I am a Principal Member of Technical Staff at AMD Research. My research intrests are in designing scalable, fault tolerant and energy efficient Machine Learning and Data Mining (MLDM) algorithms. A few examples include Deep Learning algorithms (with Keras, TensorFlow and Caffe), Support Vector Machines (SVM), Frequent Pattern Mining (FP-Growth) and several others such as K-Nearest Neighbors (k-NN), k-means using MPI and PGAS models, such as Global Arrays. The MLDM research is integrated in Machine Learning Toolkit for Extreme Scale (MaTEx). I am also interested in applications of Machine Learning including fault, performance modeling and domain sciences.