI is to solve open-ended research questions in basic sciences by developing/using free and open source software by using Machine Learning. This year the kind of projects ranged from deep learning in gravitational physics, astronomy, quantum machine learning, machine learning to fluid turbulence, novel quantum materials etc. I have compiled my work into a single open-source repository. Titled, "Decoding Quantum States with NMR", we have a set of tutorials to read, preprocess and extract features from the NMR simulation data.