To achieve this, we developed Skewencoder, an Autoencoder-based framework 45 that reduces data to a 1D latent space while incorporating higher-moment statistical information. By integrating an Autoencoder neural network (NN) with a loss function based on the skewness of projected samples, Skewencoder efficiently utilizes limited early-stage sampling data in a multitask learning framework, as defined in Equation (2).