Samuel Kakraba, PhD
Dr. Samuel Kakraba’s research centers chiefly on the development and implementation of efficient computationally-driven pipelines aided by robust data science, statistical, mathematical and biostatistical predictive machine learning algorithms like neural networks, deep learning, support vector machines, k-nearest neighbors, random forests, Naïve Bayes, and others, in R statistical software, Python, SAS, and others, for estimation, prediction, and inferences into complex high-dimensional relationships in many fields like pharmaceutical sciences, public health, among others.