Machine Learning Researcher At Mit Kavli Institute
CurrentI created a semi-supervised classifier for time-series photon measurements of supernova called light curves. The neural network consisted of a variational autoencoder and a random forest classifier. The autoencoder was first trained and then the random forest would classify light curves into 8 different classes based on the light curves encoded representation. I first processed all the data by using min-max normalization and added padding to make all light curves the same size. Because of this padding, I then added masking so that the autoencoder skips the time steps with not data. Lastly, I tuned all the hyperparameters of the neural network. I was able to reduce the light curves to 30 data points and get a 93% accuracy rate.