ABSTRACT: In this lecture first we will discuss the current trends and challenges in Artificial Intelligence (AI) and Machine Learning (ML). In addition to the fundamentals of ML, we will demonstrate the importance of using Deep Learning (DL), Graph neural network (GNN) and explainable AI. While DL has been used very successfully…

ABSTRACT: Molecular dynamics (MD) simulations are widely used to study the mechanisms of biological processes at an atomistic resolution. Most physiological events, e.g., drug-target binding and protein folding, occur at timescales beyond milliseconds. But, we can simulate only up to a few microseconds at an affordable…

ABSTRACT:  Implicit neural representations (INRs) are a powerful family of continuous learned function approximators for signal data that are implemented using multilayer perceptron (MLP). INRs enable a non-linear signal representation of bases functions that are generated by scaling and shifting the activation function.…

ABSTRACT: In many real-world applications such as intelligent transportation, biosurveillance, climate science, and bioinformatics, statistical models and machine learning algorithms are applied to large-scale spatio-temporal data. Such data are typically high-dimensional and demonstrate complex nonlinear spatial and temporal…